Image Classifier Project - Notebook.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="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="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">numpy</span> <span class="k">as</span> <span class="nn">np</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">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">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</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="o">%</span><span class="k">matplotlib</span> inline
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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"># Dataset parameters</span>
<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;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>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</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 set</span>

<span class="k">def</span> <span class="nf">data_preprocessing</span><span class="p">(</span><span class="n">train_dir</span><span class="p">,</span> <span class="n">valid_dir</span><span class="p">,</span> <span class="n">test_dir</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
                      <span class="n">mean</span><span class="o">=</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="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_transforms</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;training&#39;</span> <span class="p">:</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">30</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="mi">224</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="s1">&#39;validation&#39;</span> <span class="p">:</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="mi">256</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="mi">224</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="s1">&#39;testing&#39;</span> <span class="p">:</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="mi">256</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="mi">224</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">img_datasets</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;training&#39;</span> <span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">train_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">img_transforms</span><span class="p">[</span><span class="s1">&#39;training&#39;</span><span class="p">]),</span>
                    <span class="s1">&#39;validation&#39;</span> <span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">valid_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">img_transforms</span><span class="p">[</span><span class="s1">&#39;validation&#39;</span><span class="p">]),</span>
                    <span class="s1">&#39;testing&#39;</span> <span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">test_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">img_transforms</span><span class="p">[</span><span class="s1">&#39;testing&#39;</span><span class="p">])}</span>

    <span class="c1"># TODO: Using the image datasets and the trainforms, define the dataloaders</span>
    <span class="n">dataloaders</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;training&#39;</span> <span class="p">:</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">img_datasets</span><span class="p">[</span><span class="s1">&#39;training&#39;</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="s1">&#39;validation&#39;</span> <span class="p">:</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">img_datasets</span><span class="p">[</span><span class="s1">&#39;validation&#39;</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="s1">&#39;testing&#39;</span> <span class="p">:</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">img_datasets</span><span class="p">[</span><span class="s1">&#39;testing&#39;</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="k">return</span> <span class="n">img_datasets</span><span class="p">,</span> <span class="n">dataloaders</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="kn">import</span> <span class="nn">json</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>
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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="c1"># Fully connected classifier class definition</span>
<span class="k">class</span> <span class="nc">classifier_network</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Creates a fully connected network with an arbitrary amount of hidden layers.</span>
<span class="sd">        </span>
<span class="sd">        Arguments</span>
<span class="sd">        ---------</span>
<span class="sd">        input_size: integer, size of input</span>
<span class="sd">        output_size: integer, size of output</span>
<span class="sd">        hidden_sizes: list of integers, the sizes of the hidden layers</span>
<span class="sd">        dropout_p: float between 0 and 1, dropout probability</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout_prob</span><span class="p">)</span>
        
        <span class="c1"># First layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</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">input_size</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">])])</span>
        <span class="k">for</span> <span class="n">size1</span><span class="p">,</span> <span class="n">size2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">hidden_sizes</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">hidden_sizes</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">hidden_layers</span><span class="o">.</span><span class="n">extend</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">size1</span><span class="p">,</span> <span class="n">size2</span><span class="p">)])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_sizes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">output_size</span><span class="p">)</span>
        
        <span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span>
        
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">hidden_layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_layers</span><span class="p">:</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">hidden_layer</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
            <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
            
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">freeze_parameters</span><span class="p">(</span><span class="n">model</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="k">return</span> <span class="n">model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">image_classifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">class_to_idx</span><span class="p">,</span> <span class="n">feature_network</span><span class="o">=</span><span class="s1">&#39;resnet&#39;</span><span class="p">,</span> 
                 <span class="n">dropout_prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">learn_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">):</span>
        <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">        &#39;&#39;&#39;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        
        <span class="c1"># Model</span>
        <span class="k">if</span> <span class="n">feature_network</span> <span class="o">==</span> <span class="s1">&#39;densenet&#39;</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</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="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">freeze_parameters</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
            <span class="n">input_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">in_features</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier_network</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">,</span> 
                                                       <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_prob</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</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">learn_rate</span><span class="p">)</span>
            
        <span class="k">elif</span> <span class="n">feature_network</span> <span class="o">==</span> <span class="s1">&#39;resnet&#39;</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</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="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">freeze_parameters</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
            <span class="n">input_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">in_features</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">classifier_network</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">,</span> 
                                               <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_prob</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</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">learn_rate</span><span class="p">)</span>
            
        <span class="k">elif</span> <span class="n">feature_network</span> <span class="o">==</span> <span class="s1">&#39;vgg&#39;</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</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="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">freeze_parameters</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
            <span class="n">input_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">in_features</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier_network</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_sizes</span><span class="p">,</span> 
                                                       <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_prob</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</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">learn_rate</span><span class="p">)</span>
        
        
        <span class="bp">self</span><span class="o">.</span><span class="n">feature_network</span> <span class="o">=</span> <span class="n">feature_network</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_sizes</span> <span class="o">=</span> <span class="n">hidden_sizes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout_prob</span> <span class="o">=</span> <span class="n">dropout_prob</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">learn_rate</span> <span class="o">=</span> <span class="n">learn_rate</span>
        <span class="bp">self</span><span class="o">.</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="bp">self</span><span class="o">.</span><span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">class_to_idx</span>
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<div class="prompt input_prompt">In&nbsp;[9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Classifier parameters</span>
<span class="n">output_size</span> <span class="o">=</span> <span class="mi">102</span>
<span class="n">hidden_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">]</span>
<span class="n">dropout_prob</span> <span class="o">=</span> <span class="mf">0.2</span>
<span class="c1">#model = models.densenet121(pretrained=True)</span>
<span class="c1">#model.classifier.in_features</span>
<span class="n">network</span> <span class="o">=</span> <span class="n">image_classifier</span><span class="p">(</span><span class="n">hidden_sizes</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">img_datasets</span><span class="p">[</span><span class="s1">&#39;training&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">class_to_idx</span><span class="p">,</span> <span class="s1">&#39;densenet&#39;</span><span class="p">)</span>
<span class="n">network</span>
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<pre>/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
Downloading: &#34;https://download.pytorch.org/models/densenet121-a639ec97.pth&#34; to /root/.torch/models/densenet121-a639ec97.pth
100%|██████████| 32342954/32342954 [00:00&lt;00:00, 40503269.03it/s]
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<pre>image_classifier(
  (model): DenseNet(
    (features): Sequential(
      (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu0): ReLU(inplace)
      (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (denseblock1): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition1): _Transition(
        (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock2): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition2): _Transition(
        (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock3): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer13): _DenseLayer(
          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer14): _DenseLayer(
          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer15): _DenseLayer(
          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer16): _DenseLayer(
          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer17): _DenseLayer(
          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer18): _DenseLayer(
          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer19): _DenseLayer(
          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer20): _DenseLayer(
          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer21): _DenseLayer(
          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer22): _DenseLayer(
          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer23): _DenseLayer(
          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer24): _DenseLayer(
          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition3): _Transition(
        (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock4): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer13): _DenseLayer(
          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer14): _DenseLayer(
          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer15): _DenseLayer(
          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer16): _DenseLayer(
          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace)
          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (classifier): classifier_network(
      (dropout): Dropout(p=0.2)
      (hidden_layers): ModuleList(
        (0): Linear(in_features=1024, out_features=512, bias=True)
        (1): Linear(in_features=512, out_features=256, bias=True)
        (2): Linear(in_features=256, out_features=128, bias=True)
      )
      (output_layer): Linear(in_features=128, out_features=102, bias=True)
    )
  )
  (criterion): NLLLoss()
)</pre>
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<div class="prompt input_prompt">In&nbsp;[10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">validation</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">n_validation_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloaders</span><span class="p">[</span><span class="s1">&#39;validation&#39;</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="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">dataloaders</span><span class="p">[</span><span class="s1">&#39;validation&#39;</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">output</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        
            <span class="n">loss</span> <span class="o">+=</span> <span class="n">network</span><span class="o">.</span><span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        
            <span class="n">probs</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">predicted_classes</span> <span class="o">=</span> <span class="n">probs</span><span class="o">.</span><span class="n">max</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="mi">1</span><span class="p">]</span>
            <span class="n">correct_classes</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="n">predicted_classes</span><span class="p">)</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="n">accuracy</span> <span class="o">+=</span> <span class="n">correct_classes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
        
    <span class="k">return</span> <span class="n">loss</span><span class="o">/</span><span class="n">n_validation_batches</span><span class="p">,</span> <span class="n">accuracy</span><span class="o">/</span><span class="n">n_validation_batches</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</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="n">epochs</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">print_every</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
    <span class="n">training_loss</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span>

    <span class="n">network</span><span class="o">.</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">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">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</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">dataloaders</span><span class="p">[</span><span class="s1">&#39;training&#39;</span><span class="p">]:</span>
            <span class="n">steps</span> <span class="o">+=</span> <span class="mi">1</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="c1"># Loss and gradient calculation</span>
            <span class="n">network</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">criterion</span><span class="p">(</span><span class="n">output</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">network</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
        
            <span class="n">training_loss</span> <span class="o">+=</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">/</span> <span class="n">print_every</span><span class="p">)</span>
        
            <span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
                <span class="n">valid_loss</span><span class="p">,</span> <span class="n">valid_accuracy</span> <span class="o">=</span> <span class="n">validation</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
            
                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch: </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">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="s2">&quot;Training loss: </span><span class="si">{:.3f}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">training_loss</span><span class="p">),</span>
                      <span class="s2">&quot;Validation loss: </span><span class="si">{:.3f}</span><span class="s2">...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">),</span>
                      <span class="s2">&quot;Validation accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">valid_accuracy</span><span class="p">))</span>
            
                <span class="n">training_loss</span> <span class="o">=</span> <span class="mi">0</span>
                <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training parameters</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">7</span>
<span class="n">print_every</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">print_every</span><span class="p">)</span>
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<pre>Epoch: 1/7... Training loss: 4.558... Validation loss: 4.484... Validation accuracy: 0.035
Epoch: 1/7... Training loss: 4.483... Validation loss: 4.278... Validation accuracy: 0.076
Epoch: 1/7... Training loss: 4.233... Validation loss: 3.903... Validation accuracy: 0.124
Epoch: 1/7... Training loss: 3.918... Validation loss: 3.464... Validation accuracy: 0.209
Epoch: 1/7... Training loss: 3.504... Validation loss: 3.044... Validation accuracy: 0.278
Epoch: 2/7... Training loss: 3.234... Validation loss: 2.682... Validation accuracy: 0.290
Epoch: 2/7... Training loss: 2.935... Validation loss: 2.345... Validation accuracy: 0.357
Epoch: 2/7... Training loss: 2.736... Validation loss: 2.170... Validation accuracy: 0.417
Epoch: 2/7... Training loss: 2.636... Validation loss: 2.012... Validation accuracy: 0.439
Epoch: 2/7... Training loss: 2.473... Validation loss: 1.853... Validation accuracy: 0.481
Epoch: 3/7... Training loss: 2.221... Validation loss: 1.621... Validation accuracy: 0.557
Epoch: 3/7... Training loss: 2.139... Validation loss: 1.580... Validation accuracy: 0.576
Epoch: 3/7... Training loss: 2.005... Validation loss: 1.367... Validation accuracy: 0.618
Epoch: 3/7... Training loss: 1.913... Validation loss: 1.319... Validation accuracy: 0.628
Epoch: 3/7... Training loss: 1.850... Validation loss: 1.204... Validation accuracy: 0.662
Epoch: 4/7... Training loss: 1.743... Validation loss: 1.113... Validation accuracy: 0.674
Epoch: 4/7... Training loss: 1.731... Validation loss: 1.102... Validation accuracy: 0.678
Epoch: 4/7... Training loss: 1.664... Validation loss: 1.111... Validation accuracy: 0.691
Epoch: 4/7... Training loss: 1.535... Validation loss: 0.940... Validation accuracy: 0.736
Epoch: 4/7... Training loss: 1.522... Validation loss: 0.919... Validation accuracy: 0.724
Epoch: 5/7... Training loss: 1.498... Validation loss: 0.922... Validation accuracy: 0.713
Epoch: 5/7... Training loss: 1.402... Validation loss: 0.985... Validation accuracy: 0.706
Epoch: 5/7... Training loss: 1.432... Validation loss: 0.909... Validation accuracy: 0.744
Epoch: 5/7... Training loss: 1.324... Validation loss: 0.796... Validation accuracy: 0.777
Epoch: 5/7... Training loss: 1.373... Validation loss: 0.782... Validation accuracy: 0.767
Epoch: 6/7... Training loss: 1.263... Validation loss: 0.763... Validation accuracy: 0.781
Epoch: 6/7... Training loss: 1.279... Validation loss: 0.710... Validation accuracy: 0.794
Epoch: 6/7... Training loss: 1.249... Validation loss: 0.736... Validation accuracy: 0.783
Epoch: 6/7... Training loss: 1.221... Validation loss: 0.679... Validation accuracy: 0.797
Epoch: 6/7... Training loss: 1.154... Validation loss: 0.673... Validation accuracy: 0.807
Epoch: 7/7... Training loss: 1.191... Validation loss: 0.707... Validation accuracy: 0.801
Epoch: 7/7... Training loss: 1.193... Validation loss: 0.660... Validation accuracy: 0.799
Epoch: 7/7... Training loss: 1.204... Validation loss: 0.638... Validation accuracy: 0.811
Epoch: 7/7... Training loss: 1.109... Validation loss: 0.704... Validation accuracy: 0.797
Epoch: 7/7... Training loss: 1.138... Validation loss: 0.600... Validation accuracy: 0.828
Epoch: 7/7... Training loss: 1.036... Validation loss: 0.617... Validation accuracy: 0.822
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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="k">def</span> <span class="nf">test_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
    <span class="n">accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">n_testing_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloaders</span><span class="p">[</span><span class="s1">&#39;testing&#39;</span><span class="p">])</span>
    <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">network</span><span class="o">.</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">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">dataloaders</span><span class="p">[</span><span class="s1">&#39;testing&#39;</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">output</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        <span class="n">probs</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">predicted_classes</span> <span class="o">=</span> <span class="n">probs</span><span class="o">.</span><span class="n">max</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="mi">1</span><span class="p">]</span>
        <span class="n">correct_classes</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="n">predicted_classes</span><span class="p">)</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="n">accuracy</span> <span class="o">+=</span> <span class="n">correct_classes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
        
    <span class="k">return</span> <span class="n">accuracy</span> <span class="o">/</span> <span class="n">n_testing_batches</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">))</span>
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<pre>Test accuracy: 0.811
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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="k">def</span> <span class="nf">save_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">filepath</span><span class="p">):</span>
    <span class="n">checkpoint</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;model_state_dict&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
                  <span class="s1">&#39;feature_network&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">feature_network</span><span class="p">,</span>
                  <span class="s1">&#39;output_size&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">output_size</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_sizes&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">hidden_sizes</span><span class="p">,</span>
                  <span class="s1">&#39;dropout_prob&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">dropout_prob</span><span class="p">,</span>
                  <span class="s1">&#39;learn_rate&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">learn_rate</span><span class="p">,</span>
                  <span class="s1">&#39;class_to_idx&#39;</span> <span class="p">:</span> <span class="n">network</span><span class="o">.</span><span class="n">class_to_idx</span><span class="p">,</span>
                  <span class="s1">&#39;optimizer_state_dict&#39;</span> <span class="p">:</span> <span class="n">network</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="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">filepath</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">filepath</span> <span class="o">=</span> <span class="s1">&#39;checkpoint.pth&#39;</span>
<span class="n">save_network</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">filepath</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">load_network</span><span class="p">(</span><span class="n">filepath</span><span class="p">):</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">filepath</span><span class="p">)</span>
    <span class="n">network</span> <span class="o">=</span> <span class="n">image_classifier</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;hidden_sizes&#39;</span><span class="p">],</span>
                               <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;output_size&#39;</span><span class="p">],</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">checkpoint</span><span class="p">[</span><span class="s1">&#39;feature_network&#39;</span><span class="p">],</span> 
                               <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;dropout_prob&#39;</span><span class="p">],</span> 
                               <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;learn_rate&#39;</span><span class="p">])</span>
    <span class="n">network</span><span class="o">.</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">network</span><span class="o">.</span><span class="n">optimizer</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;optimizer_state_dict&#39;</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">network</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">network2</span> <span class="o">=</span> <span class="n">load_network</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
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<pre>/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">test_network</span><span class="p">(</span><span class="n">network2</span><span class="p">,</span> <span class="n">dataloaders</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test accuracy: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">))</span>
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<pre>Test accuracy: 0.810
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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"># 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">image</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</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="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="sd">&#39;&#39;&#39; </span>
<span class="sd">    Scales, crops, and normalizes a PIL image for a PyTorch model,</span>
<span class="sd">    returns an Numpy array</span>
<span class="sd">    Arguments</span>
<span class="sd">    ---------</span>
<span class="sd">    image: Image-object, the image to be processed</span>
<span class="sd">    mean: list of floats, the mean of the different image channels</span>
<span class="sd">    std: list of floats, the standard deviation of the different image channels</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># Scaling</span>
    <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span>
    <span class="n">scale_factor</span> <span class="o">=</span> <span class="mi">256</span> <span class="o">/</span> <span class="nb">min</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">)</span>
    <span class="n">new_width</span><span class="p">,</span> <span class="n">new_height</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">scale_factor</span> <span class="o">*</span> <span class="n">width</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">scale_factor</span> <span class="o">*</span> <span class="n">height</span><span class="p">)</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">new_width</span><span class="p">,</span> <span class="n">new_height</span><span class="p">))</span>
    
    <span class="c1"># Cropping</span>
    <span class="n">left</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="n">new_width</span><span class="o">//</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">112</span><span class="p">,</span> <span class="n">new_width</span><span class="o">//</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">112</span>
    <span class="n">upper</span><span class="p">,</span> <span class="n">lower</span> <span class="o">=</span> <span class="n">new_height</span><span class="o">//</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">112</span><span class="p">,</span> <span class="n">new_height</span><span class="o">//</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">112</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</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">upper</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">))</span>
    
    <span class="c1"># Normalizing</span>
    <span class="n">np_image</span><span class="p">,</span> <span class="n">mean</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="n">image</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">std</span><span class="p">)</span>
    <span class="n">np_image</span> <span class="o">=</span> <span class="p">(</span><span class="n">np_image</span> <span class="o">/</span> <span class="mi">255</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="c1"># Reordering</span>
    <span class="n">np_image</span> <span class="o">=</span> <span class="n">np_image</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="k">return</span> <span class="n">np_image</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="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="n">image</span> <span class="o">=</span> <span class="n">image</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_path</span> <span class="o">=</span> <span class="s1">&#39;flowers/test/18/image_04272.jpg&#39;</span>
<span class="n">image</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">img_path</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">image</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"># TODO: Implement the code to predict the class from an image file</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">network</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="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; </span>
<span class="sd">    Predict the class (or classes) of an image using a trained deep learning model.</span>
<span class="sd">    Arguments</span>
<span class="sd">    ---------</span>
<span class="sd">    image_path: string, path to image</span>
<span class="sd">    network: image_classifier, the CNN image classifier</span>
<span class="sd">    device: string, the device to calculate the predictions on</span>
<span class="sd">    topk: int, the k most probable predicted classes</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># Image processing</span>
    <span class="n">image</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">np_image</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">image_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np_image</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
    <span class="n">image_tensor</span><span class="o">.</span><span class="n">unsqueeze_</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    
    <span class="c1"># Making network and input ready for evaluation</span>
    <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">network</span><span class="o">.</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">image_tensor</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"># Processing output</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">image_tensor</span><span class="p">)</span>
    <span class="n">probs</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_probs</span><span class="p">,</span> <span class="n">top_indices</span> <span class="o">=</span> <span class="n">probs</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">idx_to_class</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span> <span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">network</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="n">top_probs</span> <span class="o">=</span> <span class="n">top_probs</span><span class="o">.</span><span class="n">detach</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">ravel</span><span class="p">()</span>
    <span class="n">top_indices</span> <span class="o">=</span> <span class="n">top_indices</span><span class="o">.</span><span class="n">detach</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">ravel</span><span class="p">()</span>
    
    <span class="n">top_classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">idx_to_class</span><span class="o">.</span><span class="n">get</span><span class="p">)(</span><span class="n">top_indices</span><span class="p">)</span>
    
    <span class="c1"># Convert from np.array to lists</span>
    <span class="n">top_probs</span> <span class="o">=</span> <span class="n">top_probs</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    <span class="n">top_classes</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">top_classes</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
    
    <span class="k">return</span> <span class="n">top_probs</span><span class="p">,</span> <span class="n">top_classes</span>
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<div class=" highlight hl-ipython3"><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">img_path</span><span class="p">,</span> <span class="n">network</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
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<pre>[0.9859468936920166, 0.01161868590861559, 0.0018507075728848577, 0.0002459698007442057, 0.00011223888577660546]
[&#39;18&#39;, &#39;17&#39;, &#39;91&#39;, &#39;6&#39;, &#39;13&#39;]
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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="k">def</span> <span class="nf">plot_results</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">class_to_name</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Shows the input image and plots the top k predicted classes.</span>
<span class="sd">    Arguments</span>
<span class="sd">    ---------</span>
<span class="sd">    image_path: string, path to the image</span>
<span class="sd">    probs: list of floats, the probability of the predicted classes</span>
<span class="sd">    classes: list of strings, the predicted class indices (strings)</span>
<span class="sd">    class_to_name: dict, key = string, value = string</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># Class names</span>
    <span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">class_to_name</span><span class="p">[</span><span class="n">item</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">]</span>
    <span class="n">y_pos</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">))]</span>
    
    <span class="c1"># Image processing</span>
    <span class="n">image</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">image</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    
    <span class="c1"># Probabilities plot</span>
    <span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</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="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="n">rowspan</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">colspan</span><span class="o">=</span><span class="mi">1</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="nb">max</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="o">/</span> <span class="mi">5</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticklabels</span><span class="p">(</span><span class="n">class_names</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">y_pos</span><span class="p">,</span> <span class="n">probs</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
    
    <span class="c1"># Image plot</span>
    <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</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="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="n">rowspan</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">colspan</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">([])</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_ticks</span><span class="p">([])</span>
    <span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">ax1</span><span class="p">)</span>
    
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plot_results</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">cat_to_name</span><span class="p">)</span>
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