dlnd_face_generation.html

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<title>dlnd_face_generation</title>

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https:/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    /*
Author: Neil Panchal
http://neil.panchal.io

New colors. These are a bit darker to allow for a better contrast. I started with Solarized palette and adjusted the colors as needed.


YELLOW      = #ba9600			[181,137,0]
ORANGE      = #BA5400			[186,84,0]
RED         = #D43132			[176,47,48]
MAGENTA     = #D14187			[209,65,135]
VIOLET      = #793ac7			[144,89,212]
BLUE        = #007ad0			[15,134,217]
CYAN        = #009489			[0,163,151]
GREEN       = #688A0A			[104,138,10]
BROWN 		= #A05200			[156,98,48]

BACKGROUND  = #F8F8F8
DARK GRAY   = #383838
MID GRAY    = #d8d8d8
LIGHT GRAY  = #828282
*/

/* FONTS */

@font-face {
 font-family: 'sf-display';
 font-weight: normal;
 font-style: normal;
 src: url('fonts/sf-display/SanFranciscoDisplay-Regular.otf') format('opentype');
}

@font-face {
 font-family: 'sf-display';
 font-weight: normal;
 font-style: italic;
 src: url('fonts/sf-display/SanFranciscoDisplay-RegularItalic.otf') format('opentype');
}

@font-face {
 font-family: 'sf-display';
 font-weight: bold;
 font-style: normal;
 src: url('fonts/sf-display/SanFranciscoDisplay-Bold.otf') format('opentype');
}

@font-face {
 font-family: 'sf-display';
 font-weight: bold;
 font-style: italic;
 src: url('fonts/sf-display/SanFranciscoDisplay-BoldItalic.otf') format('opentype');
}

@font-face {
 font-family: 'cmu-text-decorated';
 font-weight: normal;
 font-style: italic;
 src: url('fonts/cmu-text/CMUSerif-UprightItalic.otf') format('opentype');
}

@font-face {
 font-family: 'cmu-text';
 font-weight: normal;
 font-style: normal;
 src: url('fonts/cmu-text/CMUSerif-Roman.otf') format('opentype');
}

@font-face {
 font-family: 'cmu-text';
 font-weight: normal;
 font-style: italic;
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<h1 id="Face-Generation">Face Generation<a class="anchor-link" href="#Face-Generation">&#182;</a></h1><p>In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate <em>new</em> images of faces that look as realistic as possible!</p>
<p>The project will be broken down into a series of tasks from <strong>loading in data to defining and training adversarial networks</strong>. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.</p>
<h3 id="Get-the-Data">Get the Data<a class="anchor-link" href="#Get-the-Data">&#182;</a></h3><p>You'll be using the <a href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html">CelebFaces Attributes Dataset (CelebA)</a> to train your adversarial networks.</p>
<p>This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.</p>
<h3 id="Pre-processed-Data">Pre-processed Data<a class="anchor-link" href="#Pre-processed-Data">&#182;</a></h3><p>Since the project's main focus is on building the GANs, we've done <em>some</em> of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.</p>
<p><img src='assets/processed_face_data.png' width=60% /></p>
<blockquote><p>If you are working locally, you can download this data <a href="https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip">by clicking here</a></p>
</blockquote>
<p>This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data <code>processed_celeba_small/</code></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># can comment out after executing</span>
<span class="c1"># !unzip processed_celeba_small.zip</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;processed_celeba_small/&#39;</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">pickle</span> <span class="k">as</span> <span class="nn">pkl</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">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
<span class="c1">#import helper</span>

<span class="o">%</span><span class="k">matplotlib</span> inline
</pre></div>

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<h2 id="Visualize-the-CelebA-Data">Visualize the CelebA Data<a class="anchor-link" href="#Visualize-the-CelebA-Data">&#182;</a></h2><p>The <a href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html">CelebA</a> dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with <a href="https://en.wikipedia.org/wiki/Channel_(digital_image">3 color channels (RGB)</a>#RGB_Images) each.</p>
<h3 id="Pre-process-and-Load-the-Data">Pre-process and Load the Data<a class="anchor-link" href="#Pre-process-and-Load-the-Data">&#182;</a></h3><p>Since the project's main focus is on building the GANs, we've done <em>some</em> of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This <em>pre-processed</em> dataset is a smaller subset of the very large CelebA data.</p>
<blockquote><p>There are a few other steps that you'll need to <strong>transform</strong> this data and create a <strong>DataLoader</strong>.</p>
</blockquote>
<h4 id="Exercise:-Complete-the-following-get_dataloader-function,-such-that-it-satisfies-these-requirements:">Exercise: Complete the following <code>get_dataloader</code> function, such that it satisfies these requirements:<a class="anchor-link" href="#Exercise:-Complete-the-following-get_dataloader-function,-such-that-it-satisfies-these-requirements:">&#182;</a></h4><ul>
<li>Your images should be square, Tensor images of size <code>image_size x image_size</code> in the x and y dimension.</li>
<li>Your function should return a DataLoader that shuffles and batches these Tensor images.</li>
</ul>
<h4 id="ImageFolder">ImageFolder<a class="anchor-link" href="#ImageFolder">&#182;</a></h4><p>To create a dataset given a directory of images, it's recommended that you use PyTorch's <a href="https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder">ImageFolder</a> wrapper, with a root directory <code>processed_celeba_small/</code> and data transformation passed in.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># necessary imports</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>

<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_dataloader</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">image_size</span><span class="p">,</span> <span class="n">data_dir</span><span class="o">=</span><span class="s1">&#39;processed_celeba_small/&#39;</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Batch the neural network data using DataLoader</span>
<span class="sd">    :param batch_size: The size of each batch; the number of images in a batch</span>
<span class="sd">    :param img_size: The square size of the image data (x, y)</span>
<span class="sd">    :param data_dir: Directory where image data is located</span>
<span class="sd">    :return: DataLoader with batched data</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="c1"># TODO: Implement function and return a dataloader</span>
    <span class="c1"># resize and normalize the images</span>
    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="n">image_size</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="c1"># images directory</span>
    <span class="n">images_path</span> <span class="o">=</span> <span class="n">data_dir</span>
    
    <span class="c1"># define dataset using ImageFolder</span>
    <span class="n">dataset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">images_path</span><span class="p">,</span> <span class="n">transform</span><span class="p">)</span>
    
    <span class="c1"># create loader</span>
    <span class="n">image_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">image_loader</span>
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<h2 id="Create-a-DataLoader">Create a DataLoader<a class="anchor-link" href="#Create-a-DataLoader">&#182;</a></h2><h4 id="Exercise:-Create-a-DataLoader-celeba_train_loader-with-appropriate-hyperparameters.">Exercise: Create a DataLoader <code>celeba_train_loader</code> with appropriate hyperparameters.<a class="anchor-link" href="#Exercise:-Create-a-DataLoader-celeba_train_loader-with-appropriate-hyperparameters.">&#182;</a></h4><p>Call the above function and create a dataloader to view images.</p>
<ul>
<li>You can decide on any reasonable <code>batch_size</code> parameter</li>
<li>Your <code>image_size</code> <strong>must be</strong> <code>32</code>. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define function hyperparameters</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">img_size</span> <span class="o">=</span> <span class="mi">32</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># Call your function and get a dataloader</span>
<span class="n">celeba_train_loader</span> <span class="o">=</span> <span class="n">get_dataloader</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">img_size</span><span class="p">)</span>
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<p>Next, you can view some images! You should seen square images of somewhat-centered faces.</p>
<p>Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested <code>imshow</code> code is below, but it may not be perfect.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># helper display function</span>
<span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">):</span>
    <span class="n">npimg</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">npimg</span><span class="p">,</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="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># obtain one batch of training images</span>
<span class="n">dataiter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">celeba_train_loader</span><span class="p">)</span>
<span class="n">images</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">dataiter</span><span class="o">.</span><span class="n">next</span><span class="p">()</span> <span class="c1"># _ for no labels</span>

<span class="c1"># plot the images in the batch, along with the corresponding labels</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">plot_size</span><span class="o">=</span><span class="mi">20</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">plot_size</span><span class="p">):</span>
    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">plot_size</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">idx</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">xticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[])</span>
    <span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
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<h4 id="Exercise:-Pre-process-your-image-data-and-scale-it-to-a-pixel-range-of--1-to-1">Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1<a class="anchor-link" href="#Exercise:-Pre-process-your-image-data-and-scale-it-to-a-pixel-range-of--1-to-1">&#182;</a></h4><p>You need to do a bit of pre-processing; you know that the output of a <code>tanh</code> activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Complete the scale function</span>
<span class="k">def</span> <span class="nf">scale</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">feature_range</span><span class="o">=</span><span class="p">(</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="sd">&#39;&#39;&#39; Scale takes in an image x and returns that image, scaled</span>
<span class="sd">       with a feature_range of pixel values from -1 to 1. </span>
<span class="sd">       This function assumes that the input x is already scaled from 0-1.&#39;&#39;&#39;</span>
    <span class="c1"># assume x is scaled to (0, 1)</span>
    <span class="c1"># scale to feature_range and return scaled x</span>
    <span class="nb">min</span><span class="p">,</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">feature_range</span>

    <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span> <span class="o">-</span> <span class="nb">min</span><span class="p">)</span> <span class="o">+</span> <span class="nb">min</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># check scaled range</span>
<span class="c1"># should be close to -1 to 1</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">scaled_img</span> <span class="o">=</span> <span class="n">scale</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Min: &#39;</span><span class="p">,</span> <span class="n">scaled_img</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Max: &#39;</span><span class="p">,</span> <span class="n">scaled_img</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
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<pre>Min:  tensor(-0.8980)
Max:  tensor(0.9765)
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<h1 id="Define-the-Model">Define the Model<a class="anchor-link" href="#Define-the-Model">&#182;</a></h1><p>A GAN is comprised of two adversarial networks, a discriminator and a generator.</p>
<h2 id="Discriminator">Discriminator<a class="anchor-link" href="#Discriminator">&#182;</a></h2><p>Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with <strong>normalization</strong>. You are also allowed to create any helper functions that may be useful.</p>
<h4 id="Exercise:-Complete-the-Discriminator-class">Exercise: Complete the Discriminator class<a class="anchor-link" href="#Exercise:-Complete-the-Discriminator-class">&#182;</a></h4><ul>
<li>The inputs to the discriminator are 32x32x3 tensor images</li>
<li>The output should be a single value that will indicate whether a given image is real or fake</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</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="c1"># Helper conv function</span>
<span class="k">def</span> <span class="nf">conv</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Creates a convolutional layer, with optional batch normalization.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">conv_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span> 
                           <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    
    <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">conv_layer</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span>
        <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">))</span>
        
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Discriminator</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="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">conv_dim</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Initialize the Discriminator Module</span>
<span class="sd">        :param conv_dim: The depth of the first convolutional layer</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Discriminator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="c1"># complete init function</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv_dim</span> <span class="o">=</span> <span class="n">conv_dim</span>
        
        <span class="c1"># Convolutional layers</span>
        <span class="c1"># 32x32x3 input</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">conv_dim</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="c1"># 16x16 out</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv</span><span class="p">(</span><span class="n">conv_dim</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> 
        <span class="c1"># 8x8 out</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv</span><span class="p">(</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
        <span class="c1"># 4x4 out</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv4</span> <span class="o">=</span> <span class="n">conv</span><span class="p">(</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
        <span class="c1"># 2x2 out</span>
        
        <span class="c1"># Final layer, fully-connected layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc</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">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        Forward propagation of the neural network</span>
<span class="sd">        :param x: The input to the neural network     </span>
<span class="sd">        :return: Discriminator logits; the output of the neural network</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># define feedforward behavior</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mf">0.2</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="mf">0.2</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="mf">0.2</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">leaky_relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv4</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="mf">0.2</span><span class="p">)</span>
        
        <span class="c1"># flatten</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
        
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">out</span>


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<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_discriminator</span><span class="p">(</span><span class="n">Discriminator</span><span class="p">)</span>
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<pre>Tests Passed
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<h2 id="Generator">Generator<a class="anchor-link" href="#Generator">&#182;</a></h2><p>The generator should upsample an input and generate a <em>new</em> image of the same size as our training data <code>32x32x3</code>. This should be mostly transpose convolutional layers with normalization applied to the outputs.</p>
<h4 id="Exercise:-Complete-the-Generator-class">Exercise: Complete the Generator class<a class="anchor-link" href="#Exercise:-Complete-the-Generator-class">&#182;</a></h4><ul>
<li>The inputs to the generator are vectors of some length <code>z_size</code></li>
<li>The output should be a image of shape <code>32x32x3</code></li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># helper deconv function</span>
<span class="k">def</span> <span class="nf">deconv</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Creates a transposed-convolutional layer, with optional batch normalization.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># create a sequence of transpose + optional batch norm layers</span>
    <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">transpose_conv_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> 
                                              <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="c1"># append transpose convolutional layer</span>
    <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transpose_conv_layer</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span>
        <span class="c1"># append batchnorm layer</span>
        <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">))</span>
        
    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">Generator</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="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z_size</span><span class="p">,</span> <span class="n">conv_dim</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Initialize the Generator Module</span>
<span class="sd">        :param z_size: The length of the input latent vector, z</span>
<span class="sd">        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Generator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="c1"># complete init function</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv_dim</span> <span class="o">=</span> <span class="n">conv_dim</span>
        
        <span class="c1"># First, fully connected layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc</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">z_size</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span>
        
        <span class="c1"># Transpose convolutional layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">t_conv1</span> <span class="o">=</span> <span class="n">deconv</span><span class="p">(</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">t_conv2</span> <span class="o">=</span> <span class="n">deconv</span><span class="p">(</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">t_conv3</span> <span class="o">=</span> <span class="n">deconv</span><span class="p">(</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">conv_dim</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">t_conv4</span> <span class="o">=</span> <span class="n">deconv</span><span class="p">(</span><span class="n">conv_dim</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">False</span><span class="p">)</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        Forward propagation of the neural network</span>
<span class="sd">        :param x: The input to the neural network     </span>
<span class="sd">        :return: A 32x32x3 Tensor image as output</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># define feedforward behavior</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_dim</span><span class="o">*</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># (batch_size, depth, 2, 2)</span>
        
        <span class="c1"># transpose convolutional layers</span>
        <span class="n">out</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="bp">self</span><span class="o">.</span><span class="n">t_conv1</span><span class="p">(</span><span class="n">out</span><span class="p">))</span>
        <span class="n">out</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="bp">self</span><span class="o">.</span><span class="n">t_conv2</span><span class="p">(</span><span class="n">out</span><span class="p">))</span>
        <span class="n">out</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="bp">self</span><span class="o">.</span><span class="n">t_conv3</span><span class="p">(</span><span class="n">out</span><span class="p">))</span>
        
        <span class="c1"># last layer with tanh activation</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">t_conv4</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="n">out</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_generator</span><span class="p">(</span><span class="n">Generator</span><span class="p">)</span>
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<pre>Tests Passed
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<h2 id="Initialize-the-weights-of-your-networks">Initialize the weights of your networks<a class="anchor-link" href="#Initialize-the-weights-of-your-networks">&#182;</a></h2><p>To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the <a href="https://arxiv.org/pdf/1511.06434.pdf">original DCGAN paper</a>, they say:</p>
<blockquote><p>All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.</p>
</blockquote>
<p>So, your next task will be to define a weight initialization function that does just this!</p>
<p>You can refer back to the lesson on weight initialization or even consult existing model code, such as that from <a href="https:/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py">the <code>networks.py</code> file in CycleGAN Github repository</a> to help you complete this function.</p>
<h4 id="Exercise:-Complete-the-weight-initialization-function">Exercise: Complete the weight initialization function<a class="anchor-link" href="#Exercise:-Complete-the-weight-initialization-function">&#182;</a></h4><ul>
<li>This should initialize only <strong>convolutional</strong> and <strong>linear</strong> layers</li>
<li>Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.</li>
<li>The bias terms, if they exist, may be left alone or set to 0.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">weights_init_normal</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Applies initial weights to certain layers in a model .</span>
<span class="sd">    The weights are taken from a normal distribution </span>
<span class="sd">    with mean = 0, std dev = 0.02.</span>
<span class="sd">    :param m: A module or layer in a network    </span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># classname will be something like:</span>
    <span class="c1"># `Conv`, `BatchNorm2d`, `Linear`, etc.</span>
    <span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
    <span class="n">init_gain</span> <span class="o">=</span> <span class="mf">0.02</span>
    
    <span class="c1"># TODO: Apply initial weights to convolutional and linear layers</span>
    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">&#39;weight&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Conv&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Linear&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="c1"># Init with normal distribution centered around 0 and with std of 0.02</span>
        <span class="n">dist_mean</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">dist_mean</span><span class="p">,</span> <span class="n">init_gain</span><span class="p">)</span>
        
        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="s1">&#39;bias&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
            
    <span class="c1"># BatchNorm layer is not a matrix; only normal distribution applies</span>
    <span class="k">elif</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;BatchNorm2d&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
        <span class="n">dist_mean</span> <span class="o">=</span> <span class="mf">1.0</span>
        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">dist_mean</span><span class="p">,</span> <span class="n">init_gain</span><span class="p">)</span>
        <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>            
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<h2 id="Build-complete-network">Build complete network<a class="anchor-link" href="#Build-complete-network">&#182;</a></h2><p>Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">build_network</span><span class="p">(</span><span class="n">d_conv_dim</span><span class="p">,</span> <span class="n">g_conv_dim</span><span class="p">,</span> <span class="n">z_size</span><span class="p">):</span>
    <span class="c1"># define discriminator and generator</span>
    <span class="n">D</span> <span class="o">=</span> <span class="n">Discriminator</span><span class="p">(</span><span class="n">d_conv_dim</span><span class="p">)</span>
    <span class="n">G</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">z_size</span><span class="o">=</span><span class="n">z_size</span><span class="p">,</span> <span class="n">conv_dim</span><span class="o">=</span><span class="n">g_conv_dim</span><span class="p">)</span>

    <span class="c1"># initialize model weights</span>
    <span class="n">D</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init_normal</span><span class="p">)</span>
    <span class="n">G</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init_normal</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">D</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">D</span><span class="p">,</span> <span class="n">G</span>
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<h4 id="Exercise:-Define-model-hyperparameters">Exercise: Define model hyperparameters<a class="anchor-link" href="#Exercise:-Define-model-hyperparameters">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define model hyperparams</span>
<span class="n">d_conv_dim</span> <span class="o">=</span> <span class="mi">32</span> 
<span class="n">g_conv_dim</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">z_size</span> <span class="o">=</span> <span class="mi">100</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">D</span><span class="p">,</span> <span class="n">G</span> <span class="o">=</span> <span class="n">build_network</span><span class="p">(</span><span class="n">d_conv_dim</span><span class="p">,</span> <span class="n">g_conv_dim</span><span class="p">,</span> <span class="n">z_size</span><span class="p">)</span>
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<pre>Discriminator(
  (conv1): Sequential(
    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  )
  (conv2): Sequential(
    (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (conv3): Sequential(
    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (conv4): Sequential(
    (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (fc): Linear(in_features=1024, out_features=1, bias=True)
)

Generator(
  (fc): Linear(in_features=100, out_features=1024, bias=True)
  (t_conv1): Sequential(
    (0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (t_conv2): Sequential(
    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (t_conv3): Sequential(
    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (t_conv4): Sequential(
    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
  )
)
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<h3 id="Training-on-GPU">Training on GPU<a class="anchor-link" href="#Training-on-GPU">&#182;</a></h3><p>Check if you can train on GPU. Here, we'll set this as a boolean variable <code>train_on_gpu</code>. Later, you'll be responsible for making sure that</p>
<blockquote><ul>
<li>Models,</li>
<li>Model inputs, and</li>
<li>Loss function arguments</li>
</ul>
</blockquote>
<p>Are moved to GPU, where appropriate.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">torch</span>

<span class="c1"># Check for a GPU</span>
<span class="n">train_on_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">train_on_gpu</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;No GPU found. Please use a GPU to train your neural network.&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training on GPU!&#39;</span><span class="p">)</span>
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<pre>Training on GPU!
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<hr>
<h2 id="Discriminator-and-Generator-Losses">Discriminator and Generator Losses<a class="anchor-link" href="#Discriminator-and-Generator-Losses">&#182;</a></h2><p>Now we need to calculate the losses for both types of adversarial networks.</p>
<h3 id="Discriminator-Losses">Discriminator Losses<a class="anchor-link" href="#Discriminator-Losses">&#182;</a></h3><blockquote><ul>
<li>For the discriminator, the total loss is the sum of the losses for real and fake images, <code>d_loss = d_real_loss + d_fake_loss</code>. </li>
<li>Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.</li>
</ul>
</blockquote>
<h3 id="Generator-Loss">Generator Loss<a class="anchor-link" href="#Generator-Loss">&#182;</a></h3><p>The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to <em>think</em> its generated images are <em>real</em>.</p>
<h4 id="Exercise:-Complete-real-and-fake-loss-functions">Exercise: Complete real and fake loss functions<a class="anchor-link" href="#Exercise:-Complete-real-and-fake-loss-functions">&#182;</a></h4><p><strong>You may choose to use either cross entropy or a least squares error loss to complete the following <code>real_loss</code> and <code>fake_loss</code> functions.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">real_loss</span><span class="p">(</span><span class="n">D_out</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;Calculates how close discriminator outputs are to being real.</span>
<span class="sd">       param, D_out: discriminator logits</span>
<span class="sd">       return: real loss&#39;&#39;&#39;</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">D_out</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
    
    <span class="c1"># move labels to GPU if available     </span>
    <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
        <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    
    <span class="c1"># binary cross entropy with logits loss</span>
    <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
    
    <span class="c1"># calculate loss</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">D_out</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">labels</span><span class="p">)</span>    
    
    <span class="k">return</span> <span class="n">loss</span>

<span class="k">def</span> <span class="nf">fake_loss</span><span class="p">(</span><span class="n">D_out</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;Calculates how close discriminator outputs are to being fake.</span>
<span class="sd">       param, D_out: discriminator logits</span>
<span class="sd">       return: fake loss&#39;&#39;&#39;</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="n">D_out</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> <span class="c1"># fake labels = 0</span>
    
    <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
        <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    
    <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
    <span class="c1"># calculate loss</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">D_out</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">labels</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">loss</span>
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<h2 id="Optimizers">Optimizers<a class="anchor-link" href="#Optimizers">&#182;</a></h2><h4 id="Exercise:-Define-optimizers-for-your-Discriminator-(D)-and-Generator-(G)">Exercise: Define optimizers for your Discriminator (D) and Generator (G)<a class="anchor-link" href="#Exercise:-Define-optimizers-for-your-Discriminator-(D)-and-Generator-(G)">&#182;</a></h4><p>Define optimizers for your models with appropriate hyperparameters.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>

<span class="c1"># Hyperparameters</span>
<span class="n">lr</span> <span class="o">=</span> <span class="mf">0.0002</span>
<span class="n">beta1</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">beta2</span> <span class="o">=</span> <span class="mf">0.999</span>

<span class="c1"># Create optimizers for the discriminator D and generator G</span>
<span class="n">d_optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">D</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="p">,</span> <span class="p">[</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="p">])</span> 
<span class="n">g_optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="p">,</span> <span class="p">[</span><span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span><span class="p">])</span>
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<h2 id="Training">Training<a class="anchor-link" href="#Training">&#182;</a></h2><p>Training will involve alternating between training the discriminator and the generator. You'll use your functions <code>real_loss</code> and <code>fake_loss</code> to help you calculate the discriminator losses.</p>
<ul>
<li>You should train the discriminator by alternating on real and fake images</li>
<li>Then the generator, which tries to trick the discriminator and should have an opposing loss function</li>
</ul>
<h4 id="Saving-Samples">Saving Samples<a class="anchor-link" href="#Saving-Samples">&#182;</a></h4><p>You've been given some code to print out some loss statistics and save some generated "fake" samples.</p>

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<h4 id="Exercise:-Complete-the-training-function">Exercise: Complete the training function<a class="anchor-link" href="#Exercise:-Complete-the-training-function">&#182;</a></h4><p>Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">print_every</span><span class="o">=</span><span class="mi">50</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;Trains adversarial networks for some number of epochs</span>
<span class="sd">       param, D: the discriminator network</span>
<span class="sd">       param, G: the generator network</span>
<span class="sd">       param, n_epochs: number of epochs to train for</span>
<span class="sd">       param, print_every: when to print and record the models&#39; losses</span>
<span class="sd">       return: D and G losses&#39;&#39;&#39;</span>
    
    <span class="c1"># move models to GPU</span>
    <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
        <span class="n">D</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="n">G</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="c1"># keep track of loss and generated, &quot;fake&quot; samples</span>
    <span class="n">samples</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="c1"># Get some fixed data for sampling. These are images that are held</span>
    <span class="c1"># constant throughout training, and allow us to inspect the model&#39;s performance</span>
    <span class="n">sample_size</span><span class="o">=</span><span class="mi">16</span>
    <span class="n">fixed_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</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">size</span><span class="o">=</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="n">z_size</span><span class="p">))</span>
    <span class="n">fixed_z</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">fixed_z</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
    <span class="c1"># move z to GPU if available</span>
    <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
        <span class="n">fixed_z</span> <span class="o">=</span> <span class="n">fixed_z</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

    <span class="c1"># epoch training loop</span>
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>

        <span class="c1"># batch training loop</span>
        <span class="k">for</span> <span class="n">batch_i</span><span class="p">,</span> <span class="p">(</span><span class="n">real_images</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">celeba_train_loader</span><span class="p">):</span>

            <span class="n">batch_size</span> <span class="o">=</span> <span class="n">real_images</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">real_images</span> <span class="o">=</span> <span class="n">scale</span><span class="p">(</span><span class="n">real_images</span><span class="p">)</span>

            <span class="c1"># ===============================================</span>
            <span class="c1">#         YOUR CODE HERE: TRAIN THE NETWORKS</span>
            <span class="c1"># ===============================================</span>
            
            <span class="n">d_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            
            <span class="c1"># 1. Train the discriminator on real and fake images</span>
            <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
                <span class="n">real_images</span> <span class="o">=</span> <span class="n">real_images</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
                
            <span class="n">D_real</span> <span class="o">=</span> <span class="n">D</span><span class="p">(</span><span class="n">real_images</span><span class="p">)</span>
            <span class="n">d_real_loss</span> <span class="o">=</span> <span class="n">real_loss</span><span class="p">(</span><span class="n">D_real</span><span class="p">)</span>

            <span class="c1"># Generate fake images</span>
            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</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">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">z_size</span><span class="p">))</span>
            <span class="n">z</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">z</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
            
            <span class="c1"># Move to GPU (if available)</span>
            <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
                <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

            <span class="n">fake_images</span> <span class="o">=</span> <span class="n">G</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
            
            <span class="c1"># Compute the discriminator losses on fake images</span>
            <span class="n">D_fake</span> <span class="o">=</span> <span class="n">D</span><span class="p">(</span><span class="n">fake_images</span><span class="p">)</span>
            <span class="n">d_fake_loss</span> <span class="o">=</span> <span class="n">fake_loss</span><span class="p">(</span><span class="n">D_fake</span><span class="p">)</span>
            
            <span class="c1"># Add up losses and perform backprop</span>
            <span class="n">d_loss</span> <span class="o">=</span> <span class="n">d_real_loss</span> <span class="o">+</span> <span class="n">d_fake_loss</span>
            <span class="n">d_loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            <span class="n">d_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            
            <span class="c1"># 2. Train the generator with an adversarial loss</span>
            <span class="n">g_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            
            <span class="c1"># Generate fake images</span>
            <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</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">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">z_size</span><span class="p">))</span>
            <span class="n">z</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">z</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
            
            <span class="c1"># Move to GPU (if available)</span>
            <span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
                <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

            <span class="n">fake_images</span> <span class="o">=</span> <span class="n">G</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
            
            <span class="c1"># Compute the discriminator losses on fake images</span>
            <span class="c1"># using flipped labels</span>
            <span class="n">D_fake</span> <span class="o">=</span> <span class="n">D</span><span class="p">(</span><span class="n">fake_images</span><span class="p">)</span>
            <span class="n">g_loss</span> <span class="o">=</span> <span class="n">real_loss</span><span class="p">(</span><span class="n">D_fake</span><span class="p">)</span> <span class="c1"># using real_loss to flip labels</span>
            
            <span class="c1"># Perform backprop</span>
            <span class="n">g_loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            <span class="n">g_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>            
            
            <span class="c1"># ===============================================</span>
            <span class="c1">#              END OF YOUR CODE</span>
            <span class="c1"># ===============================================</span>

            <span class="c1"># Print some loss stats</span>
            <span class="k">if</span> <span class="n">batch_i</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="c1"># append discriminator loss and generator loss</span>
                <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">d_loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">g_loss</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
                <span class="c1"># print discriminator and generator loss</span>
                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch [</span><span class="si">{:5d}</span><span class="s1">/</span><span class="si">{:5d}</span><span class="s1">] | d_loss: </span><span class="si">{:6.4f}</span><span class="s1"> | g_loss: </span><span class="si">{:6.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                        <span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">d_loss</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">g_loss</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>


        <span class="c1">## AFTER EACH EPOCH##    </span>
        <span class="c1"># this code assumes your generator is named G, feel free to change the name</span>
        <span class="c1"># generate and save sample, fake images</span>
        <span class="n">G</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> <span class="c1"># for generating samples</span>
        <span class="n">samples_z</span> <span class="o">=</span> <span class="n">G</span><span class="p">(</span><span class="n">fixed_z</span><span class="p">)</span>
        <span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples_z</span><span class="p">)</span>
        <span class="n">G</span><span class="o">.</span><span class="n">train</span><span class="p">()</span> <span class="c1"># back to training mode</span>

    <span class="c1"># Save training generator samples</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_samples.pkl&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">pkl</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
    
    <span class="c1"># finally return losses</span>
    <span class="k">return</span> <span class="n">losses</span>
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<p>Set your number of training epochs and train your GAN!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># set number of epochs </span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">50</span>


<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># call training function</span>
<span class="n">losses</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">D</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">print_every</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>Epoch [    1/   50] | d_loss: 1.4148 | g_loss: 1.0176
Epoch [    1/   50] | d_loss: 0.1953 | g_loss: 3.7043
Epoch [    1/   50] | d_loss: 0.6218 | g_loss: 1.8758
Epoch [    1/   50] | d_loss: 0.5708 | g_loss: 2.4974
Epoch [    1/   50] | d_loss: 0.6703 | g_loss: 3.4005
Epoch [    1/   50] | d_loss: 0.8556 | g_loss: 4.8611
Epoch [    1/   50] | d_loss: 0.8385 | g_loss: 4.4242
Epoch [    1/   50] | d_loss: 0.5338 | g_loss: 2.8490
Epoch [    1/   50] | d_loss: 0.7188 | g_loss: 1.9161
Epoch [    1/   50] | d_loss: 0.8726 | g_loss: 1.6696
Epoch [    1/   50] | d_loss: 0.6685 | g_loss: 2.7708
Epoch [    1/   50] | d_loss: 0.6828 | g_loss: 2.9503
Epoch [    1/   50] | d_loss: 0.8130 | g_loss: 3.0600
Epoch [    1/   50] | d_loss: 0.8680 | g_loss: 2.2984
Epoch [    1/   50] | d_loss: 0.6446 | g_loss: 1.9800
Epoch [    2/   50] | d_loss: 0.5778 | g_loss: 1.2809
Epoch [    2/   50] | d_loss: 0.7198 | g_loss: 1.4475
Epoch [    2/   50] | d_loss: 1.1422 | g_loss: 0.7942
Epoch [    2/   50] | d_loss: 0.7123 | g_loss: 1.8956
Epoch [    2/   50] | d_loss: 1.0027 | g_loss: 1.6634
Epoch [    2/   50] | d_loss: 0.6466 | g_loss: 2.6912
Epoch [    2/   50] | d_loss: 1.0688 | g_loss: 2.9961
Epoch [    2/   50] | d_loss: 0.4221 | g_loss: 2.7549
Epoch [    2/   50] | d_loss: 1.4504 | g_loss: 4.4273
Epoch [    2/   50] | d_loss: 0.7773 | g_loss: 2.8154
Epoch [    2/   50] | d_loss: 0.7803 | g_loss: 3.7319
Epoch [    2/   50] | d_loss: 0.6033 | g_loss: 1.9032
Epoch [    2/   50] | d_loss: 0.7737 | g_loss: 2.6833
Epoch [    2/   50] | d_loss: 0.5186 | g_loss: 1.5526
Epoch [    2/   50] | d_loss: 0.6505 | g_loss: 2.3338
Epoch [    3/   50] | d_loss: 0.6135 | g_loss: 3.1318
Epoch [    3/   50] | d_loss: 0.5321 | g_loss: 1.5948
Epoch [    3/   50] | d_loss: 0.4792 | g_loss: 2.2747
Epoch [    3/   50] | d_loss: 0.6680 | g_loss: 2.6908
Epoch [    3/   50] | d_loss: 0.4207 | g_loss: 1.7779
Epoch [    3/   50] | d_loss: 0.7360 | g_loss: 2.6095
Epoch [    3/   50] | d_loss: 0.7064 | g_loss: 1.7882
Epoch [    3/   50] | d_loss: 0.4645 | g_loss: 3.6242
Epoch [    3/   50] | d_loss: 0.5372 | g_loss: 1.5882
Epoch [    3/   50] | d_loss: 0.7313 | g_loss: 1.3482
Epoch [    3/   50] | d_loss: 0.8901 | g_loss: 1.1576
Epoch [    3/   50] | d_loss: 0.4081 | g_loss: 4.0377
Epoch [    3/   50] | d_loss: 1.2458 | g_loss: 4.0868
Epoch [    3/   50] | d_loss: 0.3967 | g_loss: 1.8471
Epoch [    3/   50] | d_loss: 1.0264 | g_loss: 3.1438
Epoch [    4/   50] | d_loss: 0.7264 | g_loss: 3.1912
Epoch [    4/   50] | d_loss: 0.5173 | g_loss: 4.0621
Epoch [    4/   50] | d_loss: 0.5121 | g_loss: 2.2306
Epoch [    4/   50] | d_loss: 0.5900 | g_loss: 2.5074
Epoch [    4/   50] | d_loss: 0.4789 | g_loss: 2.4600
Epoch [    4/   50] | d_loss: 0.3861 | g_loss: 2.5525
Epoch [    4/   50] | d_loss: 0.4375 | g_loss: 2.2045
Epoch [    4/   50] | d_loss: 1.0183 | g_loss: 2.1949
Epoch [    4/   50] | d_loss: 0.4789 | g_loss: 3.7079
Epoch [    4/   50] | d_loss: 0.9414 | g_loss: 2.9612
Epoch [    4/   50] | d_loss: 0.4425 | g_loss: 2.7184
Epoch [    4/   50] | d_loss: 1.7322 | g_loss: 5.7402
Epoch [    4/   50] | d_loss: 0.8771 | g_loss: 3.6603
Epoch [    4/   50] | d_loss: 0.6248 | g_loss: 3.3645
Epoch [    4/   50] | d_loss: 0.4445 | g_loss: 2.4313
Epoch [    5/   50] | d_loss: 1.3672 | g_loss: 4.6709
Epoch [    5/   50] | d_loss: 0.4185 | g_loss: 2.5973
Epoch [    5/   50] | d_loss: 0.3523 | g_loss: 3.4042
Epoch [    5/   50] | d_loss: 0.4472 | g_loss: 2.3785
Epoch [    5/   50] | d_loss: 0.3786 | g_loss: 2.5763
Epoch [    5/   50] | d_loss: 0.2649 | g_loss: 3.3502
Epoch [    5/   50] | d_loss: 0.5173 | g_loss: 3.6681
Epoch [    5/   50] | d_loss: 1.1255 | g_loss: 1.6432
Epoch [    5/   50] | d_loss: 0.5230 | g_loss: 1.7538
Epoch [    5/   50] | d_loss: 0.9735 | g_loss: 0.4630
Epoch [    5/   50] | d_loss: 0.2746 | g_loss: 1.9337
Epoch [    5/   50] | d_loss: 3.9789 | g_loss: 11.0825
Epoch [    5/   50] | d_loss: 0.5172 | g_loss: 4.4793
Epoch [    5/   50] | d_loss: 0.1959 | g_loss: 3.5421
Epoch [    5/   50] | d_loss: 0.4932 | g_loss: 3.6653
Epoch [    6/   50] | d_loss: 0.9548 | g_loss: 6.1211
Epoch [    6/   50] | d_loss: 0.2612 | g_loss: 4.1490
Epoch [    6/   50] | d_loss: 0.9531 | g_loss: 0.6516
Epoch [    6/   50] | d_loss: 0.3661 | g_loss: 3.4260
Epoch [    6/   50] | d_loss: 0.3814 | g_loss: 2.2141
Epoch [    6/   50] | d_loss: 0.3585 | g_loss: 3.5555
Epoch [    6/   50] | d_loss: 0.3957 | g_loss: 3.0837
Epoch [    6/   50] | d_loss: 0.7580 | g_loss: 1.8808
Epoch [    6/   50] | d_loss: 0.2021 | g_loss: 3.5628
Epoch [    6/   50] | d_loss: 0.2346 | g_loss: 3.3356
Epoch [    6/   50] | d_loss: 0.1797 | g_loss: 3.6135
Epoch [    6/   50] | d_loss: 0.5704 | g_loss: 4.1033
Epoch [    6/   50] | d_loss: 0.7981 | g_loss: 4.1097
Epoch [    6/   50] | d_loss: 0.2862 | g_loss: 5.2801
Epoch [    6/   50] | d_loss: 0.4224 | g_loss: 4.6652
Epoch [    7/   50] | d_loss: 0.2393 | g_loss: 3.5371
Epoch [    7/   50] | d_loss: 0.3324 | g_loss: 3.4557
Epoch [    7/   50] | d_loss: 0.2225 | g_loss: 3.4268
Epoch [    7/   50] | d_loss: 0.8334 | g_loss: 3.2331
Epoch [    7/   50] | d_loss: 0.1231 | g_loss: 3.7530
Epoch [    7/   50] | d_loss: 0.2775 | g_loss: 4.8514
Epoch [    7/   50] | d_loss: 0.1799 | g_loss: 4.0393
Epoch [    7/   50] | d_loss: 0.3038 | g_loss: 3.2867
Epoch [    7/   50] | d_loss: 0.1862 | g_loss: 2.5189
Epoch [    7/   50] | d_loss: 0.1478 | g_loss: 3.3485
Epoch [    7/   50] | d_loss: 0.6216 | g_loss: 4.8579
Epoch [    7/   50] | d_loss: 0.6274 | g_loss: 3.0173
Epoch [    7/   50] | d_loss: 0.2961 | g_loss: 3.9256
Epoch [    7/   50] | d_loss: 0.1462 | g_loss: 3.1264
Epoch [    7/   50] | d_loss: 0.9217 | g_loss: 1.7803
Epoch [    8/   50] | d_loss: 0.4059 | g_loss: 6.0070
Epoch [    8/   50] | d_loss: 0.1431 | g_loss: 3.7297
Epoch [    8/   50] | d_loss: 0.5770 | g_loss: 3.2814
Epoch [    8/   50] | d_loss: 0.1184 | g_loss: 3.7089
Epoch [    8/   50] | d_loss: 0.2037 | g_loss: 5.0083
Epoch [    8/   50] | d_loss: 0.1019 | g_loss: 4.4226
Epoch [    8/   50] | d_loss: 0.7131 | g_loss: 2.4318
Epoch [    8/   50] | d_loss: 0.1988 | g_loss: 3.0255
Epoch [    8/   50] | d_loss: 0.7143 | g_loss: 5.4883
Epoch [    8/   50] | d_loss: 0.2258 | g_loss: 2.9520
Epoch [    8/   50] | d_loss: 1.1124 | g_loss: 3.7858
Epoch [    8/   50] | d_loss: 0.4808 | g_loss: 1.9321
Epoch [    8/   50] | d_loss: 0.2528 | g_loss: 3.6484
Epoch [    8/   50] | d_loss: 0.1978 | g_loss: 5.4419
Epoch [    8/   50] | d_loss: 0.1918 | g_loss: 8.5698
Epoch [    9/   50] | d_loss: 0.3694 | g_loss: 5.4780
Epoch [    9/   50] | d_loss: 0.6264 | g_loss: 4.8655
Epoch [    9/   50] | d_loss: 1.2555 | g_loss: 1.6396
Epoch [    9/   50] | d_loss: 0.8626 | g_loss: 8.1301
Epoch [    9/   50] | d_loss: 0.5488 | g_loss: 1.7365
Epoch [    9/   50] | d_loss: 0.1997 | g_loss: 6.3045
Epoch [    9/   50] | d_loss: 0.1410 | g_loss: 3.7984
Epoch [    9/   50] | d_loss: 0.9585 | g_loss: 2.7841
Epoch [    9/   50] | d_loss: 0.0419 | g_loss: 6.3798
Epoch [    9/   50] | d_loss: 0.1738 | g_loss: 2.4139
Epoch [    9/   50] | d_loss: 0.2797 | g_loss: 3.2730
Epoch [    9/   50] | d_loss: 0.1465 | g_loss: 3.6809
Epoch [    9/   50] | d_loss: 0.1933 | g_loss: 2.7808
Epoch [    9/   50] | d_loss: 0.1237 | g_loss: 4.6640
Epoch [    9/   50] | d_loss: 0.2102 | g_loss: 5.4728
Epoch [   10/   50] | d_loss: 0.0824 | g_loss: 4.1817
Epoch [   10/   50] | d_loss: 0.1961 | g_loss: 2.9583
Epoch [   10/   50] | d_loss: 0.1181 | g_loss: 5.4473
Epoch [   10/   50] | d_loss: 0.2561 | g_loss: 4.2976
Epoch [   10/   50] | d_loss: 0.2831 | g_loss: 3.5903
Epoch [   10/   50] | d_loss: 0.3320 | g_loss: 5.8755
Epoch [   10/   50] | d_loss: 0.3615 | g_loss: 5.8265
Epoch [   10/   50] | d_loss: 0.1118 | g_loss: 4.7592
Epoch [   10/   50] | d_loss: 0.1691 | g_loss: 3.2193
Epoch [   10/   50] | d_loss: 0.2663 | g_loss: 3.4728
Epoch [   10/   50] | d_loss: 0.3319 | g_loss: 3.0372
Epoch [   10/   50] | d_loss: 0.3794 | g_loss: 4.8532
Epoch [   10/   50] | d_loss: 0.3289 | g_loss: 3.4638
Epoch [   10/   50] | d_loss: 0.2926 | g_loss: 2.3421
Epoch [   10/   50] | d_loss: 0.3265 | g_loss: 3.5550
Epoch [   11/   50] | d_loss: 0.2754 | g_loss: 3.4073
Epoch [   11/   50] | d_loss: 0.4813 | g_loss: 1.3638
Epoch [   11/   50] | d_loss: 0.1599 | g_loss: 5.6643
Epoch [   11/   50] | d_loss: 0.6704 | g_loss: 5.3729
Epoch [   11/   50] | d_loss: 0.1612 | g_loss: 5.5731
Epoch [   11/   50] | d_loss: 0.5049 | g_loss: 5.4150
Epoch [   11/   50] | d_loss: 0.0427 | g_loss: 3.4632
Epoch [   11/   50] | d_loss: 0.5568 | g_loss: 7.3570
Epoch [   11/   50] | d_loss: 0.5572 | g_loss: 6.3128
Epoch [   11/   50] | d_loss: 0.2575 | g_loss: 4.9169
Epoch [   11/   50] | d_loss: 1.9037 | g_loss: 7.2122
Epoch [   11/   50] | d_loss: 0.0771 | g_loss: 5.1335
Epoch [   11/   50] | d_loss: 0.1056 | g_loss: 2.5598
Epoch [   11/   50] | d_loss: 0.1786 | g_loss: 5.9410
Epoch [   11/   50] | d_loss: 0.1204 | g_loss: 4.6992
Epoch [   12/   50] | d_loss: 0.1291 | g_loss: 4.5283
Epoch [   12/   50] | d_loss: 0.6622 | g_loss: 0.6170
Epoch [   12/   50] | d_loss: 0.3236 | g_loss: 4.9360
Epoch [   12/   50] | d_loss: 0.0540 | g_loss: 6.2283
Epoch [   12/   50] | d_loss: 0.1600 | g_loss: 3.3392
Epoch [   12/   50] | d_loss: 0.0492 | g_loss: 2.9345
Epoch [   12/   50] | d_loss: 0.1127 | g_loss: 5.3619
Epoch [   12/   50] | d_loss: 0.0575 | g_loss: 5.4037
Epoch [   12/   50] | d_loss: 0.3016 | g_loss: 3.0788
Epoch [   12/   50] | d_loss: 0.1051 | g_loss: 5.1348
Epoch [   12/   50] | d_loss: 0.2703 | g_loss: 5.9777
Epoch [   12/   50] | d_loss: 0.1480 | g_loss: 2.6599
Epoch [   12/   50] | d_loss: 0.3088 | g_loss: 3.2505
Epoch [   12/   50] | d_loss: 0.0801 | g_loss: 6.1399
Epoch [   12/   50] | d_loss: 0.1709 | g_loss: 3.7704
Epoch [   13/   50] | d_loss: 0.1909 | g_loss: 2.2372
Epoch [   13/   50] | d_loss: 0.1945 | g_loss: 2.6416
Epoch [   13/   50] | d_loss: 0.1990 | g_loss: 4.2087
Epoch [   13/   50] | d_loss: 0.1119 | g_loss: 4.9951
Epoch [   13/   50] | d_loss: 0.1300 | g_loss: 5.0927
Epoch [   13/   50] | d_loss: 0.1037 | g_loss: 5.5174
Epoch [   13/   50] | d_loss: 0.7496 | g_loss: 2.1536
Epoch [   13/   50] | d_loss: 0.0560 | g_loss: 3.4937
Epoch [   13/   50] | d_loss: 0.1887 | g_loss: 7.5570
Epoch [   13/   50] | d_loss: 0.1308 | g_loss: 6.8548
Epoch [   13/   50] | d_loss: 0.0762 | g_loss: 5.1874
Epoch [   13/   50] | d_loss: 0.8898 | g_loss: 2.7450
Epoch [   13/   50] | d_loss: 0.0765 | g_loss: 2.3700
Epoch [   13/   50] | d_loss: 0.4653 | g_loss: 6.3018
Epoch [   13/   50] | d_loss: 0.0546 | g_loss: 5.9833
Epoch [   14/   50] | d_loss: 0.6362 | g_loss: 5.8583
Epoch [   14/   50] | d_loss: 0.0951 | g_loss: 4.9599
Epoch [   14/   50] | d_loss: 0.1542 | g_loss: 4.8660
Epoch [   14/   50] | d_loss: 0.1591 | g_loss: 4.4914
Epoch [   14/   50] | d_loss: 0.3884 | g_loss: 1.2104
Epoch [   14/   50] | d_loss: 0.0720 | g_loss: 3.8526
Epoch [   14/   50] | d_loss: 0.6642 | g_loss: 7.2965
Epoch [   14/   50] | d_loss: 0.2075 | g_loss: 4.2558
Epoch [   14/   50] | d_loss: 0.0653 | g_loss: 5.0373
Epoch [   14/   50] | d_loss: 0.3207 | g_loss: 1.4548
Epoch [   14/   50] | d_loss: 0.4520 | g_loss: 4.0231
Epoch [   14/   50] | d_loss: 1.1296 | g_loss: 7.7252
Epoch [   14/   50] | d_loss: 0.0498 | g_loss: 8.1454
Epoch [   14/   50] | d_loss: 0.0478 | g_loss: 4.4196
Epoch [   14/   50] | d_loss: 0.1126 | g_loss: 4.9857
Epoch [   15/   50] | d_loss: 0.0507 | g_loss: 4.8143
Epoch [   15/   50] | d_loss: 0.2405 | g_loss: 2.9568
Epoch [   15/   50] | d_loss: 0.1267 | g_loss: 4.1820
Epoch [   15/   50] | d_loss: 0.0674 | g_loss: 4.5916
Epoch [   15/   50] | d_loss: 0.5066 | g_loss: 1.4109
Epoch [   15/   50] | d_loss: 0.1177 | g_loss: 4.1583
Epoch [   15/   50] | d_loss: 0.1350 | g_loss: 3.3323
Epoch [   15/   50] | d_loss: 0.1579 | g_loss: 4.7767
Epoch [   15/   50] | d_loss: 0.2946 | g_loss: 3.6061
Epoch [   15/   50] | d_loss: 0.0789 | g_loss: 4.5151
Epoch [   15/   50] | d_loss: 0.0834 | g_loss: 3.5478
Epoch [   15/   50] | d_loss: 0.1249 | g_loss: 4.0760
Epoch [   15/   50] | d_loss: 1.6857 | g_loss: 5.2660
Epoch [   15/   50] | d_loss: 0.2833 | g_loss: 4.7322
Epoch [   15/   50] | d_loss: 0.1802 | g_loss: 5.3190
Epoch [   16/   50] | d_loss: 0.2091 | g_loss: 6.4284
Epoch [   16/   50] | d_loss: 0.1380 | g_loss: 3.7277
Epoch [   16/   50] | d_loss: 0.1295 | g_loss: 4.3064
Epoch [   16/   50] | d_loss: 0.0446 | g_loss: 6.1617
Epoch [   16/   50] | d_loss: 0.1781 | g_loss: 6.0101
Epoch [   16/   50] | d_loss: 0.0488 | g_loss: 3.9513
Epoch [   16/   50] | d_loss: 0.1592 | g_loss: 5.2473
Epoch [   16/   50] | d_loss: 0.2309 | g_loss: 3.8161
Epoch [   16/   50] | d_loss: 0.2014 | g_loss: 6.6447
Epoch [   16/   50] | d_loss: 0.0762 | g_loss: 2.6844
Epoch [   16/   50] | d_loss: 0.0760 | g_loss: 3.5518
Epoch [   16/   50] | d_loss: 0.3220 | g_loss: 3.4244
Epoch [   16/   50] | d_loss: 0.2935 | g_loss: 4.3467
Epoch [   16/   50] | d_loss: 0.2777 | g_loss: 4.7610
Epoch [   16/   50] | d_loss: 0.1447 | g_loss: 5.6390
Epoch [   17/   50] | d_loss: 0.0746 | g_loss: 4.8598
Epoch [   17/   50] | d_loss: 0.0711 | g_loss: 4.5737
Epoch [   17/   50] | d_loss: 0.1524 | g_loss: 4.3460
Epoch [   17/   50] | d_loss: 0.3614 | g_loss: 3.5102
Epoch [   17/   50] | d_loss: 0.0888 | g_loss: 5.2096
Epoch [   17/   50] | d_loss: 0.2357 | g_loss: 6.3845
Epoch [   17/   50] | d_loss: 0.1791 | g_loss: 4.7134
Epoch [   17/   50] | d_loss: 2.8421 | g_loss: 3.0314
Epoch [   17/   50] | d_loss: 0.0472 | g_loss: 2.4568
Epoch [   17/   50] | d_loss: 0.0546 | g_loss: 4.3168
Epoch [   17/   50] | d_loss: 0.2236 | g_loss: 4.3790
Epoch [   17/   50] | d_loss: 0.2310 | g_loss: 4.2293
Epoch [   17/   50] | d_loss: 0.0691 | g_loss: 4.8203
Epoch [   17/   50] | d_loss: 0.0817 | g_loss: 5.6937
Epoch [   17/   50] | d_loss: 0.6317 | g_loss: 3.6522
Epoch [   18/   50] | d_loss: 0.1677 | g_loss: 4.6123
Epoch [   18/   50] | d_loss: 0.2089 | g_loss: 5.0735
Epoch [   18/   50] | d_loss: 0.0854 | g_loss: 8.0484
Epoch [   18/   50] | d_loss: 0.0506 | g_loss: 2.8731
Epoch [   18/   50] | d_loss: 0.1317 | g_loss: 5.6409
Epoch [   18/   50] | d_loss: 0.0548 | g_loss: 4.7273
Epoch [   18/   50] | d_loss: 0.0538 | g_loss: 4.2738
Epoch [   18/   50] | d_loss: 0.0321 | g_loss: 3.0114
Epoch [   18/   50] | d_loss: 0.1276 | g_loss: 7.2920
Epoch [   18/   50] | d_loss: 0.0534 | g_loss: 2.3237
Epoch [   18/   50] | d_loss: 0.0532 | g_loss: 4.2936
Epoch [   18/   50] | d_loss: 0.0974 | g_loss: 5.2912
Epoch [   18/   50] | d_loss: 0.9749 | g_loss: 0.1883
Epoch [   18/   50] | d_loss: 0.0643 | g_loss: 4.9398
Epoch [   18/   50] | d_loss: 0.1487 | g_loss: 7.5875
Epoch [   19/   50] | d_loss: 0.3921 | g_loss: 8.0073
Epoch [   19/   50] | d_loss: 0.0308 | g_loss: 5.8776
Epoch [   19/   50] | d_loss: 0.0452 | g_loss: 4.2749
Epoch [   19/   50] | d_loss: 0.0545 | g_loss: 4.3135
Epoch [   19/   50] | d_loss: 0.0423 | g_loss: 3.4958
Epoch [   19/   50] | d_loss: 0.0415 | g_loss: 5.9444
Epoch [   19/   50] | d_loss: 0.2790 | g_loss: 5.2127
Epoch [   19/   50] | d_loss: 0.2217 | g_loss: 5.8113
Epoch [   19/   50] | d_loss: 0.1061 | g_loss: 5.4092
Epoch [   19/   50] | d_loss: 0.0250 | g_loss: 4.1539
Epoch [   19/   50] | d_loss: 0.0649 | g_loss: 4.1148
Epoch [   19/   50] | d_loss: 0.2233 | g_loss: 4.5573
Epoch [   19/   50] | d_loss: 0.0319 | g_loss: 6.0160
Epoch [   19/   50] | d_loss: 0.1275 | g_loss: 3.5706
Epoch [   19/   50] | d_loss: 0.2180 | g_loss: 9.1911
Epoch [   20/   50] | d_loss: 0.0355 | g_loss: 3.0372
Epoch [   20/   50] | d_loss: 0.2527 | g_loss: 4.1565
Epoch [   20/   50] | d_loss: 0.1422 | g_loss: 2.2887
Epoch [   20/   50] | d_loss: 0.0784 | g_loss: 4.4850
Epoch [   20/   50] | d_loss: 0.1175 | g_loss: 3.9132
Epoch [   20/   50] | d_loss: 0.0329 | g_loss: 9.9647
Epoch [   20/   50] | d_loss: 0.4631 | g_loss: 3.5142
Epoch [   20/   50] | d_loss: 0.3548 | g_loss: 2.8491
Epoch [   20/   50] | d_loss: 0.3054 | g_loss: 7.4627
Epoch [   20/   50] | d_loss: 0.1776 | g_loss: 4.2289
Epoch [   20/   50] | d_loss: 0.0256 | g_loss: 4.3490
Epoch [   20/   50] | d_loss: 0.1660 | g_loss: 4.6288
Epoch [   20/   50] | d_loss: 0.2372 | g_loss: 3.2956
Epoch [   20/   50] | d_loss: 0.0745 | g_loss: 5.3094
Epoch [   20/   50] | d_loss: 0.0356 | g_loss: 4.9279
Epoch [   21/   50] | d_loss: 0.2775 | g_loss: 6.4214
Epoch [   21/   50] | d_loss: 0.0470 | g_loss: 7.1844
Epoch [   21/   50] | d_loss: 0.1733 | g_loss: 2.0913
Epoch [   21/   50] | d_loss: 0.0476 | g_loss: 5.6791
Epoch [   21/   50] | d_loss: 0.0769 | g_loss: 6.5921
Epoch [   21/   50] | d_loss: 0.2130 | g_loss: 3.2983
Epoch [   21/   50] | d_loss: 0.0680 | g_loss: 4.1886
Epoch [   21/   50] | d_loss: 0.3414 | g_loss: 3.2713
Epoch [   21/   50] | d_loss: 0.0481 | g_loss: 5.5216
Epoch [   21/   50] | d_loss: 0.0237 | g_loss: 6.9316
Epoch [   21/   50] | d_loss: 0.0772 | g_loss: 4.0402
Epoch [   21/   50] | d_loss: 0.2472 | g_loss: 5.7649
Epoch [   21/   50] | d_loss: 0.2243 | g_loss: 4.1102
Epoch [   21/   50] | d_loss: 0.1778 | g_loss: 3.7554
Epoch [   21/   50] | d_loss: 0.0550 | g_loss: 5.8715
Epoch [   22/   50] | d_loss: 0.1016 | g_loss: 5.5310
Epoch [   22/   50] | d_loss: 0.0628 | g_loss: 5.1117
Epoch [   22/   50] | d_loss: 0.0544 | g_loss: 9.7195
Epoch [   22/   50] | d_loss: 0.1720 | g_loss: 7.2094
Epoch [   22/   50] | d_loss: 0.3879 | g_loss: 7.1907
Epoch [   22/   50] | d_loss: 0.0639 | g_loss: 5.5671
Epoch [   22/   50] | d_loss: 0.0239 | g_loss: 2.5321
Epoch [   22/   50] | d_loss: 0.1469 | g_loss: 4.7818
Epoch [   22/   50] | d_loss: 0.1138 | g_loss: 3.4929
Epoch [   22/   50] | d_loss: 0.0773 | g_loss: 4.1446
Epoch [   22/   50] | d_loss: 0.0746 | g_loss: 3.9053
Epoch [   22/   50] | d_loss: 0.0647 | g_loss: 5.5606
Epoch [   22/   50] | d_loss: 0.1110 | g_loss: 5.9604
Epoch [   22/   50] | d_loss: 0.1023 | g_loss: 6.7622
Epoch [   22/   50] | d_loss: 0.1236 | g_loss: 5.1118
Epoch [   23/   50] | d_loss: 0.0615 | g_loss: 3.8606
Epoch [   23/   50] | d_loss: 0.1107 | g_loss: 4.0936
Epoch [   23/   50] | d_loss: 0.0536 | g_loss: 5.2526
Epoch [   23/   50] | d_loss: 0.1022 | g_loss: 4.4029
Epoch [   23/   50] | d_loss: 0.0404 | g_loss: 4.3518
Epoch [   23/   50] | d_loss: 0.0884 | g_loss: 4.6886
Epoch [   23/   50] | d_loss: 0.0889 | g_loss: 2.4912
Epoch [   23/   50] | d_loss: 0.0333 | g_loss: 4.8500
Epoch [   23/   50] | d_loss: 0.0396 | g_loss: 5.0832
Epoch [   23/   50] | d_loss: 0.2162 | g_loss: 3.1450
Epoch [   23/   50] | d_loss: 0.4150 | g_loss: 3.8418
Epoch [   23/   50] | d_loss: 0.0689 | g_loss: 4.7873
Epoch [   23/   50] | d_loss: 0.0149 | g_loss: 8.7158
Epoch [   23/   50] | d_loss: 0.0656 | g_loss: 6.0100
Epoch [   23/   50] | d_loss: 0.4356 | g_loss: 7.2556
Epoch [   24/   50] | d_loss: 0.0105 | g_loss: 5.1172
Epoch [   24/   50] | d_loss: 0.2658 | g_loss: 3.5732
Epoch [   24/   50] | d_loss: 0.0489 | g_loss: 4.2847
Epoch [   24/   50] | d_loss: 0.4385 | g_loss: 12.7878
Epoch [   24/   50] | d_loss: 0.1080 | g_loss: 3.3803
Epoch [   24/   50] | d_loss: 0.1883 | g_loss: 6.7671
Epoch [   24/   50] | d_loss: 0.6670 | g_loss: 0.4847
Epoch [   24/   50] | d_loss: 0.0880 | g_loss: 2.8805
Epoch [   24/   50] | d_loss: 0.1612 | g_loss: 8.2967
Epoch [   24/   50] | d_loss: 0.1014 | g_loss: 5.1812
Epoch [   24/   50] | d_loss: 0.1012 | g_loss: 6.5657
Epoch [   24/   50] | d_loss: 0.0369 | g_loss: 5.5610
Epoch [   24/   50] | d_loss: 0.0486 | g_loss: 5.7234
Epoch [   24/   50] | d_loss: 0.0318 | g_loss: 5.0097
Epoch [   24/   50] | d_loss: 0.0872 | g_loss: 3.6198
Epoch [   25/   50] | d_loss: 0.0651 | g_loss: 5.5714
Epoch [   25/   50] | d_loss: 0.0294 | g_loss: 5.0656
Epoch [   25/   50] | d_loss: 0.0513 | g_loss: 2.3810
Epoch [   25/   50] | d_loss: 0.0207 | g_loss: 3.4557
Epoch [   25/   50] | d_loss: 0.0950 | g_loss: 8.4592
Epoch [   25/   50] | d_loss: 0.0466 | g_loss: 4.6781
Epoch [   25/   50] | d_loss: 0.0331 | g_loss: 6.0726
Epoch [   25/   50] | d_loss: 1.4724 | g_loss: 7.9929
Epoch [   25/   50] | d_loss: 0.0738 | g_loss: 1.5142
Epoch [   25/   50] | d_loss: 0.0622 | g_loss: 4.2819
Epoch [   25/   50] | d_loss: 0.0065 | g_loss: 6.4319
Epoch [   25/   50] | d_loss: 0.2395 | g_loss: 5.3853
Epoch [   25/   50] | d_loss: 0.0449 | g_loss: 2.9343
Epoch [   25/   50] | d_loss: 0.2762 | g_loss: 7.4092
Epoch [   25/   50] | d_loss: 0.0442 | g_loss: 5.5932
Epoch [   26/   50] | d_loss: 0.2056 | g_loss: 3.5144
Epoch [   26/   50] | d_loss: 0.1816 | g_loss: 1.9826
Epoch [   26/   50] | d_loss: 0.0636 | g_loss: 6.5084
Epoch [   26/   50] | d_loss: 0.0130 | g_loss: 6.1351
Epoch [   26/   50] | d_loss: 2.2885 | g_loss: 3.1648
Epoch [   26/   50] | d_loss: 0.0583 | g_loss: 4.4646
Epoch [   26/   50] | d_loss: 0.0512 | g_loss: 4.4229
Epoch [   26/   50] | d_loss: 0.0745 | g_loss: 6.9831
Epoch [   26/   50] | d_loss: 0.0103 | g_loss: 7.3078
Epoch [   26/   50] | d_loss: 0.1054 | g_loss: 6.5035
Epoch [   26/   50] | d_loss: 0.0575 | g_loss: 3.1520
Epoch [   26/   50] | d_loss: 0.0326 | g_loss: 4.2817
Epoch [   26/   50] | d_loss: 0.0618 | g_loss: 4.1264
Epoch [   26/   50] | d_loss: 0.1291 | g_loss: 4.9552
Epoch [   26/   50] | d_loss: 0.0545 | g_loss: 4.5744
Epoch [   27/   50] | d_loss: 1.7450 | g_loss: 10.6118
Epoch [   27/   50] | d_loss: 0.1294 | g_loss: 4.5205
Epoch [   27/   50] | d_loss: 0.1342 | g_loss: 4.8285
Epoch [   27/   50] | d_loss: 0.1274 | g_loss: 5.7697
Epoch [   27/   50] | d_loss: 0.0152 | g_loss: 6.3873
Epoch [   27/   50] | d_loss: 0.0051 | g_loss: 7.7565
Epoch [   27/   50] | d_loss: 0.0709 | g_loss: 5.5910
Epoch [   27/   50] | d_loss: 0.1054 | g_loss: 7.0184
Epoch [   27/   50] | d_loss: 0.0932 | g_loss: 4.6580
Epoch [   27/   50] | d_loss: 0.0276 | g_loss: 4.9083
Epoch [   27/   50] | d_loss: 0.0537 | g_loss: 6.2183
Epoch [   27/   50] | d_loss: 0.0031 | g_loss: 8.1933
Epoch [   27/   50] | d_loss: 0.1026 | g_loss: 5.7255
Epoch [   27/   50] | d_loss: 0.4165 | g_loss: 1.3720
Epoch [   27/   50] | d_loss: 0.0339 | g_loss: 3.4091
Epoch [   28/   50] | d_loss: 0.4909 | g_loss: 4.3518
Epoch [   28/   50] | d_loss: 0.1125 | g_loss: 6.5259
Epoch [   28/   50] | d_loss: 0.2522 | g_loss: 4.9252
Epoch [   28/   50] | d_loss: 0.0172 | g_loss: 6.8535
Epoch [   28/   50] | d_loss: 0.0047 | g_loss: 6.3320
Epoch [   28/   50] | d_loss: 0.0360 | g_loss: 5.6653
Epoch [   28/   50] | d_loss: 0.0190 | g_loss: 8.1456
Epoch [   28/   50] | d_loss: 0.0126 | g_loss: 11.0731
Epoch [   28/   50] | d_loss: 0.0338 | g_loss: 7.1413
Epoch [   28/   50] | d_loss: 0.0242 | g_loss: 3.0937
Epoch [   28/   50] | d_loss: 0.0298 | g_loss: 5.1614
Epoch [   28/   50] | d_loss: 0.0235 | g_loss: 5.6232
Epoch [   28/   50] | d_loss: 0.1370 | g_loss: 5.0213
Epoch [   28/   50] | d_loss: 0.0330 | g_loss: 7.4550
Epoch [   28/   50] | d_loss: 0.2041 | g_loss: 6.0219
Epoch [   29/   50] | d_loss: 0.0094 | g_loss: 2.7760
Epoch [   29/   50] | d_loss: 0.0125 | g_loss: 5.0888
Epoch [   29/   50] | d_loss: 0.0520 | g_loss: 4.0828
Epoch [   29/   50] | d_loss: 0.1086 | g_loss: 6.1044
Epoch [   29/   50] | d_loss: 0.1514 | g_loss: 4.7244
Epoch [   29/   50] | d_loss: 0.0482 | g_loss: 4.0437
Epoch [   29/   50] | d_loss: 0.0625 | g_loss: 4.0506
Epoch [   29/   50] | d_loss: 0.0586 | g_loss: 4.2011
Epoch [   29/   50] | d_loss: 0.0148 | g_loss: 6.6908
Epoch [   29/   50] | d_loss: 0.0381 | g_loss: 9.9383
Epoch [   29/   50] | d_loss: 0.1507 | g_loss: 4.6249
Epoch [   29/   50] | d_loss: 0.0855 | g_loss: 8.1257
Epoch [   29/   50] | d_loss: 0.0535 | g_loss: 3.7604
Epoch [   29/   50] | d_loss: 0.0281 | g_loss: 5.6478
Epoch [   29/   50] | d_loss: 0.0348 | g_loss: 10.1874
Epoch [   30/   50] | d_loss: 0.0586 | g_loss: 5.0966
Epoch [   30/   50] | d_loss: 0.0544 | g_loss: 7.1382
Epoch [   30/   50] | d_loss: 0.3427 | g_loss: 4.3898
Epoch [   30/   50] | d_loss: 0.0910 | g_loss: 4.7405
Epoch [   30/   50] | d_loss: 0.3990 | g_loss: 7.0455
Epoch [   30/   50] | d_loss: 0.0177 | g_loss: 3.4111
Epoch [   30/   50] | d_loss: 0.0924 | g_loss: 5.1961
Epoch [   30/   50] | d_loss: 0.1014 | g_loss: 4.5324
Epoch [   30/   50] | d_loss: 0.0188 | g_loss: 6.6455
Epoch [   30/   50] | d_loss: 0.3173 | g_loss: 5.1489
Epoch [   30/   50] | d_loss: 0.0181 | g_loss: 4.1695
Epoch [   30/   50] | d_loss: 0.7680 | g_loss: 6.5611
Epoch [   30/   50] | d_loss: 0.0442 | g_loss: 5.1297
Epoch [   30/   50] | d_loss: 0.5815 | g_loss: 8.9519
Epoch [   30/   50] | d_loss: 0.0748 | g_loss: 6.9847
Epoch [   31/   50] | d_loss: 0.1913 | g_loss: 5.2218
Epoch [   31/   50] | d_loss: 0.0211 | g_loss: 3.9711
Epoch [   31/   50] | d_loss: 0.0228 | g_loss: 5.6835
Epoch [   31/   50] | d_loss: 0.5635 | g_loss: 1.0364
Epoch [   31/   50] | d_loss: 1.5228 | g_loss: 9.4814
Epoch [   31/   50] | d_loss: 0.0062 | g_loss: 5.9297
Epoch [   31/   50] | d_loss: 0.2088 | g_loss: 7.4299
Epoch [   31/   50] | d_loss: 0.0439 | g_loss: 4.9796
Epoch [   31/   50] | d_loss: 0.0213 | g_loss: 5.6862
Epoch [   31/   50] | d_loss: 0.0955 | g_loss: 7.2320
Epoch [   31/   50] | d_loss: 0.0487 | g_loss: 4.8775
Epoch [   31/   50] | d_loss: 0.0211 | g_loss: 7.2023
Epoch [   31/   50] | d_loss: 0.0549 | g_loss: 3.8862
Epoch [   31/   50] | d_loss: 0.1317 | g_loss: 6.7178
Epoch [   31/   50] | d_loss: 0.0796 | g_loss: 5.5706
Epoch [   32/   50] | d_loss: 0.4862 | g_loss: 4.6330
Epoch [   32/   50] | d_loss: 0.2285 | g_loss: 8.2367
Epoch [   32/   50] | d_loss: 0.0296 | g_loss: 5.8219
Epoch [   32/   50] | d_loss: 0.0706 | g_loss: 11.1757
Epoch [   32/   50] | d_loss: 0.1064 | g_loss: 2.6330
Epoch [   32/   50] | d_loss: 0.3113 | g_loss: 5.7124
Epoch [   32/   50] | d_loss: 0.1447 | g_loss: 3.3182
Epoch [   32/   50] | d_loss: 0.0254 | g_loss: 3.8091
Epoch [   32/   50] | d_loss: 0.0550 | g_loss: 4.2232
Epoch [   32/   50] | d_loss: 0.0638 | g_loss: 8.5611
Epoch [   32/   50] | d_loss: 0.0346 | g_loss: 3.8137
Epoch [   32/   50] | d_loss: 0.1659 | g_loss: 5.6167
Epoch [   32/   50] | d_loss: 0.0225 | g_loss: 5.3336
Epoch [   32/   50] | d_loss: 0.0456 | g_loss: 3.9903
Epoch [   32/   50] | d_loss: 0.2346 | g_loss: 4.4025
Epoch [   33/   50] | d_loss: 0.0192 | g_loss: 6.0632
Epoch [   33/   50] | d_loss: 0.0076 | g_loss: 8.5534
Epoch [   33/   50] | d_loss: 0.0517 | g_loss: 6.1636
Epoch [   33/   50] | d_loss: 0.0196 | g_loss: 7.2034
Epoch [   33/   50] | d_loss: 0.2522 | g_loss: 7.1637
Epoch [   33/   50] | d_loss: 0.0491 | g_loss: 4.1950
Epoch [   33/   50] | d_loss: 0.2585 | g_loss: 5.1423
Epoch [   33/   50] | d_loss: 0.0531 | g_loss: 7.2193
Epoch [   33/   50] | d_loss: 0.0673 | g_loss: 8.0272
Epoch [   33/   50] | d_loss: 0.2288 | g_loss: 3.5796
Epoch [   33/   50] | d_loss: 0.0395 | g_loss: 4.7613
Epoch [   33/   50] | d_loss: 0.2275 | g_loss: 4.9244
Epoch [   33/   50] | d_loss: 0.0333 | g_loss: 9.2222
Epoch [   33/   50] | d_loss: 0.1762 | g_loss: 6.3942
Epoch [   33/   50] | d_loss: 0.0787 | g_loss: 8.0124
Epoch [   34/   50] | d_loss: 0.0879 | g_loss: 5.4885
Epoch [   34/   50] | d_loss: 0.3310 | g_loss: 3.5681
Epoch [   34/   50] | d_loss: 0.0618 | g_loss: 7.7203
Epoch [   34/   50] | d_loss: 0.1677 | g_loss: 7.5120
Epoch [   34/   50] | d_loss: 0.0483 | g_loss: 4.6787
Epoch [   34/   50] | d_loss: 0.0149 | g_loss: 4.9549
Epoch [   34/   50] | d_loss: 0.0785 | g_loss: 8.5717
Epoch [   34/   50] | d_loss: 0.1769 | g_loss: 5.6500
Epoch [   34/   50] | d_loss: 0.0247 | g_loss: 6.4134
Epoch [   34/   50] | d_loss: 0.0680 | g_loss: 4.6181
Epoch [   34/   50] | d_loss: 0.0331 | g_loss: 4.6693
Epoch [   34/   50] | d_loss: 0.3550 | g_loss: 5.0835
Epoch [   34/   50] | d_loss: 0.0856 | g_loss: 3.1795
Epoch [   34/   50] | d_loss: 0.0317 | g_loss: 5.7681
Epoch [   34/   50] | d_loss: 0.0276 | g_loss: 8.6242
Epoch [   35/   50] | d_loss: 1.0840 | g_loss: 12.3714
Epoch [   35/   50] | d_loss: 0.0129 | g_loss: 1.5079
Epoch [   35/   50] | d_loss: 0.0273 | g_loss: 4.1363
Epoch [   35/   50] | d_loss: 0.1856 | g_loss: 5.2414
Epoch [   35/   50] | d_loss: 0.2245 | g_loss: 5.0121
Epoch [   35/   50] | d_loss: 0.1041 | g_loss: 4.4644
Epoch [   35/   50] | d_loss: 0.1647 | g_loss: 6.0596
Epoch [   35/   50] | d_loss: 0.0783 | g_loss: 6.5881
Epoch [   35/   50] | d_loss: 0.0814 | g_loss: 4.5237
Epoch [   35/   50] | d_loss: 0.0227 | g_loss: 5.7077
Epoch [   35/   50] | d_loss: 0.2356 | g_loss: 4.1723
Epoch [   35/   50] | d_loss: 0.0615 | g_loss: 3.0796
Epoch [   35/   50] | d_loss: 0.1034 | g_loss: 8.2147
Epoch [   35/   50] | d_loss: 0.0369 | g_loss: 4.6202
Epoch [   35/   50] | d_loss: 0.0211 | g_loss: 5.1963
Epoch [   36/   50] | d_loss: 0.0368 | g_loss: 5.7545
Epoch [   36/   50] | d_loss: 0.0399 | g_loss: 5.6783
Epoch [   36/   50] | d_loss: 0.0795 | g_loss: 5.8376
Epoch [   36/   50] | d_loss: 0.0626 | g_loss: 5.9964
Epoch [   36/   50] | d_loss: 0.0391 | g_loss: 7.6744
Epoch [   36/   50] | d_loss: 0.0747 | g_loss: 5.8607
Epoch [   36/   50] | d_loss: 0.3325 | g_loss: 6.7255
Epoch [   36/   50] | d_loss: 0.1441 | g_loss: 4.8269
Epoch [   36/   50] | d_loss: 0.0443 | g_loss: 2.5913
Epoch [   36/   50] | d_loss: 0.0007 | g_loss: 7.7679
Epoch [   36/   50] | d_loss: 0.0277 | g_loss: 6.0814
Epoch [   36/   50] | d_loss: 0.0412 | g_loss: 4.6216
Epoch [   36/   50] | d_loss: 0.0187 | g_loss: 4.6484
Epoch [   36/   50] | d_loss: 0.0749 | g_loss: 10.2956
Epoch [   36/   50] | d_loss: 0.0091 | g_loss: 3.9412
Epoch [   37/   50] | d_loss: 0.1106 | g_loss: 6.1316
Epoch [   37/   50] | d_loss: 0.0201 | g_loss: 6.8384
Epoch [   37/   50] | d_loss: 0.0436 | g_loss: 5.3594
Epoch [   37/   50] | d_loss: 0.3455 | g_loss: 1.9625
Epoch [   37/   50] | d_loss: 0.2463 | g_loss: 5.6982
Epoch [   37/   50] | d_loss: 0.0037 | g_loss: 5.3763
Epoch [   37/   50] | d_loss: 0.0118 | g_loss: 8.2163
Epoch [   37/   50] | d_loss: 0.0121 | g_loss: 10.9214
Epoch [   37/   50] | d_loss: 0.2516 | g_loss: 7.1514
Epoch [   37/   50] | d_loss: 0.1064 | g_loss: 4.9065
Epoch [   37/   50] | d_loss: 1.2999 | g_loss: 3.1461
Epoch [   37/   50] | d_loss: 0.1425 | g_loss: 4.6428
Epoch [   37/   50] | d_loss: 0.0590 | g_loss: 5.3368
Epoch [   37/   50] | d_loss: 0.0682 | g_loss: 5.5458
Epoch [   37/   50] | d_loss: 0.1129 | g_loss: 2.5772
Epoch [   38/   50] | d_loss: 0.0154 | g_loss: 6.1584
Epoch [   38/   50] | d_loss: 0.1295 | g_loss: 4.1518
Epoch [   38/   50] | d_loss: 0.0410 | g_loss: 6.3374
Epoch [   38/   50] | d_loss: 0.1849 | g_loss: 4.7494
Epoch [   38/   50] | d_loss: 0.0039 | g_loss: 5.7565
Epoch [   38/   50] | d_loss: 0.2654 | g_loss: 7.1022
Epoch [   38/   50] | d_loss: 0.0100 | g_loss: 6.3273
Epoch [   38/   50] | d_loss: 0.0065 | g_loss: 6.5926
Epoch [   38/   50] | d_loss: 0.1769 | g_loss: 6.1443
Epoch [   38/   50] | d_loss: 0.0317 | g_loss: 7.1934
Epoch [   38/   50] | d_loss: 0.0240 | g_loss: 7.6542
Epoch [   38/   50] | d_loss: 0.0557 | g_loss: 7.7005
Epoch [   38/   50] | d_loss: 0.0124 | g_loss: 7.8272
Epoch [   38/   50] | d_loss: 0.0120 | g_loss: 7.1914
Epoch [   38/   50] | d_loss: 0.0346 | g_loss: 6.2816
Epoch [   39/   50] | d_loss: 4.8643 | g_loss: 17.5054
Epoch [   39/   50] | d_loss: 0.0215 | g_loss: 5.6971
Epoch [   39/   50] | d_loss: 0.0517 | g_loss: 6.9105
Epoch [   39/   50] | d_loss: 0.2535 | g_loss: 4.4749
Epoch [   39/   50] | d_loss: 0.0192 | g_loss: 5.3186
Epoch [   39/   50] | d_loss: 0.0207 | g_loss: 6.1677
Epoch [   39/   50] | d_loss: 0.0316 | g_loss: 0.8949
Epoch [   39/   50] | d_loss: 0.0312 | g_loss: 8.7150
Epoch [   39/   50] | d_loss: 0.2073 | g_loss: 5.6292
Epoch [   39/   50] | d_loss: 0.0240 | g_loss: 5.8538
Epoch [   39/   50] | d_loss: 0.0898 | g_loss: 3.1228
Epoch [   39/   50] | d_loss: 0.0283 | g_loss: 3.1629
Epoch [   39/   50] | d_loss: 0.3063 | g_loss: 5.8792
Epoch [   39/   50] | d_loss: 0.0419 | g_loss: 4.9534
Epoch [   39/   50] | d_loss: 0.0742 | g_loss: 6.4139
Epoch [   40/   50] | d_loss: 0.0787 | g_loss: 3.3398
Epoch [   40/   50] | d_loss: 0.0391 | g_loss: 5.0417
Epoch [   40/   50] | d_loss: 0.0164 | g_loss: 7.9854
Epoch [   40/   50] | d_loss: 0.0463 | g_loss: 2.7904
Epoch [   40/   50] | d_loss: 0.0291 | g_loss: 4.5907
Epoch [   40/   50] | d_loss: 0.0669 | g_loss: 5.0835
Epoch [   40/   50] | d_loss: 0.1389 | g_loss: 3.6152
Epoch [   40/   50] | d_loss: 0.0190 | g_loss: 5.7727
Epoch [   40/   50] | d_loss: 0.0764 | g_loss: 7.1647
Epoch [   40/   50] | d_loss: 0.0475 | g_loss: 4.9887
Epoch [   40/   50] | d_loss: 0.0729 | g_loss: 7.1683
Epoch [   40/   50] | d_loss: 0.0199 | g_loss: 5.1218
Epoch [   40/   50] | d_loss: 0.0081 | g_loss: 4.0176
Epoch [   40/   50] | d_loss: 0.0343 | g_loss: 3.4433
Epoch [   40/   50] | d_loss: 0.0213 | g_loss: 5.3583
Epoch [   41/   50] | d_loss: 0.0283 | g_loss: 4.9751
Epoch [   41/   50] | d_loss: 0.0223 | g_loss: 5.1331
Epoch [   41/   50] | d_loss: 0.2067 | g_loss: 7.2245
Epoch [   41/   50] | d_loss: 0.1611 | g_loss: 9.3896
Epoch [   41/   50] | d_loss: 0.1346 | g_loss: 7.8948
Epoch [   41/   50] | d_loss: 0.0761 | g_loss: 5.6761
Epoch [   41/   50] | d_loss: 0.0024 | g_loss: 7.4360
Epoch [   41/   50] | d_loss: 0.0188 | g_loss: 6.4746
Epoch [   41/   50] | d_loss: 0.0011 | g_loss: 7.6816
Epoch [   41/   50] | d_loss: 0.1551 | g_loss: 7.0033
Epoch [   41/   50] | d_loss: 0.0186 | g_loss: 8.8224
Epoch [   41/   50] | d_loss: 0.0960 | g_loss: 7.5978
Epoch [   41/   50] | d_loss: 0.0196 | g_loss: 6.5454
Epoch [   41/   50] | d_loss: 0.0235 | g_loss: 5.5154
Epoch [   41/   50] | d_loss: 0.0103 | g_loss: 7.6242
Epoch [   42/   50] | d_loss: 0.0054 | g_loss: 8.3096
Epoch [   42/   50] | d_loss: 0.0454 | g_loss: 6.6012
Epoch [   42/   50] | d_loss: 0.0181 | g_loss: 6.8840
Epoch [   42/   50] | d_loss: 0.0106 | g_loss: 3.3208
Epoch [   42/   50] | d_loss: 0.1127 | g_loss: 6.7731
Epoch [   42/   50] | d_loss: 0.0182 | g_loss: 4.0376
Epoch [   42/   50] | d_loss: 0.0020 | g_loss: 7.2005
Epoch [   42/   50] | d_loss: 0.1362 | g_loss: 6.7246
Epoch [   42/   50] | d_loss: 0.0315 | g_loss: 5.2492
Epoch [   42/   50] | d_loss: 0.0228 | g_loss: 5.0943
Epoch [   42/   50] | d_loss: 0.0138 | g_loss: 5.7813
Epoch [   42/   50] | d_loss: 1.7661 | g_loss: 2.5162
Epoch [   42/   50] | d_loss: 0.0571 | g_loss: 5.8131
Epoch [   42/   50] | d_loss: 0.0200 | g_loss: 6.7887
Epoch [   42/   50] | d_loss: 0.0177 | g_loss: 5.0866
Epoch [   43/   50] | d_loss: 0.9186 | g_loss: 9.4418
Epoch [   43/   50] | d_loss: 0.0212 | g_loss: 8.9308
Epoch [   43/   50] | d_loss: 0.0043 | g_loss: 7.2744
Epoch [   43/   50] | d_loss: 0.1638 | g_loss: 4.4824
Epoch [   43/   50] | d_loss: 0.0617 | g_loss: 7.9328
Epoch [   43/   50] | d_loss: 0.0218 | g_loss: 6.5254
Epoch [   43/   50] | d_loss: 0.0167 | g_loss: 8.2943
Epoch [   43/   50] | d_loss: 0.1922 | g_loss: 9.1438
Epoch [   43/   50] | d_loss: 0.0135 | g_loss: 4.2057
Epoch [   43/   50] | d_loss: 0.0326 | g_loss: 7.1650
Epoch [   43/   50] | d_loss: 0.2017 | g_loss: 2.6858
Epoch [   43/   50] | d_loss: 0.0213 | g_loss: 7.2672
Epoch [   43/   50] | d_loss: 0.0057 | g_loss: 2.1858
Epoch [   43/   50] | d_loss: 0.0660 | g_loss: 4.1588
Epoch [   43/   50] | d_loss: 0.0332 | g_loss: 3.2071
Epoch [   44/   50] | d_loss: 0.1304 | g_loss: 5.5965
Epoch [   44/   50] | d_loss: 0.0091 | g_loss: 6.4694
Epoch [   44/   50] | d_loss: 0.0489 | g_loss: 5.3884
Epoch [   44/   50] | d_loss: 0.0645 | g_loss: 5.9923
Epoch [   44/   50] | d_loss: 0.0130 | g_loss: 5.8193
Epoch [   44/   50] | d_loss: 0.0402 | g_loss: 6.6376
Epoch [   44/   50] | d_loss: 0.0135 | g_loss: 5.8237
Epoch [   44/   50] | d_loss: 0.1876 | g_loss: 3.2394
Epoch [   44/   50] | d_loss: 0.0294 | g_loss: 5.8078
Epoch [   44/   50] | d_loss: 0.0110 | g_loss: 4.3825
Epoch [   44/   50] | d_loss: 0.0489 | g_loss: 7.4259
Epoch [   44/   50] | d_loss: 0.0094 | g_loss: 4.5239
Epoch [   44/   50] | d_loss: 0.3307 | g_loss: 6.2750
Epoch [   44/   50] | d_loss: 0.0136 | g_loss: 10.0200
Epoch [   44/   50] | d_loss: 0.0397 | g_loss: 6.7461
Epoch [   45/   50] | d_loss: 0.0153 | g_loss: 6.6501
Epoch [   45/   50] | d_loss: 0.0079 | g_loss: 5.8120
Epoch [   45/   50] | d_loss: 0.3164 | g_loss: 4.2309
Epoch [   45/   50] | d_loss: 0.0242 | g_loss: 5.2552
Epoch [   45/   50] | d_loss: 0.0639 | g_loss: 3.3648
Epoch [   45/   50] | d_loss: 0.0237 | g_loss: 7.6913
Epoch [   45/   50] | d_loss: 0.0441 | g_loss: 9.2445
Epoch [   45/   50] | d_loss: 0.0311 | g_loss: 6.7478
Epoch [   45/   50] | d_loss: 0.0073 | g_loss: 7.8837
Epoch [   45/   50] | d_loss: 0.1007 | g_loss: 6.9203
Epoch [   45/   50] | d_loss: 0.4232 | g_loss: 4.2096
Epoch [   45/   50] | d_loss: 0.4656 | g_loss: 6.7323
Epoch [   45/   50] | d_loss: 0.0184 | g_loss: 8.6367
Epoch [   45/   50] | d_loss: 0.0945 | g_loss: 8.1998
Epoch [   45/   50] | d_loss: 0.0493 | g_loss: 6.4195
Epoch [   46/   50] | d_loss: 0.0201 | g_loss: 8.8238
Epoch [   46/   50] | d_loss: 0.0067 | g_loss: 4.7968
Epoch [   46/   50] | d_loss: 0.0300 | g_loss: 5.4876
Epoch [   46/   50] | d_loss: 0.0515 | g_loss: 6.9778
Epoch [   46/   50] | d_loss: 0.0138 | g_loss: 6.5986
Epoch [   46/   50] | d_loss: 0.0172 | g_loss: 6.2654
Epoch [   46/   50] | d_loss: 0.0302 | g_loss: 5.9443
Epoch [   46/   50] | d_loss: 0.2904 | g_loss: 4.2129
Epoch [   46/   50] | d_loss: 0.0061 | g_loss: 6.5914
Epoch [   46/   50] | d_loss: 0.2509 | g_loss: 2.5823
Epoch [   46/   50] | d_loss: 0.0657 | g_loss: 5.6442
Epoch [   46/   50] | d_loss: 0.0229 | g_loss: 5.4692
Epoch [   46/   50] | d_loss: 0.0955 | g_loss: 6.3742
Epoch [   46/   50] | d_loss: 0.1860 | g_loss: 5.6366
Epoch [   46/   50] | d_loss: 0.1177 | g_loss: 3.8098
Epoch [   47/   50] | d_loss: 0.0266 | g_loss: 5.8933
Epoch [   47/   50] | d_loss: 0.0627 | g_loss: 3.2303
Epoch [   47/   50] | d_loss: 0.0195 | g_loss: 6.2902
Epoch [   47/   50] | d_loss: 0.0482 | g_loss: 11.1971
Epoch [   47/   50] | d_loss: 0.0079 | g_loss: 12.1473
Epoch [   47/   50] | d_loss: 0.7184 | g_loss: 19.2494
Epoch [   47/   50] | d_loss: 0.1339 | g_loss: 3.0523
Epoch [   47/   50] | d_loss: 0.0113 | g_loss: 8.3489
Epoch [   47/   50] | d_loss: 0.0428 | g_loss: 7.9569
Epoch [   47/   50] | d_loss: 0.0215 | g_loss: 8.4127
Epoch [   47/   50] | d_loss: 0.3605 | g_loss: 10.1624
Epoch [   47/   50] | d_loss: 0.0082 | g_loss: 6.3958
Epoch [   47/   50] | d_loss: 0.0554 | g_loss: 7.3355
Epoch [   47/   50] | d_loss: 0.0050 | g_loss: 5.8608
Epoch [   47/   50] | d_loss: 0.0053 | g_loss: 9.1770
Epoch [   48/   50] | d_loss: 0.0261 | g_loss: 3.5379
Epoch [   48/   50] | d_loss: 0.3216 | g_loss: 5.4447
Epoch [   48/   50] | d_loss: 0.0568 | g_loss: 5.2640
Epoch [   48/   50] | d_loss: 0.1183 | g_loss: 7.0562
Epoch [   48/   50] | d_loss: 0.0379 | g_loss: 3.6157
Epoch [   48/   50] | d_loss: 0.0041 | g_loss: 8.2658
Epoch [   48/   50] | d_loss: 0.0231 | g_loss: 5.4090
Epoch [   48/   50] | d_loss: 0.0640 | g_loss: 6.7951
Epoch [   48/   50] | d_loss: 1.0012 | g_loss: 1.0420
Epoch [   48/   50] | d_loss: 0.0438 | g_loss: 5.7410
Epoch [   48/   50] | d_loss: 0.0593 | g_loss: 9.6474
Epoch [   48/   50] | d_loss: 0.0076 | g_loss: 10.4736
Epoch [   48/   50] | d_loss: 0.0940 | g_loss: 8.5205
Epoch [   48/   50] | d_loss: 0.0194 | g_loss: 6.8633
Epoch [   48/   50] | d_loss: 0.0045 | g_loss: 6.8908
Epoch [   49/   50] | d_loss: 0.0691 | g_loss: 4.4652
Epoch [   49/   50] | d_loss: 0.0310 | g_loss: 8.3301
Epoch [   49/   50] | d_loss: 0.0039 | g_loss: 5.4516
Epoch [   49/   50] | d_loss: 0.0643 | g_loss: 8.6300
Epoch [   49/   50] | d_loss: 0.0467 | g_loss: 6.6630
Epoch [   49/   50] | d_loss: 0.0236 | g_loss: 6.7984
Epoch [   49/   50] | d_loss: 2.7281 | g_loss: 0.4346
Epoch [   49/   50] | d_loss: 0.1266 | g_loss: 5.4779
Epoch [   49/   50] | d_loss: 0.1826 | g_loss: 7.4309
Epoch [   49/   50] | d_loss: 0.0031 | g_loss: 5.5619
Epoch [   49/   50] | d_loss: 0.3661 | g_loss: 4.0511
Epoch [   49/   50] | d_loss: 0.0135 | g_loss: 5.9294
Epoch [   49/   50] | d_loss: 0.0673 | g_loss: 5.8035
Epoch [   49/   50] | d_loss: 0.0204 | g_loss: 8.1347
Epoch [   49/   50] | d_loss: 0.0301 | g_loss: 6.4336
Epoch [   50/   50] | d_loss: 0.0752 | g_loss: 5.7902
Epoch [   50/   50] | d_loss: 0.0837 | g_loss: 6.4663
Epoch [   50/   50] | d_loss: 0.3345 | g_loss: 6.8608
Epoch [   50/   50] | d_loss: 0.0049 | g_loss: 6.2399
Epoch [   50/   50] | d_loss: 0.0533 | g_loss: 5.3091
Epoch [   50/   50] | d_loss: 0.0454 | g_loss: 8.5478
Epoch [   50/   50] | d_loss: 0.0049 | g_loss: 3.0448
Epoch [   50/   50] | d_loss: 0.0021 | g_loss: 9.1505
Epoch [   50/   50] | d_loss: 0.4724 | g_loss: 8.7964
Epoch [   50/   50] | d_loss: 0.0389 | g_loss: 5.8988
Epoch [   50/   50] | d_loss: 0.0304 | g_loss: 5.2921
Epoch [   50/   50] | d_loss: 0.0028 | g_loss: 8.2029
Epoch [   50/   50] | d_loss: 0.0102 | g_loss: 5.9786
Epoch [   50/   50] | d_loss: 0.0771 | g_loss: 6.5427
Epoch [   50/   50] | d_loss: 0.0210 | g_loss: 4.7388
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<h2 id="Training-loss">Training loss<a class="anchor-link" href="#Training-loss">&#182;</a></h2><p>Plot the training losses for the generator and discriminator, recorded after each epoch.</p>

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<div class="prompt input_prompt">In&nbsp;[21]:</div>
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<div class=" highlight hl-ipython3"><pre><span></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="n">losses</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">losses</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Discriminator&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Generator&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Training Losses&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
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<pre>&lt;matplotlib.legend.Legend at 0x16d0004eb00&gt;</pre>
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<h2 id="Generator-samples-from-training">Generator samples from training<a class="anchor-link" href="#Generator-samples-from-training">&#182;</a></h2><p>View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># helper function for viewing a list of passed in sample images</span>
<span class="k">def</span> <span class="nf">view_samples</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">samples</span><span class="p">):</span>
    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">samples</span><span class="p">[</span><span class="n">epoch</span><span class="p">]):</span>
        <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
        <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
        <span class="n">img</span> <span class="o">=</span> <span class="p">((</span><span class="n">img</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="mi">255</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">3</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load samples from generator, taken while training</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_samples.pkl&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">samples</span> <span class="o">=</span> <span class="n">pkl</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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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_</span> <span class="o">=</span> <span class="n">view_samples</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">samples</span><span class="p">)</span>
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<h3 id="Question:-What-do-you-notice-about-your-generated-samples-and-how-might-you-improve-this-model?">Question: What do you notice about your generated samples and how might you improve this model?<a class="anchor-link" href="#Question:-What-do-you-notice-about-your-generated-samples-and-how-might-you-improve-this-model?">&#182;</a></h3><p>When you answer this question, consider the following factors:</p>
<ul>
<li>The dataset is biased; it is made of "celebrity" faces that are mostly white</li>
<li>Model size; larger models have the opportunity to learn more features in a data feature space</li>
<li>Optimization strategy; optimizers and number of epochs affect your final result</li>
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<p><strong>Answer:</strong></p>
<p><em>Dataset</em></p>
<ul>
<li>Most of the faces of the dataset are of white people, therefore the generated faces are aligned with that stereotype of person. </li>
<li>It is also noticeable that the pose of the generated faces is mainly camera facing and smiley. </li>
<li>Last but not least, we can also notice that the cropping of the original faces has led to many faces where the chin is missing.</li>
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<p><em>Model / Optimization</em></p>
<ul>
<li>With regards to the model I initially tried a simpler 4 layers model with conv_size 32 which I trained for 30 epochs. The results were not bad with regards the faces generated but there was room for improvement.</li>
<li>I decided to increase the depth of the model to have 5 layers in order to better capture the complexity of human faces, increasing also the number of epochs to 50. The resulting images are more realistic.</li>
<li>Regarding learning rates and other hyperparameters I basically used the original model ones (learning rate, beta1 and beta2) although for the first 4-layer model I also tried learning rate 0.0005 for faster convergence. </li>
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<h3 id="Submitting-This-Project">Submitting This Project<a class="anchor-link" href="#Submitting-This-Project">&#182;</a></h3><p>When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -&gt; "Download as". Include the "problem_unittests.py" files in your submission.</p>

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