Image Classifier Project.html

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<head><meta charset="utf-8" />

<title>Image Classifier Project</title>

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  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </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 */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.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 */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.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; }
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<h1 id="Developing-an-AI-application">Developing an AI application<a class="anchor-link" href="#Developing-an-AI-application">&#182;</a></h1><p>Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.</p>
<p>In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using <a href="http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html">this dataset</a> of 102 flower categories, you can see a few examples below.</p>
<p><img src='assets/Flowers.png' width=500px></p>
<p>The project is broken down into multiple steps:</p>
<ul>
<li>Load and preprocess the image dataset</li>
<li>Train the image classifier on your dataset</li>
<li>Use the trained classifier to predict image content</li>
</ul>
<p>We'll lead you through each part which you'll implement in Python.</p>
<p>When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.</p>
<p>First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Imports here</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">models</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">OrderedDict</span>

<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">time</span>

<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>

<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<h2 id="Alex-Kirko:-Transfer-Learning-considerations-and-a-sanity-check">Alex Kirko: Transfer Learning considerations and a sanity check<a class="anchor-link" href="#Alex-Kirko:-Transfer-Learning-considerations-and-a-sanity-check">&#182;</a></h2><p>I have some apprehensions about the suggested approach for some models. By the point where a classifier is used, some networks have already adapted to the data structure and its task. The classifier itself is a single layer for ResNet, SqueezeNet, DenseNet, and Inception, something like <code>(fc): Linear(in_features=2048, out_features=1000, bias=True)</code>. This means that these models have features that allow linear separation of ImageNet classes, and all that remains is to train a linear classifier on them. In this lab it's suggested to train a non-linear classifier, but this should work well well only if ImageNet itself has a set of classes similar to one we are trying to classify our images into (see <a href="http://cs231n.github.io/transfer-learning/">Stanford Class CS231n</a>). Otherwise the features won't have the required information for classification, and no classifier built on top of them will perform as well as updating the weights deeper in the network will.</p>
<p>The models supplied by <code>torchvision.models</code> are trained using the 2012 ImageNet challenge dataset (Large Scale Visual Recognition Challenge 2012). The list of classes (called synsets) is available at the <a href="http://image-net.org/challenges/LSVRC/2012/browse-synsets">ImageNet website</a>.</p>
<p>Flowers are symmetrical, colorful, complex objects. When I look at the ImageNet classes, I see that most of the challenge was in separating animals species from each other and classifying man-made objects. VGG and AlexNet have three layers in the classifier, so they'll likely have room to adapt to the differences in domain. Other models should be less flexible.</p>
<p>To test this hypothesis, I will provide support for VGG and Inception V3, and we'll see whether a classifier built on top of Inception will perform worse.</p>
<p>This notebook will be set up to run VGG to satisfy the requirements of the rubric, and the rest of the functionality will be available through initializing <code>model_name</code> differently two code cells down.</p>

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<h2 id="Alex-Kirko:-Set-up-project-settings-as-variables">Alex Kirko: Set up project settings as variables<a class="anchor-link" href="#Alex-Kirko:-Set-up-project-settings-as-variables">&#182;</a></h2><p>Because I will later be using this notebook as the starting point for a console application, it pays to set up everything that the user will be able to supply through command prompt as variables here, at the head of the notebook. Then I will be able to <code>import argparse</code> and change very little code for my train.py and predict.py</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">net_archs</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;AlexNet&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">,</span>
    <span class="s1">&#39;VGG-11 BN&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg11_bn</span><span class="p">,</span>
    <span class="s1">&#39;VGG-13 BN&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg13_bn</span><span class="p">,</span>
    <span class="s1">&#39;VGG-16 BN&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16_bn</span><span class="p">,</span>
    <span class="s1">&#39;VGG-19 BN&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg19_bn</span><span class="p">,</span>
    <span class="s1">&#39;Inception V3&#39;</span> <span class="p">:</span> <span class="n">models</span><span class="o">.</span><span class="n">inception_v3</span>
<span class="p">}</span>

<span class="n">net_classes</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;VGG-11 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;VGG&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-13 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;VGG&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-16 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;VGG&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-19 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;VGG&#39;</span><span class="p">,</span>
    <span class="s1">&#39;AlexNet&#39;</span> <span class="p">:</span> <span class="s1">&#39;AlexNet&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Inception V3&#39;</span> <span class="p">:</span> <span class="s1">&#39;Inception&#39;</span>
<span class="p">}</span>

<span class="n">net_prefixes</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;VGG-11 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;vgg11_bn_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-13 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;vgg13_bn_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-16 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;vgg16_bn_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;VGG-19 BN&#39;</span> <span class="p">:</span> <span class="s1">&#39;vgg19_bn_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;AlexNet&#39;</span> <span class="p">:</span> <span class="s1">&#39;alexnet_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;Inception V3&#39;</span> <span class="p">:</span> <span class="s1">&#39;inception_v3_&#39;</span>
<span class="p">}</span>

<span class="n">net_params</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;AlexNet&#39;</span> <span class="p">:</span> <span class="p">{</span>
        <span class="s1">&#39;learning_rate&#39;</span> <span class="p">:</span> <span class="mf">0.001</span><span class="p">,</span>
        <span class="s1">&#39;features_count&#39;</span> <span class="p">:</span> <span class="mi">9216</span><span class="p">,</span>
        <span class="s1">&#39;classifier_hidden&#39;</span> <span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
        <span class="s1">&#39;batch_size&#39;</span> <span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
        <span class="s1">&#39;classifier_name&#39;</span> <span class="p">:</span> <span class="s1">&#39;classifier&#39;</span><span class="p">,</span>
        <span class="s1">&#39;img_resize&#39;</span> <span class="p">:</span> <span class="mi">255</span><span class="p">,</span>
        <span class="s1">&#39;img_crop&#39;</span> <span class="p">:</span> <span class="mi">224</span>
    <span class="p">},</span>
    <span class="s1">&#39;Inception&#39;</span> <span class="p">:</span> <span class="p">{</span>
        <span class="s1">&#39;learning_rate&#39;</span> <span class="p">:</span> <span class="mf">0.001</span><span class="p">,</span>
        <span class="s1">&#39;features_count&#39;</span> <span class="p">:</span> <span class="mi">2048</span><span class="p">,</span>
        <span class="s1">&#39;classifier_hidden&#39;</span> <span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
        <span class="s1">&#39;batch_size&#39;</span> <span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
        <span class="s1">&#39;classifier_name&#39;</span> <span class="p">:</span> <span class="s1">&#39;fc&#39;</span><span class="p">,</span>
        <span class="s1">&#39;img_resize&#39;</span> <span class="p">:</span> <span class="mi">400</span><span class="p">,</span>
        <span class="s1">&#39;img_crop&#39;</span> <span class="p">:</span> <span class="mi">299</span>
    <span class="p">},</span>
    <span class="s1">&#39;VGG&#39;</span> <span class="p">:</span> <span class="p">{</span>
        <span class="s1">&#39;learning_rate&#39;</span> <span class="p">:</span> <span class="mf">0.001</span><span class="p">,</span>
        <span class="s1">&#39;features_count&#39;</span> <span class="p">:</span> <span class="mi">25088</span><span class="p">,</span>
        <span class="s1">&#39;classifier_hidden&#39;</span> <span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
        <span class="s1">&#39;batch_size&#39;</span> <span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
        <span class="s1">&#39;classifier_name&#39;</span> <span class="p">:</span> <span class="s1">&#39;classifier&#39;</span><span class="p">,</span>
        <span class="s1">&#39;img_resize&#39;</span> <span class="p">:</span> <span class="mi">255</span><span class="p">,</span>
        <span class="s1">&#39;img_crop&#39;</span> <span class="p">:</span> <span class="mi">224</span>
    <span class="p">}</span>
<span class="p">}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Choose device</span>
<span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span>

<span class="c1">#Choose the model - this determines everything</span>
<span class="c1">#model_name = &#39;Inception V3&#39;</span>
<span class="n">model_name</span> <span class="o">=</span> <span class="s1">&#39;VGG-13 BN&#39;</span>
<span class="c1">#model_name = &#39;AlexNet&#39;</span>

<span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
<span class="n">model_load</span> <span class="o">=</span> <span class="n">net_archs</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
<span class="n">net_prefix</span> <span class="o">=</span> <span class="n">net_prefixes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>

<span class="n">features_count</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;features_count&#39;</span><span class="p">]</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">]</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;batch_size&#39;</span><span class="p">]</span>
<span class="n">classifier_name</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;classifier_name&#39;</span><span class="p">]</span>
<span class="n">img_resize</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_resize&#39;</span><span class="p">]</span>
<span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>

<span class="n">user_hidden</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">classifier_hidden</span> <span class="o">=</span> <span class="n">user_hidden</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_classifier</span><span class="p">(</span><span class="n">features_count</span><span class="p">,</span> <span class="n">classifier_hidden</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Get a new classifier for the neural network</span>
<span class="sd">    </span>
<span class="sd">    In:</span>
<span class="sd">    features_count : number of inputs to the classifier</span>
<span class="sd">    classifier_hidden : number of hidden layers in</span>
<span class="sd">                        the classifier</span>
<span class="sd">    dropout_p : dropout probability for the hidden layers</span>
<span class="sd">    </span>
<span class="sd">    Out:</span>
<span class="sd">    classifier : new classifier (nn.Sequential)</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    
    <span class="c1"># Return a classifier based on number of features,</span>
    <span class="c1"># number of hidden layers, and dropout probability</span>
    <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
    
    <span class="n">classifier_spec</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([])</span>
    
    <span class="n">in_count</span> <span class="o">=</span> <span class="n">features_count</span>
    
    <span class="c1">#Number of final outputs</span>
    <span class="n">out_final</span> <span class="o">=</span> <span class="mi">102</span>
    
    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
    
    <span class="k">if</span> <span class="n">classifier_hidden</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="c1">#Transform the node count range to log2 scale</span>
        <span class="n">feat_log</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">features_count</span><span class="p">))</span>
        <span class="n">out_fin_log</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">out_final</span><span class="p">))</span>
        <span class="c1"># Linearly interpolate hidden layer counts in log2</span>
        <span class="c1"># calculate distance</span>
        <span class="n">dist</span> <span class="o">=</span> <span class="p">((</span><span class="n">feat_log</span> <span class="o">-</span> <span class="n">out_fin_log</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">classifier_hidden</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
        <span class="c1"># Build hidden layer output counts in log2 and </span>
        <span class="c1"># return tonormal scale by taking exp</span>
        <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp2</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">feat_log</span> <span class="o">-</span> <span class="n">i</span> <span class="o">*</span> <span class="n">dist</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">classifier_hidden</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
        
        <span class="c1"># Check for duplicates</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">hidden_outputs</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">hidden_outputs</span><span class="p">)):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">classifier_hidden</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; is too many hidden layers. Try 2 or 3.&#39;</span><span class="p">)</span>
        
        
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">classifier_hidden</span><span class="p">):</span>
            <span class="n">indeces</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;fc&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">&#39;relu&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="s1">&#39;dropout&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
            <span class="n">classifier_spec</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">indeces</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_count</span><span class="p">,</span> <span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">])})</span>
            <span class="n">classifier_spec</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">indeces</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()})</span>
            <span class="n">classifier_spec</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">indeces</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout_p</span><span class="p">)})</span>
            <span class="n">in_count</span> <span class="o">=</span> <span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    
    <span class="c1"># Getting final class labels</span>
    <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="n">classifier_spec</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;fc&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_count</span><span class="p">,</span> <span class="mi">102</span><span class="p">)})</span>
    <span class="n">classifier_spec</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;output&#39;</span> <span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)})</span>
            
    <span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">classifier_spec</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">classifier</span>
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<h2 id="Load-the-data">Load the data<a class="anchor-link" href="#Load-the-data">&#182;</a></h2><p>Here you'll use <code>torchvision</code> to load the data (<a href="http://pytorch.org/docs/0.3.0/torchvision/index.html">documentation</a>). The data should be included alongside this notebook, otherwise you can <a href="https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz">download it here</a>. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.</p>
<p>The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.</p>
<p>The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#For local training, use the data on an M.2 SSD</span>

<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;C:/udacity/data/flowers&#39;</span>
<span class="c1">#data_dir = &#39;data/flowers&#39;</span>
<span class="n">train_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/train&#39;</span>
<span class="n">valid_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/valid&#39;</span>
<span class="n">test_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Define your transforms for the training, validation, and testing sets</span>
<span class="c1"># Alex Kirko: using the transforms suggested in Part 8 of the PyTorch lesson</span>
<span class="c1"># since flowers are symmetrical, it won&#39;t hurt to rotate them more</span>
<span class="c1"># since we&#39;ll be using an ImageNet pre-trained network, it&#39;s important to</span>
<span class="c1"># normalize the color channels the same way they were normalized when these</span>
<span class="c1"># models were trained</span>
<span class="n">train_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">45</span><span class="p">),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="n">img_crop</span><span class="p">),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                       <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                            <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>

<span class="c1">#Alex Kirko: for validation and test there is no need to introduce randomness to the data</span>
<span class="n">valid_transforms</span> <span class="o">=</span> <span class="n">test_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">img_resize</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="n">img_crop</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                           <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>


<span class="c1"># TODO: Load the datasets with ImageFolder</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/train&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">train_transforms</span><span class="p">)</span>
<span class="n">valid_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/valid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">valid_transforms</span><span class="p">)</span>
<span class="n">test_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">test_transforms</span><span class="p">)</span>

<span class="c1"># TODO: Using the image datasets and the trainforms, define the dataloaders</span>
<span class="c1">#Alex Kirko: only the training set benefits from shuffling at the end of each epoch</span>
<span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">valid_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">valid_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
<span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
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<h3 id="Label-mapping">Label mapping<a class="anchor-link" href="#Label-mapping">&#182;</a></h3><p>You'll also need to load in a mapping from category label to category name. You can find this in the file <code>cat_to_name.json</code>. It's a JSON object which you can read in with the <a href="https://docs.python.org/2/library/json.html"><code>json</code> module</a>. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;cat_to_name.json&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">cat_to_name</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="c1">#cat_to_name</span>
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<h1 id="Building-and-training-the-classifier">Building and training the classifier<a class="anchor-link" href="#Building-and-training-the-classifier">&#182;</a></h1><p>Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from <code>torchvision.models</code> to get the image features. Build and train a new feed-forward classifier using those features.</p>
<p>We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.</p>
<p>Refer to <a href="https://review.udacity.com/#!/rubrics/1663/view">the rubric</a> for guidance on successfully completing this section. Things you'll need to do:</p>
<ul>
<li>Load a <a href="http://pytorch.org/docs/master/torchvision/models.html">pre-trained network</a> (If you need a starting point, the VGG networks work great and are straightforward to use)</li>
<li>Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout</li>
<li>Train the classifier layers using backpropagation using the pre-trained network to get the features</li>
<li>Track the loss and accuracy on the validation set to determine the best hyperparameters</li>
</ul>
<p>We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!</p>
<p>When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Build and train your network</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model_load</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span>
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<pre>VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (21): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (23): ReLU(inplace)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (26): ReLU(inplace)
    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (30): ReLU(inplace)
    (31): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (33): ReLU(inplace)
    (34): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove gradient tracking from the network parameters</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
    <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>

<span class="c1"># Create a new classifier</span>
<span class="c1"># Use dropout probability that the VGG models use - </span>
<span class="c1"># authors likely knew what they were doing</span>

<span class="n">classifier</span> <span class="o">=</span> <span class="n">get_classifier</span><span class="p">(</span><span class="n">features_count</span><span class="p">,</span> <span class="n">classifier_hidden</span><span class="o">=</span><span class="n">classifier_hidden</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>

<span class="c1">#Attach the new classifier (it has random weights now)</span>
<span class="k">if</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;classifier&#39;</span><span class="p">:</span>
    <span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="k">elif</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;fc&#39;</span><span class="p">:</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">classifier</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span>
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<pre>VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (9): ReLU(inplace)
    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (12): ReLU(inplace)
    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (16): ReLU(inplace)
    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (19): ReLU(inplace)
    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (21): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (23): ReLU(inplace)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (26): ReLU(inplace)
    (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (30): ReLU(inplace)
    (31): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (33): ReLU(inplace)
    (34): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (fc1): Linear(in_features=25088, out_features=2048, bias=True)
    (relu1): ReLU()
    (dropout1): Dropout(p=0.5)
    (fc2): Linear(in_features=2048, out_features=512, bias=True)
    (relu2): ReLU()
    (dropout2): Dropout(p=0.5)
    (fc3): Linear(in_features=512, out_features=102, bias=True)
    (output): LogSoftmax()
  )
)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Check dataset sizes</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">valid_loader</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="p">))</span>
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<pre>103
13
13
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Move the model to GPU before constructing the optimizer</span>
<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>

<span class="c1"># Set up negative likelihood loss and the adam optimizer</span>
<span class="c1"># (momentum is useful)</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
<span class="k">if</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;classifier&#39;</span><span class="p">:</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;fc&#39;</span><span class="p">:</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">)</span>

<span class="c1"># This will take some time, but we need to see how far we can reduce the error</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">15</span>


<span class="n">train_losses</span><span class="p">,</span> <span class="n">test_losses</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">train_r_acc</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span>
        
        <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
                
        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
        
        <span class="n">log_ps</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        
        <span class="c1">#Inception calculates two losses: auxillary and main while training</span>
        <span class="k">if</span> <span class="n">model_name</span> <span class="o">!=</span> <span class="s1">&#39;Inception V3&#39;</span><span class="p">:</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">log_ps</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">log_ps</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="p">)</span> <span class="o">+</span> <span class="n">criterion</span><span class="p">(</span><span class="n">log_ps</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">labels</span><span class="p">)</span>
        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
        
        <span class="c1">#print(&#39;Current time: {:.1f}&#39;.format(time.time() - start))</span>
        
        <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="c1">#print(&#39;Batch training time: {:.1f}&#39;.format(time.time() - start))</span>
        
        <span class="c1">#For debugging. Testing accuracy</span>
        <span class="k">if</span> <span class="n">model_name</span> <span class="o">!=</span> <span class="s1">&#39;Inception V3&#39;</span><span class="p">:</span>
            <span class="n">top_log_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">log_ps</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">top_log_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">log_ps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
            <span class="c1">#print(predictions)</span>
        <span class="n">equals</span> <span class="o">=</span> <span class="n">predictions</span> <span class="o">==</span> <span class="n">labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">predictions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
        <span class="n">train_r_acc</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">equals</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        
    <span class="k">else</span><span class="p">:</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="c1">## TODO: Implement the validation pass and print out the validation accuracy</span>
            
            <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
            
            <span class="n">running_acc</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="k">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">valid_loader</span><span class="p">:</span>
                
                <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
                
                <span class="n">log_ps</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
                <span class="n">top_log_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">log_ps</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
                <span class="n">equals</span> <span class="o">=</span> <span class="n">predictions</span> <span class="o">==</span> <span class="n">labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">predictions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
                <span class="n">running_acc</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">equals</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
                <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">log_ps</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
            <span class="n">train_loss</span> <span class="o">=</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
            <span class="n">test_loss</span> <span class="o">=</span> <span class="n">test_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_loader</span><span class="p">)</span>
            <span class="n">accuracy</span> <span class="o">=</span> <span class="n">running_acc</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_loader</span><span class="p">)</span>
            <span class="n">t_accuracy</span> <span class="o">=</span> <span class="n">train_r_acc</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
            
            <span class="n">train_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span>
            <span class="n">test_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)</span>
            
            <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
            
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch: </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training loss: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">train_loss</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation loss: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation Accuracy: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training accuracy: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t_accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch training time: </span><span class="si">{:.1f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">))</span>
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<pre>Epoch: 1/15
Training loss: 3.86
Validation loss: 2.04
Validation Accuracy: 48.65
Training accuracy: 15.44
Epoch training time: 124.3
Epoch: 2/15
Training loss: 2.37
Validation loss: 1.08
Validation Accuracy: 68.87
Training accuracy: 39.72
Epoch training time: 110.6
Epoch: 3/15
Training loss: 1.78
Validation loss: 0.69
Validation Accuracy: 82.58
Training accuracy: 51.61
Epoch training time: 108.4
Epoch: 4/15
Training loss: 1.55
Validation loss: 0.60
Validation Accuracy: 83.70
Training accuracy: 57.49
Epoch training time: 123.6
Epoch: 5/15
Training loss: 1.44
Validation loss: 0.51
Validation Accuracy: 87.06
Training accuracy: 61.37
Epoch training time: 105.1
Epoch: 6/15
Training loss: 1.28
Validation loss: 0.43
Validation Accuracy: 88.67
Training accuracy: 65.17
Epoch training time: 104.8
Epoch: 7/15
Training loss: 1.24
Validation loss: 0.40
Validation Accuracy: 89.24
Training accuracy: 66.02
Epoch training time: 104.7
Epoch: 8/15
Training loss: 1.19
Validation loss: 0.38
Validation Accuracy: 90.84
Training accuracy: 68.01
Epoch training time: 103.6
Epoch: 9/15
Training loss: 1.12
Validation loss: 0.40
Validation Accuracy: 90.30
Training accuracy: 70.04
Epoch training time: 104.6
Epoch: 10/15
Training loss: 1.15
Validation loss: 0.36
Validation Accuracy: 90.96
Training accuracy: 68.80
Epoch training time: 104.9
Epoch: 11/15
Training loss: 1.08
Validation loss: 0.37
Validation Accuracy: 91.08
Training accuracy: 70.79
Epoch training time: 104.0
Epoch: 12/15
Training loss: 1.06
Validation loss: 0.35
Validation Accuracy: 91.26
Training accuracy: 71.67
Epoch training time: 104.7
Epoch: 13/15
Training loss: 1.07
Validation loss: 0.35
Validation Accuracy: 90.03
Training accuracy: 71.57
Epoch training time: 104.3
Epoch: 14/15
Training loss: 0.97
Validation loss: 0.34
Validation Accuracy: 91.92
Training accuracy: 74.17
Epoch training time: 105.8
Epoch: 15/15
Training loss: 1.00
Validation loss: 0.34
Validation Accuracy: 92.26
Training accuracy: 73.22
Epoch training time: 107.1
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<h2 id="Testing-your-network">Testing your network<a class="anchor-link" href="#Testing-your-network">&#182;</a></h2><p>It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Do validation on the test set</span>
<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>

<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="n">running_acc</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>

        <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>

        <span class="n">log_ps</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        <span class="n">top_log_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">log_ps</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">equals</span> <span class="o">=</span> <span class="n">predictions</span> <span class="o">==</span> <span class="n">labels</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">predictions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
        <span class="n">running_acc</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">equals</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">))</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">log_ps</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="n">test_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>
    <span class="n">accuracy</span> <span class="o">=</span> <span class="n">running_acc</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>

    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch: </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test loss: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy: </span><span class="si">{0:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>Epoch: 15/15
Test loss: 0.33
Accuracy: 91.10
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<h3 id="Results-for-Inception-V3-(vanilla-model-has-a-linear-classifier)">Results for Inception V3 (vanilla model has a linear classifier)<a class="anchor-link" href="#Results-for-Inception-V3-(vanilla-model-has-a-linear-classifier)">&#182;</a></h3><p>Below is testing output for Inception V3, proving that using models with linear classifiers in this manner isn't very effective, even if we replace the linear classifier with a non-linear one (VGG-13 with a replaced classifier easily reaches 90% testing set accuracy).</p>
<p>This supports the intuition I wrote about at the head of this project: because ImageNet doesn't have many flower-like classes, we should be retraining more than one layer.</p>
<p><code>Epoch: 15/15
Test loss: 1.10
Accuracy: 73.28</code></p>

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<h2 id="Save-the-checkpoint">Save the checkpoint<a class="anchor-link" href="#Save-the-checkpoint">&#182;</a></h2><p>Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: <code>image_datasets['train'].class_to_idx</code>. You can attach this to the model as an attribute which makes inference easier later on.</p>
<p><code>model.class_to_idx = image_datasets['train'].class_to_idx</code></p>
<p>Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, <code>optimizer.state_dict</code>. You'll likely want to use this trained model in the next part of the project, so best to save it now.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Save the checkpoint </span>

<span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">class_to_idx</span>
<span class="n">optim_state_dict</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>

<span class="c1">#print(&quot;Our model: \n\n&quot;, model, &#39;\n&#39;)</span>
<span class="c1">#print(&quot;The state dict keys: \n\n&quot;, model.state_dict().keys())</span>

<span class="c1">#Based on https://pytorch.org/tutorials/beginner/saving_loading_models.html</span>
<span class="n">checkpoint</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;epoch&#39;</span><span class="p">:</span> <span class="n">e</span><span class="p">,</span>
    <span class="s1">&#39;optim_state_dict&#39;</span><span class="p">:</span> <span class="n">optim_state_dict</span><span class="p">,</span>
    <span class="s1">&#39;model_name&#39;</span><span class="p">:</span> <span class="n">model_name</span><span class="p">,</span>
    <span class="s1">&#39;optim_class&#39;</span> <span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
    <span class="s1">&#39;loss_class&#39;</span> <span class="p">:</span> <span class="s1">&#39;NLLLoss&#39;</span><span class="p">,</span>
    <span class="s1">&#39;model_device&#39;</span> <span class="p">:</span> <span class="n">device</span><span class="p">,</span>
    <span class="s1">&#39;model_state_dict&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
    <span class="s1">&#39;class_to_idx&#39;</span> <span class="p">:</span> <span class="n">class_to_idx</span><span class="p">,</span>
    <span class="s1">&#39;learning_rate&#39;</span> <span class="p">:</span> <span class="n">learning_rate</span><span class="p">,</span>
    <span class="s1">&#39;classifier_hidden&#39;</span> <span class="p">:</span> <span class="n">classifier_hidden</span>
<span class="p">}</span>

<span class="n">save_filename</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">save_filename</span><span class="p">:</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">save_filename</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">net_prefix</span> <span class="o">+</span> <span class="s1">&#39;checkpoint.pth&#39;</span><span class="p">)</span>
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<h2 id="Loading-the-checkpoint">Loading the checkpoint<a class="anchor-link" href="#Loading-the-checkpoint">&#182;</a></h2><p>At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Write a function that loads a checkpoint and rebuilds the model</span>

<span class="k">def</span> <span class="nf">model_load</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Load a model from checkpoint</span>
<span class="sd">    </span>
<span class="sd">    In:</span>
<span class="sd">    path : path to the checkpoint file</span>
<span class="sd">    </span>
<span class="sd">    Out:</span>
<span class="sd">    model : model object</span>
<span class="sd">    criterion : loss function</span>
<span class="sd">    optimizer : initialized optimizer</span>
<span class="sd">    epoch : epoch at which the model finished training</span>
<span class="sd">    device : device from which the model was saved</span>
<span class="sd">    model_name : model name </span>
<span class="sd">                 (used to get settings for the model)</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
    
    <span class="n">model_name</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;model_name&#39;</span><span class="p">]</span>
    <span class="n">loss_class</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;loss_class&#39;</span><span class="p">]</span>
    <span class="n">optim_class</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;optim_class&#39;</span><span class="p">]</span>
    <span class="n">model_device</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;model_device&#39;</span><span class="p">]</span>
    <span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;class_to_idx&#39;</span><span class="p">]</span>
    <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">]</span>
    <span class="n">classifier_hidden</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;classifier_hidden&#39;</span><span class="p">]</span>
    
    <span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    <span class="n">model_load</span> <span class="o">=</span> <span class="n">net_archs</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    <span class="n">net_prefix</span> <span class="o">=</span> <span class="n">net_prefixes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    
    <span class="c1">#Initialize parameters needed to build the network</span>
    <span class="n">features_count</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;features_count&#39;</span><span class="p">]</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;batch_size&#39;</span><span class="p">]</span>
    <span class="n">classifier_name</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;classifier_name&#39;</span><span class="p">]</span>
    <span class="n">img_resize</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_resize&#39;</span><span class="p">]</span>
    <span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>
        
    <span class="n">model</span> <span class="o">=</span> <span class="n">model_load</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    
    <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
        <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>

    <span class="c1"># Create a new classifier</span>
    <span class="n">classifier</span> <span class="o">=</span> <span class="n">get_classifier</span><span class="p">(</span><span class="n">features_count</span><span class="p">,</span> <span class="n">classifier_hidden</span><span class="o">=</span><span class="n">classifier_hidden</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
    <span class="c1">#Attach the new classifier (it has random weights now)</span>
    <span class="k">if</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;classifier&#39;</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
    <span class="k">elif</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;fc&#39;</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">classifier</span>
        
    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">model_device</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;model_state_dict&#39;</span><span class="p">])</span>
    <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">class_to_idx</span>
    <span class="c1"># What we need to get human-readable labels</span>
    <span class="c1"># is a mapping from indices to class labels</span>
    <span class="n">model</span><span class="o">.</span><span class="n">idx_to_class</span> <span class="o">=</span> <span class="p">{</span><span class="n">idx</span> <span class="p">:</span> <span class="n">c</span> <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
    
    
    <span class="k">if</span> <span class="n">loss_class</span> <span class="o">==</span> <span class="s1">&#39;NLLLoss&#39;</span><span class="p">:</span>
        <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>        
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">loss_class</span> <span class="o">+</span> <span class="s1">&#39; loss function is not supported. Your model should use NLLLoss.&#39;</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">optim_class</span> <span class="o">==</span> <span class="s1">&#39;Adam&#39;</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;classifier&#39;</span><span class="p">:</span>
            <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">classifier_name</span> <span class="o">==</span> <span class="s1">&#39;fc&#39;</span><span class="p">:</span>
            <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">optim_class</span> <span class="o">+</span> <span class="s1">&#39; optimizer is not supported. Your model should use Adam.&#39;</span><span class="p">)</span>
    
    <span class="c1">#optimizer.load_state_dict(checkpoint[&#39;optim_state_dict&#39;])</span>
    <span class="n">epoch</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">]</span>
    
    <span class="n">device</span> <span class="o">=</span> <span class="n">model_device</span>
    
    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">model_name</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">net_prefix</span> <span class="o">=</span> <span class="s1">&#39;vgg13_bn_&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">model_name</span> <span class="o">=</span> <span class="n">model_load</span><span class="p">(</span><span class="n">net_prefix</span> <span class="o">+</span> <span class="s1">&#39;checkpoint.pth&#39;</span><span class="p">)</span>
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<h1 id="Inference-for-classification">Inference for classification<a class="anchor-link" href="#Inference-for-classification">&#182;</a></h1><p>Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called <code>predict</code> that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
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<p>First you'll need to handle processing the input image such that it can be used in your network.</p>
<h2 id="Image-Preprocessing">Image Preprocessing<a class="anchor-link" href="#Image-Preprocessing">&#182;</a></h2><p>You'll want to use <code>PIL</code> to load the image (<a href="https://pillow.readthedocs.io/en/latest/reference/Image.html">documentation</a>). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.</p>
<p>First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>thumbnail</code></a> or <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>resize</code></a> methods. Then you'll need to crop out the center 224x224 portion of the image.</p>
<p>Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so <code>np_image = np.array(pil_image)</code>.</p>
<p>As before, the network expects the images to be normalized in a specific way. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>. You'll want to subtract the means from each color channel, then divide by the standard deviation.</p>
<p>And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html"><code>ndarray.transpose</code></a>. The color channel needs to be first and retain the order of the other two dimensions.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Note: we could have just used test_loader</span>

<span class="c1"># TODO: Process a PIL image for use in a PyTorch model</span>
<span class="k">def</span> <span class="nf">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Scales, crops, and normalizes a PIL image for a PyTorch model,</span>
<span class="sd">        returns a torch.Tensor</span>
<span class="sd">        </span>
<span class="sd">        In:</span>
<span class="sd">        img : a PIL image created with Image.open</span>
<span class="sd">        model_name : model_name (used to get settings)</span>
<span class="sd">        net_classes : mapping of model names to classes</span>
<span class="sd">        net_params : mapping of model classes to</span>
<span class="sd">                     their default parameters</span>
<span class="sd">        </span>
<span class="sd">        Out:</span>
<span class="sd">        img : tensor ready for the model</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    <span class="n">img_resize</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_resize&#39;</span><span class="p">]</span>
    <span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>
    
    <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span>
    <span class="k">if</span> <span class="n">width</span> <span class="o">&lt;</span> <span class="n">height</span><span class="p">:</span>
        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="n">img_resize</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">height</span> <span class="o">*</span> <span class="n">img_resize</span> <span class="o">/</span> <span class="n">width</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">width</span> <span class="o">*</span> <span class="n">img_resize</span> <span class="o">/</span> <span class="n">height</span><span class="p">),</span> <span class="n">img_resize</span><span class="p">)</span>
    
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
                
    <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">size</span>
    <span class="c1"># Crop based on this stackoverflow article</span>
    <span class="c1"># https://stackoverflow.com/questions/16646183/crop-an-image-in-the-centre-using-pil</span>
    
    <span class="n">left</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span> <span class="o">-</span> <span class="n">img_crop</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">top</span> <span class="o">=</span> <span class="p">(</span><span class="n">height</span> <span class="o">-</span> <span class="n">img_crop</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">right</span> <span class="o">=</span> <span class="p">(</span><span class="n">width</span> <span class="o">+</span> <span class="n">img_crop</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">bottom</span> <span class="o">=</span> <span class="p">(</span><span class="n">height</span> <span class="o">+</span> <span class="n">img_crop</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>

    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">crop</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">bottom</span><span class="p">))</span>
    
    <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span> <span class="o">/</span> <span class="mi">255</span>
    
    <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="n">img</span> <span class="o">=</span> <span class="p">(</span><span class="n">img</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
    
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    
    <span class="c1"># Sometimes the tensor gets cast to DoubleTensor.</span>
    <span class="c1"># This can break a model.</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">img</span>
    
    
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<p>To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your <code>process_image</code> function works, running the output through this function should return the original image (except for the cropped out portions).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Imshow for Tensor</span>
<span class="sd">    </span>
<span class="sd">    In:</span>
<span class="sd">    image : image tensor</span>
<span class="sd">    ax : existing axis on which to draw the image</span>
<span class="sd">    title : image title (reserved argument)</span>
<span class="sd">    </span>
<span class="sd">    Out:</span>
<span class="sd">    ax : axis with image drawn on it</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
    
    <span class="c1"># PyTorch tensors assume the color channel is the first dimension</span>
    <span class="c1"># but matplotlib assumes is the third dimension</span>
    <span class="c1"># Alex Kirko: The image is already a np_array</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
    
    <span class="c1"># Undo preprocessing</span>
    <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">image</span> <span class="o">+</span> <span class="n">mean</span>
    
    <span class="c1"># Image needs to be clipped between 0 and 1 or it looks like noise when displayed</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">ax</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;data/flowers/test/16/image_06657.jpg&#39;</span><span class="p">)</span>

<span class="n">img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">)</span>

<span class="n">ax</span> <span class="o">=</span> <span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
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<h2 id="Class-Prediction">Class Prediction<a class="anchor-link" href="#Class-Prediction">&#182;</a></h2><p>Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.</p>
<p>To get the top $K$ largest values in a tensor use <a href="http://pytorch.org/docs/master/torch.html#torch.topk"><code>x.topk(k)</code></a>. This method returns both the highest <code>k</code> probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using <code>class_to_idx</code> which hopefully you added to the model or from an <code>ImageFolder</code> you used to load the data (<a href="#Save-the-checkpoint">see here</a>). Make sure to invert the dictionary so you get a mapping from index to class as well.</p>
<p>Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Make sure class_to_idx is recoverable from checkpoint</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> 
            <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Predict the class (or classes) of an image using a trained deep learning model</span>
<span class="sd">    based on filepath.</span>
<span class="sd">    </span>
<span class="sd">    In:</span>
<span class="sd">    image_path : path to the image file</span>
<span class="sd">    model : model used to predict the classes    </span>
<span class="sd">    model_name : model_name (used to get settings)</span>
<span class="sd">    net_classes : mapping of model names to classes</span>
<span class="sd">    net_params : mapping of model classes to</span>
<span class="sd">                 their default parameters</span>
<span class="sd">    topk : number of top classes to get</span>
<span class="sd">    device : device on which to predict</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    <span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>
    
    <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">image_path</span><span class="p">)</span>

    <span class="n">img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">)</span>
    <span class="n">img</span><span class="o">.</span><span class="n">resize_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">img_crop</span><span class="p">,</span> <span class="n">img_crop</span><span class="p">)</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="c1"># Calculate the class probabilities (softmax) for img</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    
    
    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
    
    <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
    <span class="n">top_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">topk</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">idxs</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">idx_to_class</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="c1">#print(idxs)</span>
    <span class="k">return</span> <span class="n">top_p</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">idxs</span>
    
    <span class="c1"># TODO: Implement the code to predict the class from an image file</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">predict_preprocessed</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> 
                         <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">):</span>

    <span class="sd">&#39;&#39;&#39; Predict the class (or classes) of an image using a trained deep learning model</span>
<span class="sd">    based on an image tensor.</span>
<span class="sd">    </span>
<span class="sd">    In:</span>
<span class="sd">    img : image tensor</span>
<span class="sd">    model : model used to predict the classes    </span>
<span class="sd">    model_name : model_name (used to get settings)</span>
<span class="sd">    net_classes : mapping of model names to classes</span>
<span class="sd">    net_params : mapping of model classes to</span>
<span class="sd">                 their default parameters</span>
<span class="sd">    topk : number of top classes to get</span>
<span class="sd">    device : device on which to predict</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
    <span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>
    
    <span class="n">img</span><span class="o">.</span><span class="n">resize_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">img_crop</span><span class="p">,</span> <span class="n">img_crop</span><span class="p">)</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="c1"># Calculate the class probabilities (softmax) for img</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    
    <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
    <span class="n">top_p</span><span class="p">,</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">topk</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">idxs</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">idx_to_class</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="c1">#print(idxs)</span>
    <span class="k">return</span> <span class="n">top_p</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">idxs</span>
    
    <span class="c1"># TODO: Implement the code to predict the class from an image file</span>
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<h2 id="Sanity-Checking">Sanity Checking<a class="anchor-link" href="#Sanity-Checking">&#182;</a></h2><p>Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use <code>matplotlib</code> to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:</p>
<p><img src='assets/inference_example.png' width=300px></p>
<p>You can convert from the class integer encoding to actual flower names with the <code>cat_to_name.json</code> file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the <code>imshow</code> function defined above.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Display an image along with the top 5 classes</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;data/flowers/test/73/image_00394.jpg&#39;</span><span class="p">)</span>

<span class="n">img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">)</span>

<span class="n">top_p</span><span class="p">,</span> <span class="n">idxs</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="s1">&#39;data/flowers/test/73/image_00394.jpg&#39;</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> 
                      <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="n">cat_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">cat_to_name</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">]</span>
<span class="n">top_p</span> <span class="o">=</span> <span class="n">top_p</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>

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

<span class="c1">#denormalize</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>

<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">img</span> <span class="o">+</span> <span class="n">mean</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>

<span class="c1">#print(img.shape)</span>
<span class="c1">#print((np.min(img), np.max(img)))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">9</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">top_p</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">cat_names</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Class Probability&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<pre>[&#39;water lily&#39;, &#39;siam tulip&#39;, &#39;lotus lotus&#39;, &#39;cyclamen&#39;, &#39;red ginger&#39;]
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<pre>&lt;Figure size 432x288 with 0 Axes&gt;</pre>
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<div class="prompt input_prompt">In&nbsp;[23]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Loader for random test flower</span>

<span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
<span class="n">img_resize</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_resize&#39;</span><span class="p">]</span>
<span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;batch_size&#39;</span><span class="p">]</span>


<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;C:/udacity/data/flowers&#39;</span>
<span class="c1">#data_dir = &#39;data/flowers&#39;</span>

<span class="n">test_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span>


<span class="n">test_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">img_resize</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="n">img_crop</span><span class="p">),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                      <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                           <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>


<span class="n">test_data</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">test_transforms</span><span class="p">)</span>
<span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">testiter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Now for random flower</span>

<span class="n">model_class</span> <span class="o">=</span> <span class="n">net_classes</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span>
<span class="n">img_crop</span> <span class="o">=</span> <span class="n">net_params</span><span class="p">[</span><span class="n">model_class</span><span class="p">][</span><span class="s1">&#39;img_crop&#39;</span><span class="p">]</span>

<span class="k">try</span><span class="p">:</span>
    <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">testiter</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
<span class="k">except</span><span class="p">:</span>
    <span class="n">testiter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>
    <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">testiter</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
    
<span class="nb">print</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">images</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">64</span><span class="p">)]</span>
<span class="n">img</span><span class="o">.</span><span class="n">resize_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">img_crop</span><span class="p">,</span><span class="n">img_crop</span><span class="p">)</span>

<span class="c1"># Calculate the class probabilities (softmax) for img</span>
<span class="n">top_p</span><span class="p">,</span> <span class="n">idxs</span> <span class="o">=</span> <span class="n">predict_preprocessed</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> 
                                   <span class="n">net_classes</span><span class="p">,</span> <span class="n">net_params</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="n">cat_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">cat_to_name</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">idxs</span><span class="p">]</span>
<span class="n">top_p</span> <span class="o">=</span> <span class="n">top_p</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>

<span class="c1">#print(ps)sp</span>

<span class="c1">#img could have been moved to cuda</span>
<span class="c1">#img = img.to(&#39;cpu&#39;)</span>

<span class="c1">#denormalize</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>

<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">img</span> <span class="o">+</span> <span class="n">mean</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>

<span class="c1">#print(img.shape)</span>
<span class="c1">#print((np.min(img), np.max(img)))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">9</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">top_p</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">cat_names</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Class Probability&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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<pre>torch.Size([64, 3, 224, 224])
torch.Size([1, 102])
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<pre>&lt;Figure size 432x288 with 0 Axes&gt;</pre>
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