dlnd_face_generation.html

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<title>dlnd_face_generation</title>

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
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<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
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.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
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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="o">!</span>unzip processed_celeba_small.zip
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<pre>Archive:  processed_celeba_small.zip
replace processed_celeba_small/.DS_Store? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C
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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>

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<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
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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/0.4.0/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="k">import</span> <span class="n">datasets</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">transforms</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="c1"># resize to 128x128</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()])</span>
    
    
    <span class="c1"># define datasets 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">data_dir</span><span class="p">,</span> <span class="n">transform</span><span class="p">)</span>
    
    <span class="c1"># create and return DataLoaders</span>
    <span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">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="k">return</span> <span class="n">data_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>

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<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>

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<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="n">x</span> <span class="o">=</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>
    <span class="k">return</span> <span class="n">x</span>
    
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<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(-0.9922)
Max:  tensor(0.9608)
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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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create helper function to do batch normalization for both conv layers and deonv layers</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>


<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 transpose 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="c1"># append transpose conv 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">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"># optional batch norm layer</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="nf">__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="c1"># Define all convolutional layers</span>
        <span class="c1"># Should accept an RGB image as input and output a single value</span>

        <span class="c1"># Convolutional layers, increasing in depth</span>
        <span class="c1"># first layer has *no* batchnorm</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="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"># x, y = 32, depth 32</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"># (16, 16, 64)</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"># (8, 8, 128)</span>
        
        <span class="c1"># FC layer, real or fake</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">4</span><span class="o">*</span><span class="mi">4</span><span class="o">*</span><span class="mi">4</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="c1"># all hidden layers + leaky relu activation</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="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">4</span><span class="o">*</span><span class="mi">4</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
        
        <span class="c1"># final output layer</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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<pre>Tests Passed
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="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="nf">__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="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">4</span><span class="o">*</span><span class="mi">4</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
        
        <span class="c1"># transpose conv 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">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_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">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_conv3</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="c1"># fully-connected + reshape </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">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="c1"># (batch_size, depth, 4, 4)</span>
        
        <span class="c1"># hidden transpose conv layers + relu</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="c1"># last layer + 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_conv3</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">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>
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<span class="sd">&quot;&quot;&quot;</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://github.com/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="c1"># TODO: Apply initial weights to convolutional and linear layers</span>
    <span class="k">if</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"># apply a uniform distribution to the weights and a bias=0</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="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.02</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="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">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)
  )
  (fc): Linear(in_features=2048, out_features=1, bias=True)
)

Generator(
  (fc): Linear(in_features=100, out_features=2048, bias=True)
  (t_conv1): 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_conv2): 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_conv3): 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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<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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<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">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">D_out</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</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">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">D_out</span><span class="o">**</span><span class="mi">2</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"># params</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"># default value</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="c1"># ============================================</span>
            <span class="c1">#            TRAIN THE DISCRIMINATOR</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"># Train the discriminator on real and fake images</span>
            <span class="c1"># 1. Train with real images</span>

            <span class="c1"># Compute the discriminator losses on real 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"># 2. Train with fake images</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 x 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 loss 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"># =========================================</span>
            <span class="c1">#            TRAIN THE GENERATOR</span>
            <span class="c1"># =========================================</span>
            <span class="n">g_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>

            <span class="c1"># 1. Train with fake images and flipped labels</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="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"># use 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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="n">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_z</span> <span class="o">=</span> <span class="n">samples_z</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="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>
</pre></div>

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<p>Set your number of training epochs and train your GAN!</p>

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<div class="prompt input_prompt">In&nbsp;[20]:</div>
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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="kn">from</span> <span class="nn">workspace_utils</span> <span class="k">import</span> <span class="n">active_session</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="k">with</span> <span class="n">active_session</span><span class="p">():</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>
</pre></div>

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<pre>Epoch [    1/   50] | d_loss: 2.1538 | g_loss: 1.6550
Epoch [    1/   50] | d_loss: 0.1547 | g_loss: 0.8719
Epoch [    1/   50] | d_loss: 0.1220 | g_loss: 0.6862
Epoch [    1/   50] | d_loss: 0.1213 | g_loss: 1.1777
Epoch [    1/   50] | d_loss: 0.1422 | g_loss: 0.7543
Epoch [    1/   50] | d_loss: 0.1813 | g_loss: 0.6576
Epoch [    1/   50] | d_loss: 0.3093 | g_loss: 1.2246
Epoch [    1/   50] | d_loss: 0.2323 | g_loss: 0.7434
Epoch [    1/   50] | d_loss: 0.3970 | g_loss: 0.3324
Epoch [    1/   50] | d_loss: 0.3972 | g_loss: 0.5576
Epoch [    1/   50] | d_loss: 0.3261 | g_loss: 0.8415
Epoch [    1/   50] | d_loss: 0.3149 | g_loss: 0.5875
Epoch [    1/   50] | d_loss: 0.2812 | g_loss: 0.4573
Epoch [    1/   50] | d_loss: 0.2629 | g_loss: 0.5618
Epoch [    1/   50] | d_loss: 0.2905 | g_loss: 0.4950
Epoch [    1/   50] | d_loss: 0.3589 | g_loss: 0.5875
Epoch [    1/   50] | d_loss: 0.3376 | g_loss: 0.3683
Epoch [    1/   50] | d_loss: 0.3602 | g_loss: 1.0504
Epoch [    1/   50] | d_loss: 0.5187 | g_loss: 0.4443
Epoch [    1/   50] | d_loss: 0.3124 | g_loss: 0.4970
Epoch [    1/   50] | d_loss: 0.3535 | g_loss: 0.5073
Epoch [    1/   50] | d_loss: 0.3588 | g_loss: 0.3689
Epoch [    1/   50] | d_loss: 0.3803 | g_loss: 0.3889
Epoch [    1/   50] | d_loss: 0.3450 | g_loss: 0.4960
Epoch [    1/   50] | d_loss: 0.4167 | g_loss: 0.3100
Epoch [    1/   50] | d_loss: 0.3644 | g_loss: 0.4655
Epoch [    1/   50] | d_loss: 0.3921 | g_loss: 0.4062
Epoch [    1/   50] | d_loss: 0.4427 | g_loss: 0.2565
Epoch [    1/   50] | d_loss: 0.4754 | g_loss: 0.4356
Epoch [    1/   50] | d_loss: 0.3122 | g_loss: 0.5712
Epoch [    1/   50] | d_loss: 0.3911 | g_loss: 0.3024
Epoch [    1/   50] | d_loss: 0.3165 | g_loss: 0.4461
Epoch [    1/   50] | d_loss: 0.4400 | g_loss: 0.3749
Epoch [    1/   50] | d_loss: 0.5132 | g_loss: 0.2417
Epoch [    1/   50] | d_loss: 0.4066 | g_loss: 0.3816
Epoch [    1/   50] | d_loss: 0.4149 | g_loss: 0.3509
Epoch [    1/   50] | d_loss: 0.4437 | g_loss: 0.6675
Epoch [    1/   50] | d_loss: 0.4117 | g_loss: 0.6618
Epoch [    1/   50] | d_loss: 0.2145 | g_loss: 0.6637
Epoch [    1/   50] | d_loss: 0.3447 | g_loss: 0.5682
Epoch [    1/   50] | d_loss: 0.2591 | g_loss: 0.4933
Epoch [    1/   50] | d_loss: 0.4610 | g_loss: 0.3350
Epoch [    1/   50] | d_loss: 0.3356 | g_loss: 0.4177
Epoch [    1/   50] | d_loss: 0.3211 | g_loss: 0.3297
Epoch [    1/   50] | d_loss: 0.3595 | g_loss: 0.3836
Epoch [    1/   50] | d_loss: 0.3633 | g_loss: 0.3877
Epoch [    1/   50] | d_loss: 0.3715 | g_loss: 0.7847
Epoch [    1/   50] | d_loss: 0.2698 | g_loss: 0.6842
Epoch [    1/   50] | d_loss: 0.3188 | g_loss: 0.6705
Epoch [    1/   50] | d_loss: 0.3090 | g_loss: 0.9065
Epoch [    1/   50] | d_loss: 0.2985 | g_loss: 0.2636
Epoch [    1/   50] | d_loss: 0.5050 | g_loss: 0.2135
Epoch [    1/   50] | d_loss: 0.3441 | g_loss: 0.6004
Epoch [    1/   50] | d_loss: 0.4544 | g_loss: 0.5770
Epoch [    1/   50] | d_loss: 0.4277 | g_loss: 0.4089
Epoch [    1/   50] | d_loss: 0.3524 | g_loss: 0.4869
Epoch [    1/   50] | d_loss: 0.4116 | g_loss: 0.3321
Epoch [    2/   50] | d_loss: 0.4698 | g_loss: 0.5105
Epoch [    2/   50] | d_loss: 0.4184 | g_loss: 0.2735
Epoch [    2/   50] | d_loss: 0.3058 | g_loss: 0.4195
Epoch [    2/   50] | d_loss: 0.3336 | g_loss: 0.5005
Epoch [    2/   50] | d_loss: 0.3532 | g_loss: 0.4344
Epoch [    2/   50] | d_loss: 0.3884 | g_loss: 0.5882
Epoch [    2/   50] | d_loss: 0.3119 | g_loss: 0.7189
Epoch [    2/   50] | d_loss: 0.3363 | g_loss: 0.4228
Epoch [    2/   50] | d_loss: 0.3836 | g_loss: 0.5664
Epoch [    2/   50] | d_loss: 0.3531 | g_loss: 0.4505
Epoch [    2/   50] | d_loss: 0.4047 | g_loss: 0.5407
Epoch [    2/   50] | d_loss: 0.2949 | g_loss: 0.3862
Epoch [    2/   50] | d_loss: 0.3004 | g_loss: 0.3592
Epoch [    2/   50] | d_loss: 0.3121 | g_loss: 0.6047
Epoch [    2/   50] | d_loss: 0.3528 | g_loss: 0.5127
Epoch [    2/   50] | d_loss: 0.3691 | g_loss: 0.2896
Epoch [    2/   50] | d_loss: 0.6116 | g_loss: 0.2219
Epoch [    2/   50] | d_loss: 0.2675 | g_loss: 0.4094
Epoch [    2/   50] | d_loss: 0.3314 | g_loss: 0.5255
Epoch [    2/   50] | d_loss: 0.3890 | g_loss: 0.7042
Epoch [    2/   50] | d_loss: 0.2704 | g_loss: 0.6710
Epoch [    2/   50] | d_loss: 0.2694 | g_loss: 0.6990
Epoch [    2/   50] | d_loss: 0.3531 | g_loss: 0.3099
Epoch [    2/   50] | d_loss: 0.4939 | g_loss: 0.2031
Epoch [    2/   50] | d_loss: 0.4084 | g_loss: 0.2770
Epoch [    2/   50] | d_loss: 0.2858 | g_loss: 0.5711
Epoch [    2/   50] | d_loss: 0.3952 | g_loss: 0.5490
Epoch [    2/   50] | d_loss: 0.2885 | g_loss: 0.5276
Epoch [    2/   50] | d_loss: 0.4056 | g_loss: 0.5731
Epoch [    2/   50] | d_loss: 0.3613 | g_loss: 0.7697
Epoch [    2/   50] | d_loss: 0.4550 | g_loss: 0.2218
Epoch [    2/   50] | d_loss: 0.5122 | g_loss: 0.3182
Epoch [    2/   50] | d_loss: 0.2823 | g_loss: 0.4594
Epoch [    2/   50] | d_loss: 0.3981 | g_loss: 0.4980
Epoch [    2/   50] | d_loss: 0.3131 | g_loss: 0.4045
Epoch [    2/   50] | d_loss: 0.3432 | g_loss: 0.4973
Epoch [    2/   50] | d_loss: 0.3588 | g_loss: 0.3790
Epoch [    2/   50] | d_loss: 0.4357 | g_loss: 0.6759
Epoch [    2/   50] | d_loss: 0.7097 | g_loss: 0.5083
Epoch [    2/   50] | d_loss: 0.4071 | g_loss: 0.3317
Epoch [    2/   50] | d_loss: 0.4776 | g_loss: 0.3387
Epoch [    2/   50] | d_loss: 0.3803 | g_loss: 0.3835
Epoch [    2/   50] | d_loss: 0.3765 | g_loss: 0.5438
Epoch [    2/   50] | d_loss: 0.2866 | g_loss: 0.3386
Epoch [    2/   50] | d_loss: 0.3376 | g_loss: 0.2880
Epoch [    2/   50] | d_loss: 0.3174 | g_loss: 0.4911
Epoch [    2/   50] | d_loss: 0.2852 | g_loss: 1.0361
Epoch [    2/   50] | d_loss: 0.4329 | g_loss: 0.7630
Epoch [    2/   50] | d_loss: 0.4985 | g_loss: 0.5395
Epoch [    2/   50] | d_loss: 0.4344 | g_loss: 0.2527
Epoch [    2/   50] | d_loss: 0.3397 | g_loss: 0.5736
Epoch [    2/   50] | d_loss: 0.4513 | g_loss: 0.3810
Epoch [    2/   50] | d_loss: 0.2679 | g_loss: 0.4677
Epoch [    2/   50] | d_loss: 0.3888 | g_loss: 0.5822
Epoch [    2/   50] | d_loss: 0.3397 | g_loss: 0.2316
Epoch [    2/   50] | d_loss: 0.3045 | g_loss: 0.4815
Epoch [    2/   50] | d_loss: 0.5240 | g_loss: 0.4199
Epoch [    3/   50] | d_loss: 0.3580 | g_loss: 0.5697
Epoch [    3/   50] | d_loss: 0.3777 | g_loss: 0.4326
Epoch [    3/   50] | d_loss: 0.5146 | g_loss: 0.1056
Epoch [    3/   50] | d_loss: 0.4862 | g_loss: 0.6740
Epoch [    3/   50] | d_loss: 0.4398 | g_loss: 0.4359
Epoch [    3/   50] | d_loss: 0.3546 | g_loss: 0.5389
Epoch [    3/   50] | d_loss: 0.4378 | g_loss: 0.5855
Epoch [    3/   50] | d_loss: 0.5494 | g_loss: 0.4326
Epoch [    3/   50] | d_loss: 0.3618 | g_loss: 0.3394
Epoch [    3/   50] | d_loss: 0.4425 | g_loss: 0.2593
Epoch [    3/   50] | d_loss: 0.3188 | g_loss: 0.4124
Epoch [    3/   50] | d_loss: 0.2697 | g_loss: 0.6602
Epoch [    3/   50] | d_loss: 0.3847 | g_loss: 0.5166
Epoch [    3/   50] | d_loss: 0.3582 | g_loss: 0.3665
Epoch [    3/   50] | d_loss: 0.3230 | g_loss: 0.4060
Epoch [    3/   50] | d_loss: 0.4792 | g_loss: 0.3016
Epoch [    3/   50] | d_loss: 0.3539 | g_loss: 0.5227
Epoch [    3/   50] | d_loss: 0.3373 | g_loss: 0.4351
Epoch [    3/   50] | d_loss: 0.2003 | g_loss: 0.4747
Epoch [    3/   50] | d_loss: 0.3337 | g_loss: 0.5402
Epoch [    3/   50] | d_loss: 0.3582 | g_loss: 0.4288
Epoch [    3/   50] | d_loss: 0.3070 | g_loss: 0.7507
Epoch [    3/   50] | d_loss: 0.3983 | g_loss: 0.4085
Epoch [    3/   50] | d_loss: 0.3802 | g_loss: 0.5516
Epoch [    3/   50] | d_loss: 0.2456 | g_loss: 0.5130
Epoch [    3/   50] | d_loss: 0.3897 | g_loss: 0.3936
Epoch [    3/   50] | d_loss: 0.3113 | g_loss: 0.4504
Epoch [    3/   50] | d_loss: 0.3282 | g_loss: 0.3865
Epoch [    3/   50] | d_loss: 0.5152 | g_loss: 0.7590
Epoch [    3/   50] | d_loss: 0.3600 | g_loss: 0.6536
Epoch [    3/   50] | d_loss: 0.3553 | g_loss: 0.5659
Epoch [    3/   50] | d_loss: 0.4036 | g_loss: 0.5119
Epoch [    3/   50] | d_loss: 0.3458 | g_loss: 0.3743
Epoch [    3/   50] | d_loss: 0.1631 | g_loss: 0.8458
Epoch [    3/   50] | d_loss: 0.1675 | g_loss: 0.7109
Epoch [    3/   50] | d_loss: 0.2855 | g_loss: 0.4034
Epoch [    3/   50] | d_loss: 0.2950 | g_loss: 0.4653
Epoch [    3/   50] | d_loss: 0.2970 | g_loss: 0.6143
Epoch [    3/   50] | d_loss: 0.5313 | g_loss: 0.3043
Epoch [    3/   50] | d_loss: 0.4278 | g_loss: 0.5875
Epoch [    3/   50] | d_loss: 0.1989 | g_loss: 0.7067
Epoch [    3/   50] | d_loss: 0.3028 | g_loss: 0.5749
Epoch [    3/   50] | d_loss: 0.2810 | g_loss: 0.5692
Epoch [    3/   50] | d_loss: 0.3298 | g_loss: 0.5445
Epoch [    3/   50] | d_loss: 0.4263 | g_loss: 0.4210
Epoch [    3/   50] | d_loss: 0.2940 | g_loss: 0.5284
Epoch [    3/   50] | d_loss: 0.4659 | g_loss: 0.5682
Epoch [    3/   50] | d_loss: 0.4095 | g_loss: 0.7301
Epoch [    3/   50] | d_loss: 0.2366 | g_loss: 0.4431
Epoch [    3/   50] | d_loss: 0.3113 | g_loss: 0.5992
Epoch [    3/   50] | d_loss: 0.3575 | g_loss: 0.6635
Epoch [    3/   50] | d_loss: 0.3710 | g_loss: 0.4875
Epoch [    3/   50] | d_loss: 0.4582 | g_loss: 0.7407
Epoch [    3/   50] | d_loss: 0.4362 | g_loss: 0.4922
Epoch [    3/   50] | d_loss: 0.3719 | g_loss: 0.3671
Epoch [    3/   50] | d_loss: 0.2342 | g_loss: 0.6863
Epoch [    3/   50] | d_loss: 0.2095 | g_loss: 0.3103
Epoch [    4/   50] | d_loss: 0.4348 | g_loss: 0.5129
Epoch [    4/   50] | d_loss: 0.2941 | g_loss: 0.3123
Epoch [    4/   50] | d_loss: 0.3379 | g_loss: 0.7563
Epoch [    4/   50] | d_loss: 0.3062 | g_loss: 0.5034
Epoch [    4/   50] | d_loss: 0.3177 | g_loss: 0.2644
Epoch [    4/   50] | d_loss: 0.3450 | g_loss: 0.3503
Epoch [    4/   50] | d_loss: 0.3360 | g_loss: 0.8724
Epoch [    4/   50] | d_loss: 0.2673 | g_loss: 0.5944
Epoch [    4/   50] | d_loss: 0.2239 | g_loss: 1.0163
Epoch [    4/   50] | d_loss: 0.2609 | g_loss: 0.6532
Epoch [    4/   50] | d_loss: 0.3607 | g_loss: 0.5774
Epoch [    4/   50] | d_loss: 0.4080 | g_loss: 0.6754
Epoch [    4/   50] | d_loss: 0.2462 | g_loss: 0.3962
Epoch [    4/   50] | d_loss: 0.3671 | g_loss: 0.3546
Epoch [    4/   50] | d_loss: 0.2395 | g_loss: 0.3702
Epoch [    4/   50] | d_loss: 0.2613 | g_loss: 0.5019
Epoch [    4/   50] | d_loss: 0.2079 | g_loss: 0.8886
Epoch [    4/   50] | d_loss: 0.4611 | g_loss: 0.3319
Epoch [    4/   50] | d_loss: 0.1992 | g_loss: 0.4001
Epoch [    4/   50] | d_loss: 0.2511 | g_loss: 0.4822
Epoch [    4/   50] | d_loss: 0.3747 | g_loss: 0.4216
Epoch [    4/   50] | d_loss: 0.2916 | g_loss: 0.3680
Epoch [    4/   50] | d_loss: 0.3135 | g_loss: 0.5629
Epoch [    4/   50] | d_loss: 0.3427 | g_loss: 0.4324
Epoch [    4/   50] | d_loss: 0.1892 | g_loss: 0.6931
Epoch [    4/   50] | d_loss: 0.3108 | g_loss: 0.7164
Epoch [    4/   50] | d_loss: 0.2880 | g_loss: 0.4718
Epoch [    4/   50] | d_loss: 0.2157 | g_loss: 0.5258
Epoch [    4/   50] | d_loss: 0.4188 | g_loss: 0.4689
Epoch [    4/   50] | d_loss: 0.4954 | g_loss: 0.7988
Epoch [    4/   50] | d_loss: 0.2402 | g_loss: 1.0528
Epoch [    4/   50] | d_loss: 0.4156 | g_loss: 0.3328
Epoch [    4/   50] | d_loss: 0.3024 | g_loss: 0.6297
Epoch [    4/   50] | d_loss: 0.3927 | g_loss: 0.6919
Epoch [    4/   50] | d_loss: 0.2845 | g_loss: 0.5541
Epoch [    4/   50] | d_loss: 0.2542 | g_loss: 0.5788
Epoch [    4/   50] | d_loss: 0.3212 | g_loss: 0.4139
Epoch [    4/   50] | d_loss: 0.3477 | g_loss: 0.5475
Epoch [    4/   50] | d_loss: 0.3634 | g_loss: 0.3933
Epoch [    4/   50] | d_loss: 0.4073 | g_loss: 0.5509
Epoch [    4/   50] | d_loss: 0.3817 | g_loss: 0.5094
Epoch [    4/   50] | d_loss: 0.3461 | g_loss: 0.2468
Epoch [    4/   50] | d_loss: 0.2996 | g_loss: 0.5625
Epoch [    4/   50] | d_loss: 0.3468 | g_loss: 0.6297
Epoch [    4/   50] | d_loss: 0.2782 | g_loss: 0.7841
Epoch [    4/   50] | d_loss: 0.2606 | g_loss: 0.3217
Epoch [    4/   50] | d_loss: 0.2179 | g_loss: 0.6720
Epoch [    4/   50] | d_loss: 0.2619 | g_loss: 0.7258
Epoch [    4/   50] | d_loss: 0.3462 | g_loss: 0.3262
Epoch [    4/   50] | d_loss: 0.4353 | g_loss: 0.2997
Epoch [    4/   50] | d_loss: 0.3270 | g_loss: 0.6421
Epoch [    4/   50] | d_loss: 0.1778 | g_loss: 1.1375
Epoch [    4/   50] | d_loss: 0.3586 | g_loss: 0.4911
Epoch [    4/   50] | d_loss: 0.3199 | g_loss: 0.5082
Epoch [    4/   50] | d_loss: 0.3905 | g_loss: 0.6363
Epoch [    4/   50] | d_loss: 0.3233 | g_loss: 0.4748
Epoch [    4/   50] | d_loss: 0.4242 | g_loss: 0.1938
Epoch [    5/   50] | d_loss: 0.1789 | g_loss: 0.8748
Epoch [    5/   50] | d_loss: 0.4035 | g_loss: 0.4457
Epoch [    5/   50] | d_loss: 0.3090 | g_loss: 0.9533
Epoch [    5/   50] | d_loss: 0.1988 | g_loss: 0.6931
Epoch [    5/   50] | d_loss: 0.3181 | g_loss: 0.3848
Epoch [    5/   50] | d_loss: 0.1944 | g_loss: 0.6826
Epoch [    5/   50] | d_loss: 0.2463 | g_loss: 1.1706
Epoch [    5/   50] | d_loss: 0.3281 | g_loss: 0.5305
Epoch [    5/   50] | d_loss: 0.2774 | g_loss: 0.6442
Epoch [    5/   50] | d_loss: 0.3332 | g_loss: 0.5630
Epoch [    5/   50] | d_loss: 0.3455 | g_loss: 0.3701
Epoch [    5/   50] | d_loss: 0.1969 | g_loss: 0.4290
Epoch [    5/   50] | d_loss: 0.2987 | g_loss: 0.4755
Epoch [    5/   50] | d_loss: 0.1766 | g_loss: 0.4837
Epoch [    5/   50] | d_loss: 0.4478 | g_loss: 0.5553
Epoch [    5/   50] | d_loss: 0.2450 | g_loss: 0.6580
Epoch [    5/   50] | d_loss: 0.3021 | g_loss: 0.6345
Epoch [    5/   50] | d_loss: 0.2203 | g_loss: 0.3986
Epoch [    5/   50] | d_loss: 0.2834 | g_loss: 0.5598
Epoch [    5/   50] | d_loss: 0.2568 | g_loss: 0.5977
Epoch [    5/   50] | d_loss: 0.3423 | g_loss: 0.3507
Epoch [    5/   50] | d_loss: 0.2538 | g_loss: 0.5857
Epoch [    5/   50] | d_loss: 0.2801 | g_loss: 0.4368
Epoch [    5/   50] | d_loss: 0.2609 | g_loss: 0.6433
Epoch [    5/   50] | d_loss: 0.3996 | g_loss: 0.6331
Epoch [    5/   50] | d_loss: 0.2915 | g_loss: 0.5154
Epoch [    5/   50] | d_loss: 0.3716 | g_loss: 0.6244
Epoch [    5/   50] | d_loss: 0.2846 | g_loss: 0.5741
Epoch [    5/   50] | d_loss: 0.2705 | g_loss: 0.4315
Epoch [    5/   50] | d_loss: 0.2435 | g_loss: 0.4758
Epoch [    5/   50] | d_loss: 0.2927 | g_loss: 0.7385
Epoch [    5/   50] | d_loss: 0.2084 | g_loss: 0.3919
Epoch [    5/   50] | d_loss: 0.2358 | g_loss: 0.5350
Epoch [    5/   50] | d_loss: 0.3143 | g_loss: 0.6824
Epoch [    5/   50] | d_loss: 0.3866 | g_loss: 0.2727
Epoch [    5/   50] | d_loss: 0.2747 | g_loss: 0.9293
Epoch [    5/   50] | d_loss: 0.1764 | g_loss: 0.8882
Epoch [    5/   50] | d_loss: 0.3462 | g_loss: 0.5514
Epoch [    5/   50] | d_loss: 0.4141 | g_loss: 0.9897
Epoch [    5/   50] | d_loss: 0.3374 | g_loss: 0.4688
Epoch [    5/   50] | d_loss: 0.1923 | g_loss: 0.6706
Epoch [    5/   50] | d_loss: 0.3055 | g_loss: 0.7215
Epoch [    5/   50] | d_loss: 0.3311 | g_loss: 0.4625
Epoch [    5/   50] | d_loss: 0.2703 | g_loss: 0.4067
Epoch [    5/   50] | d_loss: 0.2021 | g_loss: 0.7023
Epoch [    5/   50] | d_loss: 0.1585 | g_loss: 0.5504
Epoch [    5/   50] | d_loss: 0.2803 | g_loss: 0.3644
Epoch [    5/   50] | d_loss: 0.2915 | g_loss: 0.5292
Epoch [    5/   50] | d_loss: 0.3067 | g_loss: 0.3314
Epoch [    5/   50] | d_loss: 0.1936 | g_loss: 0.5022
Epoch [    5/   50] | d_loss: 0.3564 | g_loss: 0.5618
Epoch [    5/   50] | d_loss: 0.2183 | g_loss: 0.4460
Epoch [    5/   50] | d_loss: 0.2967 | g_loss: 0.7381
Epoch [    5/   50] | d_loss: 0.3958 | g_loss: 0.2915
Epoch [    5/   50] | d_loss: 0.2569 | g_loss: 0.7787
Epoch [    5/   50] | d_loss: 0.3863 | g_loss: 0.2884
Epoch [    5/   50] | d_loss: 0.3037 | g_loss: 0.3963
Epoch [    6/   50] | d_loss: 0.5117 | g_loss: 0.4482
Epoch [    6/   50] | d_loss: 0.9938 | g_loss: 0.4496
Epoch [    6/   50] | d_loss: 0.2917 | g_loss: 0.5731
Epoch [    6/   50] | d_loss: 0.1947 | g_loss: 0.5201
Epoch [    6/   50] | d_loss: 0.3108 | g_loss: 0.4472
Epoch [    6/   50] | d_loss: 0.2953 | g_loss: 0.2702
Epoch [    6/   50] | d_loss: 0.1154 | g_loss: 0.6325
Epoch [    6/   50] | d_loss: 0.5589 | g_loss: 0.4624
Epoch [    6/   50] | d_loss: 0.2849 | g_loss: 0.5045
Epoch [    6/   50] | d_loss: 0.2475 | g_loss: 0.6022
Epoch [    6/   50] | d_loss: 0.1624 | g_loss: 0.9585
Epoch [    6/   50] | d_loss: 0.4091 | g_loss: 0.3677
Epoch [    6/   50] | d_loss: 0.4322 | g_loss: 0.4215
Epoch [    6/   50] | d_loss: 0.2871 | g_loss: 0.5893
Epoch [    6/   50] | d_loss: 0.2431 | g_loss: 0.5576
Epoch [    6/   50] | d_loss: 0.3147 | g_loss: 0.6424
Epoch [    6/   50] | d_loss: 0.3119 | g_loss: 0.3621
Epoch [    6/   50] | d_loss: 0.2929 | g_loss: 0.6890
Epoch [    6/   50] | d_loss: 0.2483 | g_loss: 0.7878
Epoch [    6/   50] | d_loss: 0.2450 | g_loss: 0.5049
Epoch [    6/   50] | d_loss: 0.3681 | g_loss: 0.4430
Epoch [    6/   50] | d_loss: 0.2064 | g_loss: 0.5089
Epoch [    6/   50] | d_loss: 0.3575 | g_loss: 0.2480
Epoch [    6/   50] | d_loss: 0.2237 | g_loss: 0.5056
Epoch [    6/   50] | d_loss: 0.4236 | g_loss: 0.7047
Epoch [    6/   50] | d_loss: 0.3510 | g_loss: 0.5727
Epoch [    6/   50] | d_loss: 0.3290 | g_loss: 0.4665
Epoch [    6/   50] | d_loss: 0.2098 | g_loss: 0.3708
Epoch [    6/   50] | d_loss: 0.3485 | g_loss: 0.4887
Epoch [    6/   50] | d_loss: 0.2133 | g_loss: 0.6257
Epoch [    6/   50] | d_loss: 0.3147 | g_loss: 0.7953
Epoch [    6/   50] | d_loss: 0.2839 | g_loss: 0.2826
Epoch [    6/   50] | d_loss: 0.2261 | g_loss: 0.8452
Epoch [    6/   50] | d_loss: 0.3625 | g_loss: 0.4331
Epoch [    6/   50] | d_loss: 0.1781 | g_loss: 0.8424
Epoch [    6/   50] | d_loss: 0.2458 | g_loss: 0.2205
Epoch [    6/   50] | d_loss: 0.2541 | g_loss: 0.6716
Epoch [    6/   50] | d_loss: 0.2079 | g_loss: 0.3899
Epoch [    6/   50] | d_loss: 0.2672 | g_loss: 0.2886
Epoch [    6/   50] | d_loss: 0.2639 | g_loss: 1.0951
Epoch [    6/   50] | d_loss: 0.2960 | g_loss: 0.3713
Epoch [    6/   50] | d_loss: 0.2834 | g_loss: 0.4363
Epoch [    6/   50] | d_loss: 0.2212 | g_loss: 1.0838
Epoch [    6/   50] | d_loss: 0.3476 | g_loss: 0.5194
Epoch [    6/   50] | d_loss: 0.1328 | g_loss: 0.7130
Epoch [    6/   50] | d_loss: 0.1351 | g_loss: 0.5394
Epoch [    6/   50] | d_loss: 0.2810 | g_loss: 0.6205
Epoch [    6/   50] | d_loss: 0.2540 | g_loss: 0.3539
Epoch [    6/   50] | d_loss: 0.2878 | g_loss: 0.3775
Epoch [    6/   50] | d_loss: 0.4117 | g_loss: 0.5818
Epoch [    6/   50] | d_loss: 0.3419 | g_loss: 0.3438
Epoch [    6/   50] | d_loss: 0.2500 | g_loss: 0.2826
Epoch [    6/   50] | d_loss: 0.2191 | g_loss: 0.6014
Epoch [    6/   50] | d_loss: 0.4370 | g_loss: 0.7056
Epoch [    6/   50] | d_loss: 0.2002 | g_loss: 0.4966
Epoch [    6/   50] | d_loss: 0.1897 | g_loss: 0.7955
Epoch [    6/   50] | d_loss: 0.2293 | g_loss: 0.8318
Epoch [    7/   50] | d_loss: 0.1914 | g_loss: 0.7363
Epoch [    7/   50] | d_loss: 0.2343 | g_loss: 0.7236
Epoch [    7/   50] | d_loss: 0.3126 | g_loss: 0.6053
Epoch [    7/   50] | d_loss: 0.2502 | g_loss: 0.7816
Epoch [    7/   50] | d_loss: 0.3067 | g_loss: 0.2727
Epoch [    7/   50] | d_loss: 0.3232 | g_loss: 0.9133
Epoch [    7/   50] | d_loss: 0.2528 | g_loss: 0.8105
Epoch [    7/   50] | d_loss: 0.2374 | g_loss: 0.4807
Epoch [    7/   50] | d_loss: 0.2242 | g_loss: 1.1864
Epoch [    7/   50] | d_loss: 0.2903 | g_loss: 0.4641
Epoch [    7/   50] | d_loss: 0.3793 | g_loss: 0.7957
Epoch [    7/   50] | d_loss: 0.2564 | g_loss: 0.5899
Epoch [    7/   50] | d_loss: 0.1670 | g_loss: 0.3898
Epoch [    7/   50] | d_loss: 0.3214 | g_loss: 0.5482
Epoch [    7/   50] | d_loss: 0.2508 | g_loss: 0.8934
Epoch [    7/   50] | d_loss: 0.1962 | g_loss: 1.0764
Epoch [    7/   50] | d_loss: 0.2225 | g_loss: 0.7463
Epoch [    7/   50] | d_loss: 0.2031 | g_loss: 0.3525
Epoch [    7/   50] | d_loss: 0.3823 | g_loss: 0.5131
Epoch [    7/   50] | d_loss: 0.1660 | g_loss: 0.5130
Epoch [    7/   50] | d_loss: 0.2308 | g_loss: 0.4029
Epoch [    7/   50] | d_loss: 0.3295 | g_loss: 0.5074
Epoch [    7/   50] | d_loss: 0.3422 | g_loss: 0.4204
Epoch [    7/   50] | d_loss: 0.4015 | g_loss: 0.3846
Epoch [    7/   50] | d_loss: 0.2216 | g_loss: 0.6788
Epoch [    7/   50] | d_loss: 0.2545 | g_loss: 0.5615
Epoch [    7/   50] | d_loss: 0.2234 | g_loss: 0.4425
Epoch [    7/   50] | d_loss: 0.3212 | g_loss: 0.5137
Epoch [    7/   50] | d_loss: 0.2215 | g_loss: 0.4042
Epoch [    7/   50] | d_loss: 0.3892 | g_loss: 0.6122
Epoch [    7/   50] | d_loss: 0.3475 | g_loss: 0.4815
Epoch [    7/   50] | d_loss: 0.2476 | g_loss: 0.5433
Epoch [    7/   50] | d_loss: 0.2717 | g_loss: 0.5743
Epoch [    7/   50] | d_loss: 0.2452 | g_loss: 0.7444
Epoch [    7/   50] | d_loss: 0.2817 | g_loss: 0.4856
Epoch [    7/   50] | d_loss: 0.2164 | g_loss: 0.4241
Epoch [    7/   50] | d_loss: 0.2597 | g_loss: 0.5368
Epoch [    7/   50] | d_loss: 0.3722 | g_loss: 0.3107
Epoch [    7/   50] | d_loss: 0.2448 | g_loss: 0.5729
Epoch [    7/   50] | d_loss: 0.1363 | g_loss: 0.7465
Epoch [    7/   50] | d_loss: 0.2830 | g_loss: 0.6352
Epoch [    7/   50] | d_loss: 0.4016 | g_loss: 1.3607
Epoch [    7/   50] | d_loss: 0.3200 | g_loss: 0.5023
Epoch [    7/   50] | d_loss: 0.2344 | g_loss: 0.6398
Epoch [    7/   50] | d_loss: 0.1608 | g_loss: 0.8285
Epoch [    7/   50] | d_loss: 0.2905 | g_loss: 0.4378
Epoch [    7/   50] | d_loss: 0.2051 | g_loss: 0.7011
Epoch [    7/   50] | d_loss: 0.2278 | g_loss: 0.5716
Epoch [    7/   50] | d_loss: 0.2691 | g_loss: 0.3525
Epoch [    7/   50] | d_loss: 0.2298 | g_loss: 0.8977
Epoch [    7/   50] | d_loss: 0.1605 | g_loss: 0.6337
Epoch [    7/   50] | d_loss: 0.3425 | g_loss: 0.8859
Epoch [    7/   50] | d_loss: 0.2590 | g_loss: 0.8128
Epoch [    7/   50] | d_loss: 0.1333 | g_loss: 0.7585
Epoch [    7/   50] | d_loss: 0.2908 | g_loss: 0.4132
Epoch [    7/   50] | d_loss: 0.2207 | g_loss: 0.8560
Epoch [    7/   50] | d_loss: 0.1693 | g_loss: 0.6814
Epoch [    8/   50] | d_loss: 0.2681 | g_loss: 0.5150
Epoch [    8/   50] | d_loss: 0.2821 | g_loss: 0.6139
Epoch [    8/   50] | d_loss: 0.2362 | g_loss: 0.5663
Epoch [    8/   50] | d_loss: 0.1614 | g_loss: 0.5572
Epoch [    8/   50] | d_loss: 0.2086 | g_loss: 0.6945
Epoch [    8/   50] | d_loss: 0.1382 | g_loss: 0.4460
Epoch [    8/   50] | d_loss: 0.2076 | g_loss: 0.6668
Epoch [    8/   50] | d_loss: 0.3254 | g_loss: 0.9130
Epoch [    8/   50] | d_loss: 0.1634 | g_loss: 0.5499
Epoch [    8/   50] | d_loss: 0.2631 | g_loss: 0.7407
Epoch [    8/   50] | d_loss: 0.4032 | g_loss: 1.0346
Epoch [    8/   50] | d_loss: 0.2160 | g_loss: 0.4375
Epoch [    8/   50] | d_loss: 0.3119 | g_loss: 0.3247
Epoch [    8/   50] | d_loss: 0.2173 | g_loss: 0.5468
Epoch [    8/   50] | d_loss: 0.2709 | g_loss: 0.4234
Epoch [    8/   50] | d_loss: 0.1278 | g_loss: 0.5063
Epoch [    8/   50] | d_loss: 0.1932 | g_loss: 0.4133
Epoch [    8/   50] | d_loss: 0.1229 | g_loss: 0.3389
Epoch [    8/   50] | d_loss: 0.1735 | g_loss: 0.6958
Epoch [    8/   50] | d_loss: 0.2639 | g_loss: 0.3212
Epoch [    8/   50] | d_loss: 0.4858 | g_loss: 0.4225
Epoch [    8/   50] | d_loss: 0.1533 | g_loss: 0.6367
Epoch [    8/   50] | d_loss: 0.4407 | g_loss: 0.6952
Epoch [    8/   50] | d_loss: 0.2437 | g_loss: 0.3832
Epoch [    8/   50] | d_loss: 0.2075 | g_loss: 0.5524
Epoch [    8/   50] | d_loss: 0.2709 | g_loss: 0.6962
Epoch [    8/   50] | d_loss: 0.2773 | g_loss: 0.7096
Epoch [    8/   50] | d_loss: 0.2158 | g_loss: 0.4828
Epoch [    8/   50] | d_loss: 0.2367 | g_loss: 0.5372
Epoch [    8/   50] | d_loss: 0.2222 | g_loss: 0.5128
Epoch [    8/   50] | d_loss: 0.2875 | g_loss: 0.4943
Epoch [    8/   50] | d_loss: 0.1681 | g_loss: 0.5357
Epoch [    8/   50] | d_loss: 0.2278 | g_loss: 0.6137
Epoch [    8/   50] | d_loss: 0.3071 | g_loss: 0.4479
Epoch [    8/   50] | d_loss: 0.2101 | g_loss: 0.5586
Epoch [    8/   50] | d_loss: 0.2713 | g_loss: 0.6986
Epoch [    8/   50] | d_loss: 0.2810 | g_loss: 0.5603
Epoch [    8/   50] | d_loss: 0.2206 | g_loss: 0.6004
Epoch [    8/   50] | d_loss: 0.2418 | g_loss: 0.4441
Epoch [    8/   50] | d_loss: 0.2749 | g_loss: 0.7219
Epoch [    8/   50] | d_loss: 0.2044 | g_loss: 0.7233
Epoch [    8/   50] | d_loss: 0.2807 | g_loss: 0.5282
Epoch [    8/   50] | d_loss: 0.1408 | g_loss: 0.5668
Epoch [    8/   50] | d_loss: 0.3198 | g_loss: 1.0838
Epoch [    8/   50] | d_loss: 0.1291 | g_loss: 0.6898
Epoch [    8/   50] | d_loss: 0.1922 | g_loss: 0.6790
Epoch [    8/   50] | d_loss: 0.3098 | g_loss: 0.8639
Epoch [    8/   50] | d_loss: 0.2706 | g_loss: 0.5054
Epoch [    8/   50] | d_loss: 0.3066 | g_loss: 0.2531
Epoch [    8/   50] | d_loss: 0.3567 | g_loss: 0.3607
Epoch [    8/   50] | d_loss: 0.3042 | g_loss: 0.5170
Epoch [    8/   50] | d_loss: 0.2752 | g_loss: 0.7940
Epoch [    8/   50] | d_loss: 0.7834 | g_loss: 0.3238
Epoch [    8/   50] | d_loss: 0.3979 | g_loss: 0.7196
Epoch [    8/   50] | d_loss: 0.2946 | g_loss: 0.6274
Epoch [    8/   50] | d_loss: 0.1583 | g_loss: 1.0451
Epoch [    8/   50] | d_loss: 0.2584 | g_loss: 0.7098
Epoch [    9/   50] | d_loss: 0.2311 | g_loss: 0.7190
Epoch [    9/   50] | d_loss: 0.2157 | g_loss: 0.5910
Epoch [    9/   50] | d_loss: 0.2854 | g_loss: 0.7729
Epoch [    9/   50] | d_loss: 0.1804 | g_loss: 0.4222
Epoch [    9/   50] | d_loss: 0.1636 | g_loss: 0.6684
Epoch [    9/   50] | d_loss: 0.3629 | g_loss: 0.5556
Epoch [    9/   50] | d_loss: 0.2042 | g_loss: 0.4070
Epoch [    9/   50] | d_loss: 0.2240 | g_loss: 0.9952
Epoch [    9/   50] | d_loss: 0.2524 | g_loss: 0.6223
Epoch [    9/   50] | d_loss: 0.2314 | g_loss: 0.6464
Epoch [    9/   50] | d_loss: 0.3490 | g_loss: 0.4936
Epoch [    9/   50] | d_loss: 0.2067 | g_loss: 0.8415
Epoch [    9/   50] | d_loss: 0.3055 | g_loss: 0.4352
Epoch [    9/   50] | d_loss: 0.1748 | g_loss: 0.7443
Epoch [    9/   50] | d_loss: 0.2333 | g_loss: 0.3690
Epoch [    9/   50] | d_loss: 0.1191 | g_loss: 0.3984
Epoch [    9/   50] | d_loss: 0.3315 | g_loss: 0.3366
Epoch [    9/   50] | d_loss: 0.2064 | g_loss: 0.6036
Epoch [    9/   50] | d_loss: 0.2670 | g_loss: 0.3729
Epoch [    9/   50] | d_loss: 0.2574 | g_loss: 0.5856
Epoch [    9/   50] | d_loss: 0.2472 | g_loss: 1.1399
Epoch [    9/   50] | d_loss: 0.1524 | g_loss: 1.0249
Epoch [    9/   50] | d_loss: 0.1115 | g_loss: 0.3788
Epoch [    9/   50] | d_loss: 0.2218 | g_loss: 1.0198
Epoch [    9/   50] | d_loss: 0.2495 | g_loss: 0.6645
Epoch [    9/   50] | d_loss: 0.2326 | g_loss: 0.4370
Epoch [    9/   50] | d_loss: 0.2669 | g_loss: 0.4722
Epoch [    9/   50] | d_loss: 0.2639 | g_loss: 0.5182
Epoch [    9/   50] | d_loss: 0.4835 | g_loss: 0.3550
Epoch [    9/   50] | d_loss: 0.2586 | g_loss: 0.6308
Epoch [    9/   50] | d_loss: 0.2690 | g_loss: 0.6720
Epoch [    9/   50] | d_loss: 0.1076 | g_loss: 0.9498
Epoch [    9/   50] | d_loss: 0.2795 | g_loss: 0.6528
Epoch [    9/   50] | d_loss: 0.1900 | g_loss: 0.8314
Epoch [    9/   50] | d_loss: 0.2218 | g_loss: 0.5275
Epoch [    9/   50] | d_loss: 0.3577 | g_loss: 0.3347
Epoch [    9/   50] | d_loss: 0.4156 | g_loss: 1.1076
Epoch [    9/   50] | d_loss: 0.2170 | g_loss: 0.4513
Epoch [    9/   50] | d_loss: 0.2266 | g_loss: 0.6912
Epoch [    9/   50] | d_loss: 0.3097 | g_loss: 0.4162
Epoch [    9/   50] | d_loss: 0.1466 | g_loss: 0.9508
Epoch [    9/   50] | d_loss: 0.2698 | g_loss: 0.8824
Epoch [    9/   50] | d_loss: 0.2648 | g_loss: 0.4159
Epoch [    9/   50] | d_loss: 0.1545 | g_loss: 0.9647
Epoch [    9/   50] | d_loss: 0.3579 | g_loss: 0.9156
Epoch [    9/   50] | d_loss: 0.1197 | g_loss: 1.0428
Epoch [    9/   50] | d_loss: 0.2160 | g_loss: 0.6632
Epoch [    9/   50] | d_loss: 0.2419 | g_loss: 0.4646
Epoch [    9/   50] | d_loss: 0.2735 | g_loss: 0.9291
Epoch [    9/   50] | d_loss: 0.2074 | g_loss: 0.8521
Epoch [    9/   50] | d_loss: 0.1742 | g_loss: 0.6878
Epoch [    9/   50] | d_loss: 0.2343 | g_loss: 0.4842
Epoch [    9/   50] | d_loss: 0.2505 | g_loss: 0.6253
Epoch [    9/   50] | d_loss: 0.2922 | g_loss: 0.6786
Epoch [    9/   50] | d_loss: 0.1976 | g_loss: 0.8340
Epoch [    9/   50] | d_loss: 0.1373 | g_loss: 0.7941
Epoch [    9/   50] | d_loss: 0.4216 | g_loss: 0.4716
Epoch [   10/   50] | d_loss: 0.4288 | g_loss: 0.8141
Epoch [   10/   50] | d_loss: 0.2329 | g_loss: 0.5177
Epoch [   10/   50] | d_loss: 0.2402 | g_loss: 0.6731
Epoch [   10/   50] | d_loss: 0.1428 | g_loss: 0.8584
Epoch [   10/   50] | d_loss: 0.2518 | g_loss: 0.5999
Epoch [   10/   50] | d_loss: 0.3870 | g_loss: 0.6541
Epoch [   10/   50] | d_loss: 0.2552 | g_loss: 1.0219
Epoch [   10/   50] | d_loss: 0.1162 | g_loss: 0.2819
Epoch [   10/   50] | d_loss: 0.1305 | g_loss: 0.6311
Epoch [   10/   50] | d_loss: 0.2106 | g_loss: 0.5149
Epoch [   10/   50] | d_loss: 0.1470 | g_loss: 0.7037
Epoch [   10/   50] | d_loss: 0.2728 | g_loss: 0.6692
Epoch [   10/   50] | d_loss: 0.3075 | g_loss: 0.9300
Epoch [   10/   50] | d_loss: 0.1377 | g_loss: 0.5974
Epoch [   10/   50] | d_loss: 0.2363 | g_loss: 0.7422
Epoch [   10/   50] | d_loss: 0.1461 | g_loss: 0.8910
Epoch [   10/   50] | d_loss: 0.2076 | g_loss: 0.4937
Epoch [   10/   50] | d_loss: 0.1080 | g_loss: 0.7985
Epoch [   10/   50] | d_loss: 0.2271 | g_loss: 0.4820
Epoch [   10/   50] | d_loss: 0.2944 | g_loss: 0.5917
Epoch [   10/   50] | d_loss: 0.1447 | g_loss: 0.6631
Epoch [   10/   50] | d_loss: 0.1362 | g_loss: 0.4417
Epoch [   10/   50] | d_loss: 0.2480 | g_loss: 0.4525
Epoch [   10/   50] | d_loss: 0.1194 | g_loss: 0.8488
Epoch [   10/   50] | d_loss: 0.2179 | g_loss: 0.7560
Epoch [   10/   50] | d_loss: 0.2152 | g_loss: 1.0037
Epoch [   10/   50] | d_loss: 0.0890 | g_loss: 1.2502
Epoch [   10/   50] | d_loss: 0.1805 | g_loss: 0.6669
Epoch [   10/   50] | d_loss: 0.1776 | g_loss: 0.6508
Epoch [   10/   50] | d_loss: 0.2324 | g_loss: 0.5152
Epoch [   10/   50] | d_loss: 0.2032 | g_loss: 0.6519
Epoch [   10/   50] | d_loss: 0.2735 | g_loss: 0.8088
Epoch [   10/   50] | d_loss: 0.2204 | g_loss: 0.5590
Epoch [   10/   50] | d_loss: 0.1453 | g_loss: 0.5857
Epoch [   10/   50] | d_loss: 0.3183 | g_loss: 0.3895
Epoch [   10/   50] | d_loss: 0.1245 | g_loss: 0.8378
Epoch [   10/   50] | d_loss: 0.1320 | g_loss: 0.4691
Epoch [   10/   50] | d_loss: 0.2188 | g_loss: 0.4660
Epoch [   10/   50] | d_loss: 0.1589 | g_loss: 0.3279
Epoch [   10/   50] | d_loss: 0.1797 | g_loss: 0.6504
Epoch [   10/   50] | d_loss: 0.3564 | g_loss: 0.6061
Epoch [   10/   50] | d_loss: 0.1772 | g_loss: 0.6453
Epoch [   10/   50] | d_loss: 0.1651 | g_loss: 0.7867
Epoch [   10/   50] | d_loss: 0.0797 | g_loss: 0.4840
Epoch [   10/   50] | d_loss: 0.2202 | g_loss: 0.3060
Epoch [   10/   50] | d_loss: 0.2306 | g_loss: 0.5339
Epoch [   10/   50] | d_loss: 0.2321 | g_loss: 0.8233
Epoch [   10/   50] | d_loss: 0.2214 | g_loss: 0.8235
Epoch [   10/   50] | d_loss: 0.1569 | g_loss: 0.3843
Epoch [   10/   50] | d_loss: 0.2062 | g_loss: 0.5830
Epoch [   10/   50] | d_loss: 0.2149 | g_loss: 0.9323
Epoch [   10/   50] | d_loss: 0.3052 | g_loss: 0.6535
Epoch [   10/   50] | d_loss: 0.2874 | g_loss: 0.4999
Epoch [   10/   50] | d_loss: 0.1373 | g_loss: 1.0751
Epoch [   10/   50] | d_loss: 0.1590 | g_loss: 1.0229
Epoch [   10/   50] | d_loss: 0.0974 | g_loss: 1.0585
Epoch [   10/   50] | d_loss: 0.2686 | g_loss: 0.4483
Epoch [   11/   50] | d_loss: 0.2832 | g_loss: 0.6870
Epoch [   11/   50] | d_loss: 0.1945 | g_loss: 0.6711
Epoch [   11/   50] | d_loss: 0.1308 | g_loss: 1.1170
Epoch [   11/   50] | d_loss: 0.2564 | g_loss: 0.8781
Epoch [   11/   50] | d_loss: 0.1774 | g_loss: 0.7217
Epoch [   11/   50] | d_loss: 0.2332 | g_loss: 0.7637
Epoch [   11/   50] | d_loss: 0.1734 | g_loss: 0.3120
Epoch [   11/   50] | d_loss: 0.1410 | g_loss: 0.6658
Epoch [   11/   50] | d_loss: 0.1882 | g_loss: 0.6349
Epoch [   11/   50] | d_loss: 0.1130 | g_loss: 0.5646
Epoch [   11/   50] | d_loss: 0.1165 | g_loss: 0.9706
Epoch [   11/   50] | d_loss: 0.2378 | g_loss: 0.6922
Epoch [   11/   50] | d_loss: 0.2543 | g_loss: 0.4005
Epoch [   11/   50] | d_loss: 0.2200 | g_loss: 0.8264
Epoch [   11/   50] | d_loss: 0.1582 | g_loss: 0.6452
Epoch [   11/   50] | d_loss: 0.2589 | g_loss: 0.7405
Epoch [   11/   50] | d_loss: 0.3778 | g_loss: 0.4409
Epoch [   11/   50] | d_loss: 0.4138 | g_loss: 0.7161
Epoch [   11/   50] | d_loss: 0.1482 | g_loss: 0.5404
Epoch [   11/   50] | d_loss: 0.1483 | g_loss: 0.8470
Epoch [   11/   50] | d_loss: 0.2454 | g_loss: 0.4586
Epoch [   11/   50] | d_loss: 0.1136 | g_loss: 0.7730
Epoch [   11/   50] | d_loss: 0.2448 | g_loss: 0.5978
Epoch [   11/   50] | d_loss: 0.3079 | g_loss: 0.7949
Epoch [   11/   50] | d_loss: 0.2341 | g_loss: 0.9931
Epoch [   11/   50] | d_loss: 0.2449 | g_loss: 0.4084
Epoch [   11/   50] | d_loss: 0.2286 | g_loss: 0.6212
Epoch [   11/   50] | d_loss: 0.3003 | g_loss: 0.5417
Epoch [   11/   50] | d_loss: 0.2983 | g_loss: 0.5827
Epoch [   11/   50] | d_loss: 0.1830 | g_loss: 0.3926
Epoch [   11/   50] | d_loss: 0.2696 | g_loss: 0.7233
Epoch [   11/   50] | d_loss: 0.2920 | g_loss: 0.4060
Epoch [   11/   50] | d_loss: 0.2050 | g_loss: 0.7584
Epoch [   11/   50] | d_loss: 0.2476 | g_loss: 0.6053
Epoch [   11/   50] | d_loss: 0.2022 | g_loss: 0.3930
Epoch [   11/   50] | d_loss: 0.1724 | g_loss: 0.6953
Epoch [   11/   50] | d_loss: 0.1889 | g_loss: 0.9832
Epoch [   11/   50] | d_loss: 0.2746 | g_loss: 0.5781
Epoch [   11/   50] | d_loss: 0.1469 | g_loss: 0.6719
Epoch [   11/   50] | d_loss: 0.1659 | g_loss: 0.7538
Epoch [   11/   50] | d_loss: 0.1825 | g_loss: 0.9473
Epoch [   11/   50] | d_loss: 0.1989 | g_loss: 0.5479
Epoch [   11/   50] | d_loss: 0.0723 | g_loss: 0.8022
Epoch [   11/   50] | d_loss: 0.2633 | g_loss: 0.7664
Epoch [   11/   50] | d_loss: 0.2420 | g_loss: 0.4987
Epoch [   11/   50] | d_loss: 0.1119 | g_loss: 0.7888
Epoch [   11/   50] | d_loss: 0.1508 | g_loss: 0.3650
Epoch [   11/   50] | d_loss: 0.1304 | g_loss: 0.8352
Epoch [   11/   50] | d_loss: 0.2464 | g_loss: 0.3240
Epoch [   11/   50] | d_loss: 0.2834 | g_loss: 0.5108
Epoch [   11/   50] | d_loss: 0.2444 | g_loss: 0.6323
Epoch [   11/   50] | d_loss: 0.2245 | g_loss: 0.6771
Epoch [   11/   50] | d_loss: 0.3051 | g_loss: 0.6308
Epoch [   11/   50] | d_loss: 0.1511 | g_loss: 0.9623
Epoch [   11/   50] | d_loss: 0.1340 | g_loss: 0.5567
Epoch [   11/   50] | d_loss: 0.0944 | g_loss: 0.5185
Epoch [   11/   50] | d_loss: 0.1614 | g_loss: 0.7151
Epoch [   12/   50] | d_loss: 0.2884 | g_loss: 0.5838
Epoch [   12/   50] | d_loss: 0.1747 | g_loss: 1.0545
Epoch [   12/   50] | d_loss: 0.2675 | g_loss: 0.4104
Epoch [   12/   50] | d_loss: 0.1286 | g_loss: 0.7979
Epoch [   12/   50] | d_loss: 0.2251 | g_loss: 0.3811
Epoch [   12/   50] | d_loss: 0.2765 | g_loss: 0.5512
Epoch [   12/   50] | d_loss: 0.3231 | g_loss: 0.4804
Epoch [   12/   50] | d_loss: 0.2374 | g_loss: 0.4487
Epoch [   12/   50] | d_loss: 0.1327 | g_loss: 0.7421
Epoch [   12/   50] | d_loss: 0.1233 | g_loss: 0.6883
Epoch [   12/   50] | d_loss: 0.1468 | g_loss: 0.7172
Epoch [   12/   50] | d_loss: 0.1877 | g_loss: 0.5256
Epoch [   12/   50] | d_loss: 0.3186 | g_loss: 0.8448
Epoch [   12/   50] | d_loss: 0.1904 | g_loss: 0.6619
Epoch [   12/   50] | d_loss: 0.2346 | g_loss: 1.0760
Epoch [   12/   50] | d_loss: 0.2322 | g_loss: 0.5665
Epoch [   12/   50] | d_loss: 0.3301 | g_loss: 0.7966
Epoch [   12/   50] | d_loss: 0.1251 | g_loss: 0.7837
Epoch [   12/   50] | d_loss: 0.3471 | g_loss: 0.4264
Epoch [   12/   50] | d_loss: 0.2307 | g_loss: 0.5768
Epoch [   12/   50] | d_loss: 0.3218 | g_loss: 0.6441
Epoch [   12/   50] | d_loss: 0.1968 | g_loss: 0.5437
Epoch [   12/   50] | d_loss: 0.2598 | g_loss: 0.6658
Epoch [   12/   50] | d_loss: 0.2110 | g_loss: 0.8080
Epoch [   12/   50] | d_loss: 0.1369 | g_loss: 0.8182
Epoch [   12/   50] | d_loss: 0.1543 | g_loss: 0.4297
Epoch [   12/   50] | d_loss: 0.2383 | g_loss: 1.0925
Epoch [   12/   50] | d_loss: 0.0855 | g_loss: 1.1166
Epoch [   12/   50] | d_loss: 0.0850 | g_loss: 0.9980
Epoch [   12/   50] | d_loss: 0.1491 | g_loss: 0.5777
Epoch [   12/   50] | d_loss: 0.1071 | g_loss: 0.6224
Epoch [   12/   50] | d_loss: 0.1629 | g_loss: 0.7088
Epoch [   12/   50] | d_loss: 0.1507 | g_loss: 0.7634
Epoch [   12/   50] | d_loss: 0.2358 | g_loss: 0.4478
Epoch [   12/   50] | d_loss: 0.2164 | g_loss: 0.5150
Epoch [   12/   50] | d_loss: 0.2330 | g_loss: 0.5660
Epoch [   12/   50] | d_loss: 0.2301 | g_loss: 0.7762
Epoch [   12/   50] | d_loss: 0.1532 | g_loss: 0.9158
Epoch [   12/   50] | d_loss: 0.2343 | g_loss: 0.6974
Epoch [   12/   50] | d_loss: 0.1927 | g_loss: 0.6456
Epoch [   12/   50] | d_loss: 0.1567 | g_loss: 0.3998
Epoch [   12/   50] | d_loss: 0.1990 | g_loss: 0.8487
Epoch [   12/   50] | d_loss: 0.2932 | g_loss: 0.7502
Epoch [   12/   50] | d_loss: 0.3733 | g_loss: 0.3972
Epoch [   12/   50] | d_loss: 0.2829 | g_loss: 0.5119
Epoch [   12/   50] | d_loss: 0.1335 | g_loss: 0.8690
Epoch [   12/   50] | d_loss: 0.1738 | g_loss: 1.1877
Epoch [   12/   50] | d_loss: 0.2219 | g_loss: 0.7713
Epoch [   12/   50] | d_loss: 0.1092 | g_loss: 1.0930
Epoch [   12/   50] | d_loss: 0.1148 | g_loss: 0.7366
Epoch [   12/   50] | d_loss: 0.2084 | g_loss: 0.6220
Epoch [   12/   50] | d_loss: 0.3887 | g_loss: 0.6635
Epoch [   12/   50] | d_loss: 0.2118 | g_loss: 0.7340
Epoch [   12/   50] | d_loss: 0.1209 | g_loss: 0.5860
Epoch [   12/   50] | d_loss: 0.1450 | g_loss: 0.7586
Epoch [   12/   50] | d_loss: 0.2158 | g_loss: 0.7763
Epoch [   12/   50] | d_loss: 0.1868 | g_loss: 0.9231
Epoch [   13/   50] | d_loss: 0.2703 | g_loss: 0.6055
Epoch [   13/   50] | d_loss: 0.2327 | g_loss: 0.6666
Epoch [   13/   50] | d_loss: 0.1863 | g_loss: 0.6280
Epoch [   13/   50] | d_loss: 0.1385 | g_loss: 0.7784
Epoch [   13/   50] | d_loss: 0.1355 | g_loss: 0.8457
Epoch [   13/   50] | d_loss: 0.0952 | g_loss: 0.8785
Epoch [   13/   50] | d_loss: 0.1440 | g_loss: 0.7083
Epoch [   13/   50] | d_loss: 0.1043 | g_loss: 0.8127
Epoch [   13/   50] | d_loss: 0.1460 | g_loss: 0.5079
Epoch [   13/   50] | d_loss: 0.2074 | g_loss: 1.1570
Epoch [   13/   50] | d_loss: 0.7563 | g_loss: 1.2470
Epoch [   13/   50] | d_loss: 0.1184 | g_loss: 0.9073
Epoch [   13/   50] | d_loss: 0.1892 | g_loss: 0.8435
Epoch [   13/   50] | d_loss: 0.1794 | g_loss: 1.1289
Epoch [   13/   50] | d_loss: 0.0899 | g_loss: 1.0440
Epoch [   13/   50] | d_loss: 0.2597 | g_loss: 0.2843
Epoch [   13/   50] | d_loss: 0.2459 | g_loss: 0.2699
Epoch [   13/   50] | d_loss: 0.2029 | g_loss: 0.7902
Epoch [   13/   50] | d_loss: 0.2317 | g_loss: 0.6576
Epoch [   13/   50] | d_loss: 0.2457 | g_loss: 0.5146
Epoch [   13/   50] | d_loss: 0.2273 | g_loss: 0.6947
Epoch [   13/   50] | d_loss: 0.2348 | g_loss: 0.9888
Epoch [   13/   50] | d_loss: 0.1468 | g_loss: 0.5260
Epoch [   13/   50] | d_loss: 0.2195 | g_loss: 0.8576
Epoch [   13/   50] | d_loss: 0.1253 | g_loss: 1.3099
Epoch [   13/   50] | d_loss: 0.1644 | g_loss: 0.5300
Epoch [   13/   50] | d_loss: 0.1688 | g_loss: 0.5274
Epoch [   13/   50] | d_loss: 0.2177 | g_loss: 0.5262
Epoch [   13/   50] | d_loss: 0.1616 | g_loss: 0.6629
Epoch [   13/   50] | d_loss: 0.2916 | g_loss: 0.6486
Epoch [   13/   50] | d_loss: 0.2130 | g_loss: 0.8019
Epoch [   13/   50] | d_loss: 0.1244 | g_loss: 1.2513
Epoch [   13/   50] | d_loss: 0.3546 | g_loss: 0.5997
Epoch [   13/   50] | d_loss: 0.3953 | g_loss: 0.5639
Epoch [   13/   50] | d_loss: 0.2543 | g_loss: 0.5963
Epoch [   13/   50] | d_loss: 0.1781 | g_loss: 0.5755
Epoch [   13/   50] | d_loss: 0.2061 | g_loss: 0.6038
Epoch [   13/   50] | d_loss: 0.1667 | g_loss: 0.8861
Epoch [   13/   50] | d_loss: 0.1723 | g_loss: 1.1758
Epoch [   13/   50] | d_loss: 0.1582 | g_loss: 0.6572
Epoch [   13/   50] | d_loss: 0.2375 | g_loss: 0.7137
Epoch [   13/   50] | d_loss: 0.2697 | g_loss: 0.6438
Epoch [   13/   50] | d_loss: 0.1557 | g_loss: 0.9929
Epoch [   13/   50] | d_loss: 0.2105 | g_loss: 0.8627
Epoch [   13/   50] | d_loss: 0.2500 | g_loss: 0.8559
Epoch [   13/   50] | d_loss: 0.1364 | g_loss: 0.9313
Epoch [   13/   50] | d_loss: 0.1441 | g_loss: 0.7070
Epoch [   13/   50] | d_loss: 0.2573 | g_loss: 0.3690
Epoch [   13/   50] | d_loss: 0.3740 | g_loss: 0.6903
Epoch [   13/   50] | d_loss: 0.2283 | g_loss: 0.5065
Epoch [   13/   50] | d_loss: 0.3377 | g_loss: 0.5699
Epoch [   13/   50] | d_loss: 0.2681 | g_loss: 1.3374
Epoch [   13/   50] | d_loss: 0.1686 | g_loss: 0.5858
Epoch [   13/   50] | d_loss: 0.1796 | g_loss: 0.6685
Epoch [   13/   50] | d_loss: 0.1874 | g_loss: 0.5846
Epoch [   13/   50] | d_loss: 0.1895 | g_loss: 0.5005
Epoch [   13/   50] | d_loss: 0.3004 | g_loss: 0.8447
Epoch [   14/   50] | d_loss: 0.1314 | g_loss: 0.4933
Epoch [   14/   50] | d_loss: 0.2565 | g_loss: 0.3203
Epoch [   14/   50] | d_loss: 0.1287 | g_loss: 0.6259
Epoch [   14/   50] | d_loss: 0.2115 | g_loss: 0.5114
Epoch [   14/   50] | d_loss: 0.3883 | g_loss: 0.9013
Epoch [   14/   50] | d_loss: 0.1721 | g_loss: 1.1022
Epoch [   14/   50] | d_loss: 0.2292 | g_loss: 0.5390
Epoch [   14/   50] | d_loss: 0.1966 | g_loss: 0.6327
Epoch [   14/   50] | d_loss: 0.1254 | g_loss: 0.9700
Epoch [   14/   50] | d_loss: 0.2826 | g_loss: 0.4222
Epoch [   14/   50] | d_loss: 0.1681 | g_loss: 0.6921
Epoch [   14/   50] | d_loss: 0.1708 | g_loss: 0.8101
Epoch [   14/   50] | d_loss: 0.2253 | g_loss: 1.0723
Epoch [   14/   50] | d_loss: 0.1714 | g_loss: 0.7859
Epoch [   14/   50] | d_loss: 0.1456 | g_loss: 0.7151
Epoch [   14/   50] | d_loss: 0.1800 | g_loss: 0.9866
Epoch [   14/   50] | d_loss: 0.2167 | g_loss: 0.5726
Epoch [   14/   50] | d_loss: 0.1335 | g_loss: 0.4542
Epoch [   14/   50] | d_loss: 0.1476 | g_loss: 0.4020
Epoch [   14/   50] | d_loss: 0.1906 | g_loss: 0.9395
Epoch [   14/   50] | d_loss: 0.1938 | g_loss: 0.7225
Epoch [   14/   50] | d_loss: 0.2396 | g_loss: 0.9566
Epoch [   14/   50] | d_loss: 0.0603 | g_loss: 1.2475
Epoch [   14/   50] | d_loss: 0.3215 | g_loss: 0.6302
Epoch [   14/   50] | d_loss: 0.2513 | g_loss: 0.7930
Epoch [   14/   50] | d_loss: 0.1356 | g_loss: 0.9709
Epoch [   14/   50] | d_loss: 0.0989 | g_loss: 0.5740
Epoch [   14/   50] | d_loss: 0.2754 | g_loss: 1.1676
Epoch [   14/   50] | d_loss: 0.1184 | g_loss: 0.9319
Epoch [   14/   50] | d_loss: 0.2348 | g_loss: 1.2243
Epoch [   14/   50] | d_loss: 0.1327 | g_loss: 0.4368
Epoch [   14/   50] | d_loss: 0.2212 | g_loss: 0.7281
Epoch [   14/   50] | d_loss: 0.1944 | g_loss: 0.8933
Epoch [   14/   50] | d_loss: 0.3052 | g_loss: 0.7265
Epoch [   14/   50] | d_loss: 0.1695 | g_loss: 1.0401
Epoch [   14/   50] | d_loss: 0.1216 | g_loss: 0.8436
Epoch [   14/   50] | d_loss: 0.3737 | g_loss: 0.7854
Epoch [   14/   50] | d_loss: 0.1479 | g_loss: 1.1177
Epoch [   14/   50] | d_loss: 0.3562 | g_loss: 0.6616
Epoch [   14/   50] | d_loss: 0.1620 | g_loss: 0.9378
Epoch [   14/   50] | d_loss: 0.1932 | g_loss: 0.4709
Epoch [   14/   50] | d_loss: 0.5088 | g_loss: 0.4213
Epoch [   14/   50] | d_loss: 0.1712 | g_loss: 0.9552
Epoch [   14/   50] | d_loss: 0.0962 | g_loss: 1.6271
Epoch [   14/   50] | d_loss: 0.1254 | g_loss: 0.6355
Epoch [   14/   50] | d_loss: 0.1675 | g_loss: 0.6543
Epoch [   14/   50] | d_loss: 0.1313 | g_loss: 0.6917
Epoch [   14/   50] | d_loss: 0.3334 | g_loss: 0.7115
Epoch [   14/   50] | d_loss: 0.1323 | g_loss: 0.8219
Epoch [   14/   50] | d_loss: 0.1316 | g_loss: 0.6987
Epoch [   14/   50] | d_loss: 0.2128 | g_loss: 0.6592
Epoch [   14/   50] | d_loss: 0.1250 | g_loss: 0.8081
Epoch [   14/   50] | d_loss: 0.1394 | g_loss: 1.1721
Epoch [   14/   50] | d_loss: 0.3319 | g_loss: 0.2380
Epoch [   14/   50] | d_loss: 0.2926 | g_loss: 0.6582
Epoch [   14/   50] | d_loss: 0.2276 | g_loss: 0.4371
Epoch [   14/   50] | d_loss: 0.1640 | g_loss: 0.6431
Epoch [   15/   50] | d_loss: 0.1193 | g_loss: 0.8902
Epoch [   15/   50] | d_loss: 0.1467 | g_loss: 0.6102
Epoch [   15/   50] | d_loss: 0.1041 | g_loss: 0.7769
Epoch [   15/   50] | d_loss: 0.2225 | g_loss: 0.6300
Epoch [   15/   50] | d_loss: 0.2888 | g_loss: 0.6752
Epoch [   15/   50] | d_loss: 0.1144 | g_loss: 1.0544
Epoch [   15/   50] | d_loss: 0.2003 | g_loss: 0.7841
Epoch [   15/   50] | d_loss: 0.4063 | g_loss: 0.5329
Epoch [   15/   50] | d_loss: 0.2038 | g_loss: 0.9335
Epoch [   15/   50] | d_loss: 0.2569 | g_loss: 0.9110
Epoch [   15/   50] | d_loss: 0.2631 | g_loss: 1.7181
Epoch [   15/   50] | d_loss: 0.2142 | g_loss: 0.7632
Epoch [   15/   50] | d_loss: 0.1806 | g_loss: 0.9534
Epoch [   15/   50] | d_loss: 0.2356 | g_loss: 0.6295
Epoch [   15/   50] | d_loss: 0.2280 | g_loss: 1.0940
Epoch [   15/   50] | d_loss: 0.1037 | g_loss: 0.7859
Epoch [   15/   50] | d_loss: 0.1656 | g_loss: 0.7204
Epoch [   15/   50] | d_loss: 0.2097 | g_loss: 1.1073
Epoch [   15/   50] | d_loss: 0.1250 | g_loss: 0.8506
Epoch [   15/   50] | d_loss: 0.1170 | g_loss: 0.6540
Epoch [   15/   50] | d_loss: 0.2327 | g_loss: 0.7559
Epoch [   15/   50] | d_loss: 0.1409 | g_loss: 0.6461
Epoch [   15/   50] | d_loss: 0.2577 | g_loss: 0.6966
Epoch [   15/   50] | d_loss: 0.1550 | g_loss: 0.3953
Epoch [   15/   50] | d_loss: 0.1904 | g_loss: 0.5973
Epoch [   15/   50] | d_loss: 0.0987 | g_loss: 0.6508
Epoch [   15/   50] | d_loss: 0.1452 | g_loss: 0.6687
Epoch [   15/   50] | d_loss: 0.1514 | g_loss: 0.9857
Epoch [   15/   50] | d_loss: 0.1255 | g_loss: 0.7133
Epoch [   15/   50] | d_loss: 0.1501 | g_loss: 0.4415
Epoch [   15/   50] | d_loss: 0.2276 | g_loss: 0.7940
Epoch [   15/   50] | d_loss: 0.1038 | g_loss: 0.9146
Epoch [   15/   50] | d_loss: 0.2218 | g_loss: 0.6834
Epoch [   15/   50] | d_loss: 0.1922 | g_loss: 1.2672
Epoch [   15/   50] | d_loss: 0.1554 | g_loss: 0.7544
Epoch [   15/   50] | d_loss: 0.2081 | g_loss: 0.3842
Epoch [   15/   50] | d_loss: 0.1409 | g_loss: 0.8384
Epoch [   15/   50] | d_loss: 0.2094 | g_loss: 0.7824
Epoch [   15/   50] | d_loss: 0.1236 | g_loss: 1.0088
Epoch [   15/   50] | d_loss: 0.2689 | g_loss: 0.5950
Epoch [   15/   50] | d_loss: 0.3397 | g_loss: 0.7030
Epoch [   15/   50] | d_loss: 0.1052 | g_loss: 1.1023
Epoch [   15/   50] | d_loss: 0.2016 | g_loss: 0.7652
Epoch [   15/   50] | d_loss: 0.1839 | g_loss: 0.7482
Epoch [   15/   50] | d_loss: 0.1288 | g_loss: 0.8233
Epoch [   15/   50] | d_loss: 0.0972 | g_loss: 1.0157
Epoch [   15/   50] | d_loss: 0.2683 | g_loss: 1.0353
Epoch [   15/   50] | d_loss: 0.1182 | g_loss: 0.8096
Epoch [   15/   50] | d_loss: 0.1848 | g_loss: 1.4849
Epoch [   15/   50] | d_loss: 0.1638 | g_loss: 0.7148
Epoch [   15/   50] | d_loss: 0.1230 | g_loss: 0.8008
Epoch [   15/   50] | d_loss: 0.1434 | g_loss: 0.7033
Epoch [   15/   50] | d_loss: 0.2327 | g_loss: 0.5749
Epoch [   15/   50] | d_loss: 0.2172 | g_loss: 0.5882
Epoch [   15/   50] | d_loss: 0.1030 | g_loss: 1.1295
Epoch [   15/   50] | d_loss: 0.1137 | g_loss: 1.3709
Epoch [   15/   50] | d_loss: 0.2465 | g_loss: 0.7694
Epoch [   16/   50] | d_loss: 0.4674 | g_loss: 0.7406
Epoch [   16/   50] | d_loss: 0.2320 | g_loss: 1.0963
Epoch [   16/   50] | d_loss: 0.0999 | g_loss: 0.9103
Epoch [   16/   50] | d_loss: 0.2320 | g_loss: 0.4154
Epoch [   16/   50] | d_loss: 0.3339 | g_loss: 0.6025
Epoch [   16/   50] | d_loss: 0.1658 | g_loss: 0.5561
Epoch [   16/   50] | d_loss: 0.1657 | g_loss: 0.4550
Epoch [   16/   50] | d_loss: 0.1021 | g_loss: 0.8809
Epoch [   16/   50] | d_loss: 0.1691 | g_loss: 1.7004
Epoch [   16/   50] | d_loss: 0.1756 | g_loss: 0.5791
Epoch [   16/   50] | d_loss: 0.3878 | g_loss: 0.7052
Epoch [   16/   50] | d_loss: 0.2032 | g_loss: 0.7159
Epoch [   16/   50] | d_loss: 0.1164 | g_loss: 0.6155
Epoch [   16/   50] | d_loss: 0.1836 | g_loss: 0.4723
Epoch [   16/   50] | d_loss: 0.1374 | g_loss: 0.3487
Epoch [   16/   50] | d_loss: 0.2524 | g_loss: 0.5491
Epoch [   16/   50] | d_loss: 0.3503 | g_loss: 0.4353
Epoch [   16/   50] | d_loss: 0.1783 | g_loss: 0.5809
Epoch [   16/   50] | d_loss: 0.1161 | g_loss: 1.2721
Epoch [   16/   50] | d_loss: 0.2418 | g_loss: 0.6191
Epoch [   16/   50] | d_loss: 0.1972 | g_loss: 0.4702
Epoch [   16/   50] | d_loss: 0.1751 | g_loss: 1.0038
Epoch [   16/   50] | d_loss: 0.1160 | g_loss: 0.7401
Epoch [   16/   50] | d_loss: 0.2089 | g_loss: 0.9424
Epoch [   16/   50] | d_loss: 0.1809 | g_loss: 0.8546
Epoch [   16/   50] | d_loss: 0.1582 | g_loss: 0.2745
Epoch [   16/   50] | d_loss: 0.2283 | g_loss: 0.4465
Epoch [   16/   50] | d_loss: 0.2190 | g_loss: 0.8564
Epoch [   16/   50] | d_loss: 0.1774 | g_loss: 0.7317
Epoch [   16/   50] | d_loss: 0.0693 | g_loss: 0.5243
Epoch [   16/   50] | d_loss: 0.2173 | g_loss: 0.9554
Epoch [   16/   50] | d_loss: 0.1081 | g_loss: 0.6305
Epoch [   16/   50] | d_loss: 0.1862 | g_loss: 0.7268
Epoch [   16/   50] | d_loss: 0.2102 | g_loss: 0.6751
Epoch [   16/   50] | d_loss: 0.1538 | g_loss: 0.4494
Epoch [   16/   50] | d_loss: 0.1670 | g_loss: 0.6105
Epoch [   16/   50] | d_loss: 0.2644 | g_loss: 1.1829
Epoch [   16/   50] | d_loss: 0.1603 | g_loss: 0.7660
Epoch [   16/   50] | d_loss: 0.0793 | g_loss: 0.5427
Epoch [   16/   50] | d_loss: 0.1415 | g_loss: 0.5572
Epoch [   16/   50] | d_loss: 0.1418 | g_loss: 0.9167
Epoch [   16/   50] | d_loss: 0.1215 | g_loss: 0.4677
Epoch [   16/   50] | d_loss: 0.1510 | g_loss: 0.7001
Epoch [   16/   50] | d_loss: 0.1649 | g_loss: 0.6564
Epoch [   16/   50] | d_loss: 0.1719 | g_loss: 0.8416
Epoch [   16/   50] | d_loss: 0.2874 | g_loss: 0.4984
Epoch [   16/   50] | d_loss: 0.2087 | g_loss: 0.6762
Epoch [   16/   50] | d_loss: 0.0510 | g_loss: 0.7960
Epoch [   16/   50] | d_loss: 0.1648 | g_loss: 0.6628
Epoch [   16/   50] | d_loss: 0.1116 | g_loss: 0.7430
Epoch [   16/   50] | d_loss: 0.3147 | g_loss: 0.4802
Epoch [   16/   50] | d_loss: 0.1660 | g_loss: 0.9728
Epoch [   16/   50] | d_loss: 0.2061 | g_loss: 0.8819
Epoch [   16/   50] | d_loss: 0.1659 | g_loss: 0.8405
Epoch [   16/   50] | d_loss: 0.3802 | g_loss: 0.3583
Epoch [   16/   50] | d_loss: 0.1604 | g_loss: 0.6645
Epoch [   16/   50] | d_loss: 0.1482 | g_loss: 0.9530
Epoch [   17/   50] | d_loss: 0.2204 | g_loss: 0.8851
Epoch [   17/   50] | d_loss: 0.1531 | g_loss: 0.9688
Epoch [   17/   50] | d_loss: 0.1766 | g_loss: 0.5875
Epoch [   17/   50] | d_loss: 0.1702 | g_loss: 0.6139
Epoch [   17/   50] | d_loss: 0.1017 | g_loss: 0.8040
Epoch [   17/   50] | d_loss: 0.2257 | g_loss: 0.6021
Epoch [   17/   50] | d_loss: 0.1833 | g_loss: 0.7676
Epoch [   17/   50] | d_loss: 0.1174 | g_loss: 1.0908
Epoch [   17/   50] | d_loss: 0.2351 | g_loss: 0.5691
Epoch [   17/   50] | d_loss: 0.2440 | g_loss: 0.5381
Epoch [   17/   50] | d_loss: 0.1242 | g_loss: 0.7314
Epoch [   17/   50] | d_loss: 0.1555 | g_loss: 0.8029
Epoch [   17/   50] | d_loss: 0.0552 | g_loss: 0.9999
Epoch [   17/   50] | d_loss: 0.3636 | g_loss: 0.2561
Epoch [   17/   50] | d_loss: 0.2260 | g_loss: 0.4665
Epoch [   17/   50] | d_loss: 0.2557 | g_loss: 0.7782
Epoch [   17/   50] | d_loss: 0.1554 | g_loss: 0.3310
Epoch [   17/   50] | d_loss: 0.1729 | g_loss: 0.5186
Epoch [   17/   50] | d_loss: 0.0909 | g_loss: 0.6112
Epoch [   17/   50] | d_loss: 0.1238 | g_loss: 0.4426
Epoch [   17/   50] | d_loss: 0.2207 | g_loss: 0.9353
Epoch [   17/   50] | d_loss: 0.1334 | g_loss: 0.7399
Epoch [   17/   50] | d_loss: 0.2044 | g_loss: 1.0445
Epoch [   17/   50] | d_loss: 0.3988 | g_loss: 0.5734
Epoch [   17/   50] | d_loss: 0.1517 | g_loss: 0.7263
Epoch [   17/   50] | d_loss: 0.1310 | g_loss: 0.6352
Epoch [   17/   50] | d_loss: 0.1709 | g_loss: 0.7890
Epoch [   17/   50] | d_loss: 0.1774 | g_loss: 1.3558
Epoch [   17/   50] | d_loss: 0.1136 | g_loss: 0.4260
Epoch [   17/   50] | d_loss: 0.0972 | g_loss: 0.8044
Epoch [   17/   50] | d_loss: 0.1037 | g_loss: 0.5680
Epoch [   17/   50] | d_loss: 0.2027 | g_loss: 0.8655
Epoch [   17/   50] | d_loss: 0.2078 | g_loss: 0.6004
Epoch [   17/   50] | d_loss: 0.1544 | g_loss: 0.9064
Epoch [   17/   50] | d_loss: 0.3581 | g_loss: 1.3572
Epoch [   17/   50] | d_loss: 0.1421 | g_loss: 0.9028
Epoch [   17/   50] | d_loss: 0.1117 | g_loss: 0.8022
Epoch [   17/   50] | d_loss: 0.0908 | g_loss: 0.8091
Epoch [   17/   50] | d_loss: 0.1340 | g_loss: 0.5548
Epoch [   17/   50] | d_loss: 0.0987 | g_loss: 0.3145
Epoch [   17/   50] | d_loss: 0.2108 | g_loss: 0.5856
Epoch [   17/   50] | d_loss: 0.1538 | g_loss: 0.7568
Epoch [   17/   50] | d_loss: 0.1176 | g_loss: 1.1907
Epoch [   17/   50] | d_loss: 0.1742 | g_loss: 0.5819
Epoch [   17/   50] | d_loss: 0.2470 | g_loss: 0.4801
Epoch [   17/   50] | d_loss: 0.0861 | g_loss: 1.0696
Epoch [   17/   50] | d_loss: 0.2215 | g_loss: 0.5530
Epoch [   17/   50] | d_loss: 0.2744 | g_loss: 0.9146
Epoch [   17/   50] | d_loss: 0.3011 | g_loss: 0.9541
Epoch [   17/   50] | d_loss: 0.2021 | g_loss: 0.4258
Epoch [   17/   50] | d_loss: 0.1328 | g_loss: 0.5411
Epoch [   17/   50] | d_loss: 0.0920 | g_loss: 0.7324
Epoch [   17/   50] | d_loss: 0.1719 | g_loss: 0.6538
Epoch [   17/   50] | d_loss: 0.1524 | g_loss: 0.8321
Epoch [   17/   50] | d_loss: 0.3953 | g_loss: 0.2984
Epoch [   17/   50] | d_loss: 0.1011 | g_loss: 0.8042
Epoch [   17/   50] | d_loss: 0.1555 | g_loss: 0.8265
Epoch [   18/   50] | d_loss: 0.1652 | g_loss: 1.0174
Epoch [   18/   50] | d_loss: 0.1891 | g_loss: 0.8885
Epoch [   18/   50] | d_loss: 0.1716 | g_loss: 0.8538
Epoch [   18/   50] | d_loss: 0.1901 | g_loss: 0.6766
Epoch [   18/   50] | d_loss: 0.2268 | g_loss: 0.4757
Epoch [   18/   50] | d_loss: 0.1465 | g_loss: 0.5540
Epoch [   18/   50] | d_loss: 0.0921 | g_loss: 0.8788
Epoch [   18/   50] | d_loss: 0.0925 | g_loss: 0.8068
Epoch [   18/   50] | d_loss: 0.0520 | g_loss: 0.9143
Epoch [   18/   50] | d_loss: 0.2516 | g_loss: 0.8084
Epoch [   18/   50] | d_loss: 0.0968 | g_loss: 0.8497
Epoch [   18/   50] | d_loss: 0.1147 | g_loss: 0.7159
Epoch [   18/   50] | d_loss: 0.2073 | g_loss: 0.7637
Epoch [   18/   50] | d_loss: 0.1665 | g_loss: 0.4547
Epoch [   18/   50] | d_loss: 0.3356 | g_loss: 0.9590
Epoch [   18/   50] | d_loss: 0.1992 | g_loss: 0.5791
Epoch [   18/   50] | d_loss: 0.1085 | g_loss: 0.7625
Epoch [   18/   50] | d_loss: 0.1479 | g_loss: 0.8881
Epoch [   18/   50] | d_loss: 0.1909 | g_loss: 0.6680
Epoch [   18/   50] | d_loss: 0.0885 | g_loss: 0.6748
Epoch [   18/   50] | d_loss: 0.1274 | g_loss: 0.9651
Epoch [   18/   50] | d_loss: 0.3384 | g_loss: 0.5386
Epoch [   18/   50] | d_loss: 0.2389 | g_loss: 0.9476
Epoch [   18/   50] | d_loss: 0.0996 | g_loss: 0.7550
Epoch [   18/   50] | d_loss: 0.1806 | g_loss: 0.5776
Epoch [   18/   50] | d_loss: 0.1337 | g_loss: 0.4823
Epoch [   18/   50] | d_loss: 0.2062 | g_loss: 1.0806
Epoch [   18/   50] | d_loss: 0.1712 | g_loss: 0.6130
Epoch [   18/   50] | d_loss: 0.1387 | g_loss: 0.8565
Epoch [   18/   50] | d_loss: 0.3246 | g_loss: 1.1436
Epoch [   18/   50] | d_loss: 0.0766 | g_loss: 1.0237
Epoch [   18/   50] | d_loss: 0.1134 | g_loss: 0.8525
Epoch [   18/   50] | d_loss: 0.2189 | g_loss: 0.4962
Epoch [   18/   50] | d_loss: 0.1071 | g_loss: 0.4871
Epoch [   18/   50] | d_loss: 0.1657 | g_loss: 0.6007
Epoch [   18/   50] | d_loss: 0.1718 | g_loss: 1.0627
Epoch [   18/   50] | d_loss: 0.1348 | g_loss: 0.7668
Epoch [   18/   50] | d_loss: 0.2055 | g_loss: 1.0626
Epoch [   18/   50] | d_loss: 0.1374 | g_loss: 1.0353
Epoch [   18/   50] | d_loss: 0.1507 | g_loss: 0.7606
Epoch [   18/   50] | d_loss: 0.1133 | g_loss: 0.7226
Epoch [   18/   50] | d_loss: 0.2454 | g_loss: 0.5056
Epoch [   18/   50] | d_loss: 0.1031 | g_loss: 0.6616
Epoch [   18/   50] | d_loss: 0.2052 | g_loss: 0.7079
Epoch [   18/   50] | d_loss: 0.1688 | g_loss: 0.8118
Epoch [   18/   50] | d_loss: 0.0863 | g_loss: 0.7306
Epoch [   18/   50] | d_loss: 0.0855 | g_loss: 0.8845
Epoch [   18/   50] | d_loss: 0.2131 | g_loss: 0.5341
Epoch [   18/   50] | d_loss: 0.2487 | g_loss: 0.3618
Epoch [   18/   50] | d_loss: 0.0876 | g_loss: 1.0138
Epoch [   18/   50] | d_loss: 0.1895 | g_loss: 0.5663
Epoch [   18/   50] | d_loss: 0.2157 | g_loss: 0.7169
Epoch [   18/   50] | d_loss: 0.1160 | g_loss: 0.6519
Epoch [   18/   50] | d_loss: 0.1162 | g_loss: 1.1405
Epoch [   18/   50] | d_loss: 0.1709 | g_loss: 1.0122
Epoch [   18/   50] | d_loss: 0.2077 | g_loss: 0.5585
Epoch [   18/   50] | d_loss: 0.1220 | g_loss: 1.2153
Epoch [   19/   50] | d_loss: 0.1310 | g_loss: 1.0145
Epoch [   19/   50] | d_loss: 0.0679 | g_loss: 0.8935
Epoch [   19/   50] | d_loss: 0.1376 | g_loss: 0.7445
Epoch [   19/   50] | d_loss: 0.2219 | g_loss: 0.8868
Epoch [   19/   50] | d_loss: 0.0903 | g_loss: 0.6097
Epoch [   19/   50] | d_loss: 0.1343 | g_loss: 0.6412
Epoch [   19/   50] | d_loss: 0.0816 | g_loss: 1.1630
Epoch [   19/   50] | d_loss: 0.2243 | g_loss: 0.7141
Epoch [   19/   50] | d_loss: 0.1067 | g_loss: 0.5733
Epoch [   19/   50] | d_loss: 0.0810 | g_loss: 0.7550
Epoch [   19/   50] | d_loss: 0.1104 | g_loss: 1.1718
Epoch [   19/   50] | d_loss: 0.1177 | g_loss: 0.9199
Epoch [   19/   50] | d_loss: 0.1910 | g_loss: 0.7894
Epoch [   19/   50] | d_loss: 0.1071 | g_loss: 0.8686
Epoch [   19/   50] | d_loss: 0.0813 | g_loss: 1.1912
Epoch [   19/   50] | d_loss: 0.1144 | g_loss: 0.8835
Epoch [   19/   50] | d_loss: 0.1807 | g_loss: 0.8167
Epoch [   19/   50] | d_loss: 0.1349 | g_loss: 0.7806
Epoch [   19/   50] | d_loss: 0.0906 | g_loss: 0.7055
Epoch [   19/   50] | d_loss: 0.1767 | g_loss: 1.3355
Epoch [   19/   50] | d_loss: 0.2404 | g_loss: 0.9196
Epoch [   19/   50] | d_loss: 0.1324 | g_loss: 0.4325
Epoch [   19/   50] | d_loss: 0.1013 | g_loss: 1.0236
Epoch [   19/   50] | d_loss: 0.2335 | g_loss: 0.9775
Epoch [   19/   50] | d_loss: 0.1468 | g_loss: 0.4372
Epoch [   19/   50] | d_loss: 0.0969 | g_loss: 0.8509
Epoch [   19/   50] | d_loss: 0.1108 | g_loss: 0.5408
Epoch [   19/   50] | d_loss: 0.1871 | g_loss: 0.7131
Epoch [   19/   50] | d_loss: 0.2060 | g_loss: 0.4830
Epoch [   19/   50] | d_loss: 0.2310 | g_loss: 1.8066
Epoch [   19/   50] | d_loss: 0.1694 | g_loss: 0.8530
Epoch [   19/   50] | d_loss: 0.0918 | g_loss: 0.6896
Epoch [   19/   50] | d_loss: 0.3830 | g_loss: 0.4018
Epoch [   19/   50] | d_loss: 0.1044 | g_loss: 0.6764
Epoch [   19/   50] | d_loss: 0.1585 | g_loss: 0.7384
Epoch [   19/   50] | d_loss: 0.2192 | g_loss: 0.6072
Epoch [   19/   50] | d_loss: 0.1126 | g_loss: 0.6314
Epoch [   19/   50] | d_loss: 0.4041 | g_loss: 0.7950
Epoch [   19/   50] | d_loss: 0.1209 | g_loss: 1.2605
Epoch [   19/   50] | d_loss: 0.1965 | g_loss: 0.7208
Epoch [   19/   50] | d_loss: 0.1479 | g_loss: 1.0729
Epoch [   19/   50] | d_loss: 0.1496 | g_loss: 1.1909
Epoch [   19/   50] | d_loss: 0.1821 | g_loss: 0.6744
Epoch [   19/   50] | d_loss: 0.1581 | g_loss: 0.8585
Epoch [   19/   50] | d_loss: 0.1117 | g_loss: 1.2584
Epoch [   19/   50] | d_loss: 0.1253 | g_loss: 0.6938
Epoch [   19/   50] | d_loss: 0.3206 | g_loss: 0.5978
Epoch [   19/   50] | d_loss: 0.1090 | g_loss: 0.9803
Epoch [   19/   50] | d_loss: 0.2191 | g_loss: 0.7663
Epoch [   19/   50] | d_loss: 0.3171 | g_loss: 0.4154
Epoch [   19/   50] | d_loss: 0.1693 | g_loss: 0.5947
Epoch [   19/   50] | d_loss: 0.1448 | g_loss: 0.7699
Epoch [   19/   50] | d_loss: 0.0732 | g_loss: 0.7942
Epoch [   19/   50] | d_loss: 0.3387 | g_loss: 0.8888
Epoch [   19/   50] | d_loss: 0.2583 | g_loss: 0.6921
Epoch [   19/   50] | d_loss: 0.4441 | g_loss: 0.7341
Epoch [   19/   50] | d_loss: 0.2615 | g_loss: 1.3814
Epoch [   20/   50] | d_loss: 0.2676 | g_loss: 1.3167
Epoch [   20/   50] | d_loss: 0.2328 | g_loss: 0.7025
Epoch [   20/   50] | d_loss: 0.0968 | g_loss: 0.5724
Epoch [   20/   50] | d_loss: 0.1833 | g_loss: 0.3735
Epoch [   20/   50] | d_loss: 0.2282 | g_loss: 0.7082
Epoch [   20/   50] | d_loss: 0.0838 | g_loss: 0.9231
Epoch [   20/   50] | d_loss: 0.1172 | g_loss: 0.6535
Epoch [   20/   50] | d_loss: 0.1958 | g_loss: 0.9364
Epoch [   20/   50] | d_loss: 0.2277 | g_loss: 1.0056
Epoch [   20/   50] | d_loss: 0.1578 | g_loss: 0.5321
Epoch [   20/   50] | d_loss: 0.2784 | g_loss: 0.5821
Epoch [   20/   50] | d_loss: 0.1052 | g_loss: 0.5631
Epoch [   20/   50] | d_loss: 0.2028 | g_loss: 0.5218
Epoch [   20/   50] | d_loss: 0.2074 | g_loss: 0.8011
Epoch [   20/   50] | d_loss: 0.1115 | g_loss: 0.6773
Epoch [   20/   50] | d_loss: 0.0992 | g_loss: 0.5418
Epoch [   20/   50] | d_loss: 0.1605 | g_loss: 1.2567
Epoch [   20/   50] | d_loss: 0.0986 | g_loss: 0.7782
Epoch [   20/   50] | d_loss: 0.2515 | g_loss: 0.8075
Epoch [   20/   50] | d_loss: 0.1486 | g_loss: 0.6732
Epoch [   20/   50] | d_loss: 0.1292 | g_loss: 0.6617
Epoch [   20/   50] | d_loss: 0.2190 | g_loss: 0.9130
Epoch [   20/   50] | d_loss: 0.2370 | g_loss: 0.7667
Epoch [   20/   50] | d_loss: 0.2497 | g_loss: 0.8480
Epoch [   20/   50] | d_loss: 0.1635 | g_loss: 0.9114
Epoch [   20/   50] | d_loss: 0.1850 | g_loss: 0.9099
Epoch [   20/   50] | d_loss: 0.1744 | g_loss: 0.8371
Epoch [   20/   50] | d_loss: 0.2407 | g_loss: 0.7131
Epoch [   20/   50] | d_loss: 0.3160 | g_loss: 1.1323
Epoch [   20/   50] | d_loss: 0.1148 | g_loss: 0.9384
Epoch [   20/   50] | d_loss: 0.1287 | g_loss: 0.8571
Epoch [   20/   50] | d_loss: 0.1125 | g_loss: 0.7716
Epoch [   20/   50] | d_loss: 0.1114 | g_loss: 1.1187
Epoch [   20/   50] | d_loss: 0.0908 | g_loss: 1.0150
Epoch [   20/   50] | d_loss: 0.2293 | g_loss: 0.8626
Epoch [   20/   50] | d_loss: 0.4621 | g_loss: 0.5428
Epoch [   20/   50] | d_loss: 0.1330 | g_loss: 0.8388
Epoch [   20/   50] | d_loss: 0.1134 | g_loss: 0.7443
Epoch [   20/   50] | d_loss: 0.1837 | g_loss: 0.9013
Epoch [   20/   50] | d_loss: 0.1278 | g_loss: 1.1350
Epoch [   20/   50] | d_loss: 0.1394 | g_loss: 0.3523
Epoch [   20/   50] | d_loss: 0.0851 | g_loss: 1.0333
Epoch [   20/   50] | d_loss: 0.1503 | g_loss: 0.9126
Epoch [   20/   50] | d_loss: 0.1519 | g_loss: 1.0101
Epoch [   20/   50] | d_loss: 0.1543 | g_loss: 0.7232
Epoch [   20/   50] | d_loss: 0.1144 | g_loss: 0.9099
Epoch [   20/   50] | d_loss: 0.1789 | g_loss: 0.8681
Epoch [   20/   50] | d_loss: 0.1029 | g_loss: 1.0524
Epoch [   20/   50] | d_loss: 0.2959 | g_loss: 0.9372
Epoch [   20/   50] | d_loss: 0.0484 | g_loss: 1.0435
Epoch [   20/   50] | d_loss: 0.1633 | g_loss: 0.8272
Epoch [   20/   50] | d_loss: 0.3213 | g_loss: 0.4260
Epoch [   20/   50] | d_loss: 0.1765 | g_loss: 0.6349
Epoch [   20/   50] | d_loss: 0.2170 | g_loss: 0.6994
Epoch [   20/   50] | d_loss: 0.1501 | g_loss: 0.6131
Epoch [   20/   50] | d_loss: 0.1938 | g_loss: 0.5587
Epoch [   20/   50] | d_loss: 0.1035 | g_loss: 1.2041
Epoch [   21/   50] | d_loss: 0.2430 | g_loss: 0.8597
Epoch [   21/   50] | d_loss: 0.2814 | g_loss: 1.0365
Epoch [   21/   50] | d_loss: 0.0772 | g_loss: 1.2229
Epoch [   21/   50] | d_loss: 0.1357 | g_loss: 0.8012
Epoch [   21/   50] | d_loss: 0.1661 | g_loss: 0.8336
Epoch [   21/   50] | d_loss: 0.2065 | g_loss: 0.5001
Epoch [   21/   50] | d_loss: 0.1182 | g_loss: 0.7329
Epoch [   21/   50] | d_loss: 0.1433 | g_loss: 0.6428
Epoch [   21/   50] | d_loss: 0.2770 | g_loss: 0.6713
Epoch [   21/   50] | d_loss: 0.1926 | g_loss: 0.8761
Epoch [   21/   50] | d_loss: 0.1423 | g_loss: 0.8203
Epoch [   21/   50] | d_loss: 0.1351 | g_loss: 0.7421
Epoch [   21/   50] | d_loss: 0.3502 | g_loss: 1.2178
Epoch [   21/   50] | d_loss: 0.1507 | g_loss: 0.6961
Epoch [   21/   50] | d_loss: 0.1956 | g_loss: 0.6590
Epoch [   21/   50] | d_loss: 0.2224 | g_loss: 0.4106
Epoch [   21/   50] | d_loss: 0.2196 | g_loss: 1.3170
Epoch [   21/   50] | d_loss: 0.1096 | g_loss: 0.2526
Epoch [   21/   50] | d_loss: 0.1643 | g_loss: 0.7053
Epoch [   21/   50] | d_loss: 0.1231 | g_loss: 0.9559
Epoch [   21/   50] | d_loss: 0.1886 | g_loss: 0.5261
Epoch [   21/   50] | d_loss: 0.1899 | g_loss: 0.9762
Epoch [   21/   50] | d_loss: 0.0726 | g_loss: 1.0739
Epoch [   21/   50] | d_loss: 0.1794 | g_loss: 0.4660
Epoch [   21/   50] | d_loss: 0.2984 | g_loss: 0.9503
Epoch [   21/   50] | d_loss: 0.0886 | g_loss: 1.1396
Epoch [   21/   50] | d_loss: 0.1799 | g_loss: 1.1011
Epoch [   21/   50] | d_loss: 0.2079 | g_loss: 0.8304
Epoch [   21/   50] | d_loss: 0.1501 | g_loss: 0.5114
Epoch [   21/   50] | d_loss: 0.2860 | g_loss: 0.6261
Epoch [   21/   50] | d_loss: 0.1094 | g_loss: 0.9025
Epoch [   21/   50] | d_loss: 0.0879 | g_loss: 1.0118
Epoch [   21/   50] | d_loss: 0.2661 | g_loss: 0.8829
Epoch [   21/   50] | d_loss: 0.1587 | g_loss: 0.3876
Epoch [   21/   50] | d_loss: 0.2415 | g_loss: 0.7415
Epoch [   21/   50] | d_loss: 0.1452 | g_loss: 0.5605
Epoch [   21/   50] | d_loss: 0.2962 | g_loss: 0.6605
Epoch [   21/   50] | d_loss: 0.1400 | g_loss: 0.6229
Epoch [   21/   50] | d_loss: 0.1724 | g_loss: 0.6754
Epoch [   21/   50] | d_loss: 0.1496 | g_loss: 0.7312
Epoch [   21/   50] | d_loss: 0.1715 | g_loss: 0.5661
Epoch [   21/   50] | d_loss: 0.1150 | g_loss: 0.8791
Epoch [   21/   50] | d_loss: 0.1921 | g_loss: 0.7146
Epoch [   21/   50] | d_loss: 0.3572 | g_loss: 0.3386
Epoch [   21/   50] | d_loss: 0.2204 | g_loss: 0.7413
Epoch [   21/   50] | d_loss: 0.1056 | g_loss: 0.5047
Epoch [   21/   50] | d_loss: 0.2449 | g_loss: 0.7040
Epoch [   21/   50] | d_loss: 0.0831 | g_loss: 0.8752
Epoch [   21/   50] | d_loss: 0.1117 | g_loss: 0.6817
Epoch [   21/   50] | d_loss: 0.2683 | g_loss: 0.6528
Epoch [   21/   50] | d_loss: 0.2025 | g_loss: 0.9899
Epoch [   21/   50] | d_loss: 0.1880 | g_loss: 0.7949
Epoch [   21/   50] | d_loss: 0.2237 | g_loss: 0.8300
Epoch [   21/   50] | d_loss: 0.1280 | g_loss: 0.5051
Epoch [   21/   50] | d_loss: 0.1075 | g_loss: 0.9542
Epoch [   21/   50] | d_loss: 0.0712 | g_loss: 1.3467
Epoch [   21/   50] | d_loss: 0.1307 | g_loss: 0.7986
Epoch [   22/   50] | d_loss: 0.2257 | g_loss: 0.5699
Epoch [   22/   50] | d_loss: 0.0886 | g_loss: 1.2121
Epoch [   22/   50] | d_loss: 0.1364 | g_loss: 0.8903
Epoch [   22/   50] | d_loss: 0.0816 | g_loss: 1.1152
Epoch [   22/   50] | d_loss: 0.1460 | g_loss: 0.9601
Epoch [   22/   50] | d_loss: 0.2859 | g_loss: 0.9771
Epoch [   22/   50] | d_loss: 0.1981 | g_loss: 0.4691
Epoch [   22/   50] | d_loss: 0.2860 | g_loss: 0.7324
Epoch [   22/   50] | d_loss: 0.0836 | g_loss: 0.5644
Epoch [   22/   50] | d_loss: 0.1022 | g_loss: 0.9147
Epoch [   22/   50] | d_loss: 0.1377 | g_loss: 0.7668
Epoch [   22/   50] | d_loss: 0.1468 | g_loss: 0.6175
Epoch [   22/   50] | d_loss: 0.2111 | g_loss: 0.9313
Epoch [   22/   50] | d_loss: 0.1251 | g_loss: 1.0041
Epoch [   22/   50] | d_loss: 0.1323 | g_loss: 0.8193
Epoch [   22/   50] | d_loss: 0.1090 | g_loss: 1.1179
Epoch [   22/   50] | d_loss: 0.1506 | g_loss: 0.5058
Epoch [   22/   50] | d_loss: 0.2118 | g_loss: 0.6691
Epoch [   22/   50] | d_loss: 0.1134 | g_loss: 0.5219
Epoch [   22/   50] | d_loss: 0.1783 | g_loss: 0.7608
Epoch [   22/   50] | d_loss: 0.1025 | g_loss: 1.0239
Epoch [   22/   50] | d_loss: 0.1806 | g_loss: 0.7079
Epoch [   22/   50] | d_loss: 0.2577 | g_loss: 0.9509
Epoch [   22/   50] | d_loss: 0.1728 | g_loss: 0.6626
Epoch [   22/   50] | d_loss: 0.1389 | g_loss: 0.6198
Epoch [   22/   50] | d_loss: 0.3532 | g_loss: 0.6497
Epoch [   22/   50] | d_loss: 0.3459 | g_loss: 0.6760
Epoch [   22/   50] | d_loss: 0.1876 | g_loss: 0.6897
Epoch [   22/   50] | d_loss: 0.1483 | g_loss: 1.5658
Epoch [   22/   50] | d_loss: 0.1616 | g_loss: 0.6997
Epoch [   22/   50] | d_loss: 0.0793 | g_loss: 0.5408
Epoch [   22/   50] | d_loss: 0.1321 | g_loss: 0.6247
Epoch [   22/   50] | d_loss: 0.2050 | g_loss: 0.3742
Epoch [   22/   50] | d_loss: 0.1311 | g_loss: 0.8837
Epoch [   22/   50] | d_loss: 0.0808 | g_loss: 0.5782
Epoch [   22/   50] | d_loss: 0.1630 | g_loss: 0.7352
Epoch [   22/   50] | d_loss: 0.0944 | g_loss: 0.8015
Epoch [   22/   50] | d_loss: 0.2153 | g_loss: 0.4902
Epoch [   22/   50] | d_loss: 0.1311 | g_loss: 0.5609
Epoch [   22/   50] | d_loss: 0.0947 | g_loss: 1.3199
Epoch [   22/   50] | d_loss: 0.1116 | g_loss: 1.2103
Epoch [   22/   50] | d_loss: 0.1749 | g_loss: 0.7134
Epoch [   22/   50] | d_loss: 0.1001 | g_loss: 0.8474
Epoch [   22/   50] | d_loss: 0.0776 | g_loss: 0.8652
Epoch [   22/   50] | d_loss: 0.0424 | g_loss: 0.8941
Epoch [   22/   50] | d_loss: 0.1304 | g_loss: 0.4013
Epoch [   22/   50] | d_loss: 0.0849 | g_loss: 1.0502
Epoch [   22/   50] | d_loss: 0.1712 | g_loss: 0.6106
Epoch [   22/   50] | d_loss: 0.1411 | g_loss: 0.8187
Epoch [   22/   50] | d_loss: 0.1215 | g_loss: 0.7104
Epoch [   22/   50] | d_loss: 0.0863 | g_loss: 0.9307
Epoch [   22/   50] | d_loss: 0.0758 | g_loss: 1.2214
Epoch [   22/   50] | d_loss: 0.1218 | g_loss: 0.8446
Epoch [   22/   50] | d_loss: 0.1900 | g_loss: 0.8627
Epoch [   22/   50] | d_loss: 0.1188 | g_loss: 0.4764
Epoch [   22/   50] | d_loss: 0.1255 | g_loss: 0.5675
Epoch [   22/   50] | d_loss: 0.1103 | g_loss: 0.8168
Epoch [   23/   50] | d_loss: 0.3327 | g_loss: 1.0904
Epoch [   23/   50] | d_loss: 0.1186 | g_loss: 0.8344
Epoch [   23/   50] | d_loss: 0.1457 | g_loss: 0.8886
Epoch [   23/   50] | d_loss: 0.1180 | g_loss: 0.6791
Epoch [   23/   50] | d_loss: 0.0918 | g_loss: 0.9238
Epoch [   23/   50] | d_loss: 0.1887 | g_loss: 0.7126
Epoch [   23/   50] | d_loss: 0.1169 | g_loss: 0.8403
Epoch [   23/   50] | d_loss: 0.2466 | g_loss: 0.3178
Epoch [   23/   50] | d_loss: 0.2221 | g_loss: 1.0497
Epoch [   23/   50] | d_loss: 0.0687 | g_loss: 0.8669
Epoch [   23/   50] | d_loss: 0.0927 | g_loss: 1.2496
Epoch [   23/   50] | d_loss: 0.3422 | g_loss: 0.4956
Epoch [   23/   50] | d_loss: 0.0993 | g_loss: 1.3514
Epoch [   23/   50] | d_loss: 0.1214 | g_loss: 0.7440
Epoch [   23/   50] | d_loss: 0.1006 | g_loss: 0.6024
Epoch [   23/   50] | d_loss: 0.0899 | g_loss: 0.6974
Epoch [   23/   50] | d_loss: 0.2121 | g_loss: 0.9816
Epoch [   23/   50] | d_loss: 0.1278 | g_loss: 0.9947
Epoch [   23/   50] | d_loss: 0.2170 | g_loss: 0.5411
Epoch [   23/   50] | d_loss: 0.1308 | g_loss: 0.5383
Epoch [   23/   50] | d_loss: 0.1664 | g_loss: 0.9325
Epoch [   23/   50] | d_loss: 0.2005 | g_loss: 1.1480
Epoch [   23/   50] | d_loss: 0.2417 | g_loss: 0.5181
Epoch [   23/   50] | d_loss: 0.1136 | g_loss: 0.5772
Epoch [   23/   50] | d_loss: 0.0934 | g_loss: 0.5570
Epoch [   23/   50] | d_loss: 0.2092 | g_loss: 0.9958
Epoch [   23/   50] | d_loss: 0.1233 | g_loss: 0.8427
Epoch [   23/   50] | d_loss: 0.1034 | g_loss: 0.8626
Epoch [   23/   50] | d_loss: 0.1811 | g_loss: 0.4980
Epoch [   23/   50] | d_loss: 0.1122 | g_loss: 1.2327
Epoch [   23/   50] | d_loss: 0.1215 | g_loss: 0.8618
Epoch [   23/   50] | d_loss: 0.0813 | g_loss: 1.1867
Epoch [   23/   50] | d_loss: 0.3430 | g_loss: 0.7143
Epoch [   23/   50] | d_loss: 0.2000 | g_loss: 0.7745
Epoch [   23/   50] | d_loss: 0.1215 | g_loss: 0.9330
Epoch [   23/   50] | d_loss: 0.0712 | g_loss: 0.5818
Epoch [   23/   50] | d_loss: 0.1304 | g_loss: 0.7142
Epoch [   23/   50] | d_loss: 0.2265 | g_loss: 0.7246
Epoch [   23/   50] | d_loss: 0.1572 | g_loss: 0.9614
Epoch [   23/   50] | d_loss: 0.1168 | g_loss: 0.6674
Epoch [   23/   50] | d_loss: 0.2125 | g_loss: 0.5518
Epoch [   23/   50] | d_loss: 0.1016 | g_loss: 1.0597
Epoch [   23/   50] | d_loss: 0.1828 | g_loss: 1.1951
Epoch [   23/   50] | d_loss: 0.3927 | g_loss: 0.5725
Epoch [   23/   50] | d_loss: 0.2124 | g_loss: 0.7181
Epoch [   23/   50] | d_loss: 0.0874 | g_loss: 0.7483
Epoch [   23/   50] | d_loss: 0.3793 | g_loss: 0.8390
Epoch [   23/   50] | d_loss: 0.1440 | g_loss: 0.5226
Epoch [   23/   50] | d_loss: 0.0861 | g_loss: 0.6690
Epoch [   23/   50] | d_loss: 0.0715 | g_loss: 1.0755
Epoch [   23/   50] | d_loss: 0.1301 | g_loss: 0.6481
Epoch [   23/   50] | d_loss: 0.1335 | g_loss: 0.6057
Epoch [   23/   50] | d_loss: 0.1138 | g_loss: 0.7448
Epoch [   23/   50] | d_loss: 0.0612 | g_loss: 0.5966
Epoch [   23/   50] | d_loss: 0.1554 | g_loss: 0.7753
Epoch [   23/   50] | d_loss: 0.1598 | g_loss: 1.0420
Epoch [   23/   50] | d_loss: 0.4421 | g_loss: 0.4298
Epoch [   24/   50] | d_loss: 0.2655 | g_loss: 0.8142
Epoch [   24/   50] | d_loss: 0.1303 | g_loss: 0.3842
Epoch [   24/   50] | d_loss: 0.1178 | g_loss: 0.7596
Epoch [   24/   50] | d_loss: 0.1270 | g_loss: 0.6356
Epoch [   24/   50] | d_loss: 0.2879 | g_loss: 1.0043
Epoch [   24/   50] | d_loss: 0.0800 | g_loss: 0.8087
Epoch [   24/   50] | d_loss: 0.2657 | g_loss: 0.9094
Epoch [   24/   50] | d_loss: 0.2449 | g_loss: 0.5358
Epoch [   24/   50] | d_loss: 0.1338 | g_loss: 0.7565
Epoch [   24/   50] | d_loss: 0.0835 | g_loss: 0.9353
Epoch [   24/   50] | d_loss: 0.1393 | g_loss: 0.7348
Epoch [   24/   50] | d_loss: 0.0960 | g_loss: 0.5199
Epoch [   24/   50] | d_loss: 0.1438 | g_loss: 0.9532
Epoch [   24/   50] | d_loss: 0.1423 | g_loss: 0.4692
Epoch [   24/   50] | d_loss: 0.1416 | g_loss: 0.8428
Epoch [   24/   50] | d_loss: 0.0917 | g_loss: 1.2810
Epoch [   24/   50] | d_loss: 0.1634 | g_loss: 0.8038
Epoch [   24/   50] | d_loss: 0.1367 | g_loss: 0.8286
Epoch [   24/   50] | d_loss: 0.0745 | g_loss: 0.7273
Epoch [   24/   50] | d_loss: 0.3191 | g_loss: 0.6656
Epoch [   24/   50] | d_loss: 0.1014 | g_loss: 0.7575
Epoch [   24/   50] | d_loss: 0.1604 | g_loss: 0.6611
Epoch [   24/   50] | d_loss: 0.1942 | g_loss: 0.6618
Epoch [   24/   50] | d_loss: 0.1685 | g_loss: 0.7058
Epoch [   24/   50] | d_loss: 0.0637 | g_loss: 0.9285
Epoch [   24/   50] | d_loss: 0.1616 | g_loss: 0.5358
Epoch [   24/   50] | d_loss: 0.1167 | g_loss: 0.7638
Epoch [   24/   50] | d_loss: 0.1366 | g_loss: 0.8266
Epoch [   24/   50] | d_loss: 0.0963 | g_loss: 1.0693
Epoch [   24/   50] | d_loss: 0.4122 | g_loss: 0.5509
Epoch [   24/   50] | d_loss: 0.1663 | g_loss: 0.3909
Epoch [   24/   50] | d_loss: 0.0934 | g_loss: 0.9604
Epoch [   24/   50] | d_loss: 0.1602 | g_loss: 0.8109
Epoch [   24/   50] | d_loss: 0.1378 | g_loss: 0.9871
Epoch [   24/   50] | d_loss: 0.1558 | g_loss: 0.4837
Epoch [   24/   50] | d_loss: 0.1355 | g_loss: 0.7328
Epoch [   24/   50] | d_loss: 0.1425 | g_loss: 0.8630
Epoch [   24/   50] | d_loss: 0.0637 | g_loss: 1.2210
Epoch [   24/   50] | d_loss: 0.0740 | g_loss: 0.7223
Epoch [   24/   50] | d_loss: 0.1197 | g_loss: 0.9644
Epoch [   24/   50] | d_loss: 0.0940 | g_loss: 0.4309
Epoch [   24/   50] | d_loss: 0.0774 | g_loss: 0.9779
Epoch [   24/   50] | d_loss: 0.1612 | g_loss: 0.3690
Epoch [   24/   50] | d_loss: 0.1689 | g_loss: 0.3700
Epoch [   24/   50] | d_loss: 0.0805 | g_loss: 0.8126
Epoch [   24/   50] | d_loss: 0.2537 | g_loss: 0.8230
Epoch [   24/   50] | d_loss: 0.1337 | g_loss: 0.9616
Epoch [   24/   50] | d_loss: 0.1016 | g_loss: 1.0035
Epoch [   24/   50] | d_loss: 0.0978 | g_loss: 0.8689
Epoch [   24/   50] | d_loss: 0.1553 | g_loss: 0.4782
Epoch [   24/   50] | d_loss: 0.1403 | g_loss: 0.8149
Epoch [   24/   50] | d_loss: 0.1828 | g_loss: 0.3783
Epoch [   24/   50] | d_loss: 0.0947 | g_loss: 0.9028
Epoch [   24/   50] | d_loss: 0.1116 | g_loss: 0.8244
Epoch [   24/   50] | d_loss: 0.1340 | g_loss: 0.7135
Epoch [   24/   50] | d_loss: 0.0729 | g_loss: 1.1072
Epoch [   24/   50] | d_loss: 0.1032 | g_loss: 0.9909
Epoch [   25/   50] | d_loss: 0.2555 | g_loss: 0.6539
Epoch [   25/   50] | d_loss: 0.0813 | g_loss: 0.5788
Epoch [   25/   50] | d_loss: 0.0996 | g_loss: 0.7851
Epoch [   25/   50] | d_loss: 0.1106 | g_loss: 0.8913
Epoch [   25/   50] | d_loss: 0.0676 | g_loss: 0.8021
Epoch [   25/   50] | d_loss: 0.0858 | g_loss: 0.7110
Epoch [   25/   50] | d_loss: 0.1094 | g_loss: 1.2874
Epoch [   25/   50] | d_loss: 0.1021 | g_loss: 0.8820
Epoch [   25/   50] | d_loss: 0.0953 | g_loss: 0.5568
Epoch [   25/   50] | d_loss: 0.0856 | g_loss: 0.9664
Epoch [   25/   50] | d_loss: 0.1834 | g_loss: 0.8329
Epoch [   25/   50] | d_loss: 0.1064 | g_loss: 0.5574
Epoch [   25/   50] | d_loss: 0.1196 | g_loss: 0.8489
Epoch [   25/   50] | d_loss: 0.0684 | g_loss: 0.9552
Epoch [   25/   50] | d_loss: 0.1168 | g_loss: 0.6670
Epoch [   25/   50] | d_loss: 0.1657 | g_loss: 0.7904
Epoch [   25/   50] | d_loss: 0.3619 | g_loss: 0.4053
Epoch [   25/   50] | d_loss: 0.1919 | g_loss: 1.1128
Epoch [   25/   50] | d_loss: 0.1008 | g_loss: 0.5920
Epoch [   25/   50] | d_loss: 0.1953 | g_loss: 0.7142
Epoch [   25/   50] | d_loss: 0.2314 | g_loss: 0.4992
Epoch [   25/   50] | d_loss: 0.0858 | g_loss: 0.8661
Epoch [   25/   50] | d_loss: 0.1003 | g_loss: 0.5924
Epoch [   25/   50] | d_loss: 0.1084 | g_loss: 0.6132
Epoch [   25/   50] | d_loss: 0.2477 | g_loss: 0.2471
Epoch [   25/   50] | d_loss: 0.1053 | g_loss: 0.4001
Epoch [   25/   50] | d_loss: 0.0730 | g_loss: 1.1192
Epoch [   25/   50] | d_loss: 0.0640 | g_loss: 0.8905
Epoch [   25/   50] | d_loss: 0.1869 | g_loss: 0.5421
Epoch [   25/   50] | d_loss: 0.1186 | g_loss: 1.0196
Epoch [   25/   50] | d_loss: 0.1551 | g_loss: 0.7307
Epoch [   25/   50] | d_loss: 0.1653 | g_loss: 0.6400
Epoch [   25/   50] | d_loss: 0.1434 | g_loss: 0.4666
Epoch [   25/   50] | d_loss: 0.1783 | g_loss: 0.7430
Epoch [   25/   50] | d_loss: 0.1170 | g_loss: 0.7877
Epoch [   25/   50] | d_loss: 0.1640 | g_loss: 0.8798
Epoch [   25/   50] | d_loss: 0.1291 | g_loss: 0.5851
Epoch [   25/   50] | d_loss: 0.1826 | g_loss: 0.6092
Epoch [   25/   50] | d_loss: 0.0855 | g_loss: 1.4856
Epoch [   25/   50] | d_loss: 0.0865 | g_loss: 0.4134
Epoch [   25/   50] | d_loss: 0.1123 | g_loss: 1.1239
Epoch [   25/   50] | d_loss: 0.1355 | g_loss: 0.8685
Epoch [   25/   50] | d_loss: 0.1298 | g_loss: 0.9660
Epoch [   25/   50] | d_loss: 0.1049 | g_loss: 0.5322
Epoch [   25/   50] | d_loss: 0.1868 | g_loss: 0.9267
Epoch [   25/   50] | d_loss: 0.1143 | g_loss: 1.1119
Epoch [   25/   50] | d_loss: 0.1839 | g_loss: 0.7221
Epoch [   25/   50] | d_loss: 0.1042 | g_loss: 0.9370
Epoch [   25/   50] | d_loss: 0.1300 | g_loss: 0.7102
Epoch [   25/   50] | d_loss: 0.1550 | g_loss: 0.4654
Epoch [   25/   50] | d_loss: 0.1075 | g_loss: 0.7915
Epoch [   25/   50] | d_loss: 0.0947 | g_loss: 0.7745
Epoch [   25/   50] | d_loss: 0.2461 | g_loss: 0.9563
Epoch [   25/   50] | d_loss: 0.1615 | g_loss: 0.8980
Epoch [   25/   50] | d_loss: 0.1698 | g_loss: 0.4520
Epoch [   25/   50] | d_loss: 0.1443 | g_loss: 0.9596
Epoch [   25/   50] | d_loss: 0.1217 | g_loss: 0.7120
Epoch [   26/   50] | d_loss: 0.1834 | g_loss: 0.5778
Epoch [   26/   50] | d_loss: 0.1100 | g_loss: 0.6576
Epoch [   26/   50] | d_loss: 0.1893 | g_loss: 0.7879
Epoch [   26/   50] | d_loss: 0.1522 | g_loss: 1.0419
Epoch [   26/   50] | d_loss: 0.1859 | g_loss: 0.6298
Epoch [   26/   50] | d_loss: 0.0689 | g_loss: 1.0572
Epoch [   26/   50] | d_loss: 0.2413 | g_loss: 0.6679
Epoch [   26/   50] | d_loss: 0.1027 | g_loss: 0.9914
Epoch [   26/   50] | d_loss: 0.0964 | g_loss: 1.2447
Epoch [   26/   50] | d_loss: 0.2177 | g_loss: 0.7375
Epoch [   26/   50] | d_loss: 0.1059 | g_loss: 1.1319
Epoch [   26/   50] | d_loss: 0.0952 | g_loss: 1.0607
Epoch [   26/   50] | d_loss: 0.0888 | g_loss: 0.9508
Epoch [   26/   50] | d_loss: 0.1124 | g_loss: 0.8971
Epoch [   26/   50] | d_loss: 0.1105 | g_loss: 1.0140
Epoch [   26/   50] | d_loss: 0.1725 | g_loss: 0.7287
Epoch [   26/   50] | d_loss: 0.0739 | g_loss: 0.4883
Epoch [   26/   50] | d_loss: 0.1907 | g_loss: 0.5839
Epoch [   26/   50] | d_loss: 0.0803 | g_loss: 0.7905
Epoch [   26/   50] | d_loss: 0.2892 | g_loss: 0.8385
Epoch [   26/   50] | d_loss: 0.1826 | g_loss: 0.7292
Epoch [   26/   50] | d_loss: 0.1257 | g_loss: 1.1875
Epoch [   26/   50] | d_loss: 0.1901 | g_loss: 0.4848
Epoch [   26/   50] | d_loss: 0.1071 | g_loss: 0.6133
Epoch [   26/   50] | d_loss: 0.1441 | g_loss: 0.9192
Epoch [   26/   50] | d_loss: 0.1101 | g_loss: 0.7950
Epoch [   26/   50] | d_loss: 0.1987 | g_loss: 0.5327
Epoch [   26/   50] | d_loss: 0.0876 | g_loss: 0.8462
Epoch [   26/   50] | d_loss: 0.0759 | g_loss: 1.1977
Epoch [   26/   50] | d_loss: 0.1250 | g_loss: 0.7570
Epoch [   26/   50] | d_loss: 0.2010 | g_loss: 0.4706
Epoch [   26/   50] | d_loss: 0.1438 | g_loss: 0.7523
Epoch [   26/   50] | d_loss: 0.1138 | g_loss: 0.8672
Epoch [   26/   50] | d_loss: 0.1207 | g_loss: 0.6762
Epoch [   26/   50] | d_loss: 0.0816 | g_loss: 0.5244
Epoch [   26/   50] | d_loss: 0.0838 | g_loss: 0.7807
Epoch [   26/   50] | d_loss: 0.0989 | g_loss: 1.0129
Epoch [   26/   50] | d_loss: 0.1573 | g_loss: 0.6971
Epoch [   26/   50] | d_loss: 0.1025 | g_loss: 0.7270
Epoch [   26/   50] | d_loss: 0.1493 | g_loss: 0.8250
Epoch [   26/   50] | d_loss: 0.2147 | g_loss: 0.5515
Epoch [   26/   50] | d_loss: 0.2143 | g_loss: 1.0221
Epoch [   26/   50] | d_loss: 0.0958 | g_loss: 0.4331
Epoch [   26/   50] | d_loss: 0.1549 | g_loss: 0.5589
Epoch [   26/   50] | d_loss: 0.1223 | g_loss: 0.9230
Epoch [   26/   50] | d_loss: 0.1832 | g_loss: 0.6944
Epoch [   26/   50] | d_loss: 0.1297 | g_loss: 0.7155
Epoch [   26/   50] | d_loss: 0.1049 | g_loss: 0.8100
Epoch [   26/   50] | d_loss: 0.1556 | g_loss: 1.6938
Epoch [   26/   50] | d_loss: 0.1400 | g_loss: 0.8264
Epoch [   26/   50] | d_loss: 0.1182 | g_loss: 0.8931
Epoch [   26/   50] | d_loss: 0.0829 | g_loss: 0.8779
Epoch [   26/   50] | d_loss: 0.0695 | g_loss: 1.0157
Epoch [   26/   50] | d_loss: 0.1500 | g_loss: 0.7012
Epoch [   26/   50] | d_loss: 0.1819 | g_loss: 0.7990
Epoch [   26/   50] | d_loss: 0.1463 | g_loss: 0.8884
Epoch [   26/   50] | d_loss: 0.1931 | g_loss: 0.3503
Epoch [   27/   50] | d_loss: 0.2186 | g_loss: 0.5432
Epoch [   27/   50] | d_loss: 0.0753 | g_loss: 0.6816
Epoch [   27/   50] | d_loss: 0.3333 | g_loss: 0.6823
Epoch [   27/   50] | d_loss: 0.1639 | g_loss: 0.5371
Epoch [   27/   50] | d_loss: 0.2243 | g_loss: 1.3610
Epoch [   27/   50] | d_loss: 0.1353 | g_loss: 0.6137
Epoch [   27/   50] | d_loss: 0.0682 | g_loss: 1.3620
Epoch [   27/   50] | d_loss: 0.1984 | g_loss: 0.4537
Epoch [   27/   50] | d_loss: 0.1393 | g_loss: 0.8644
Epoch [   27/   50] | d_loss: 0.0961 | g_loss: 0.6061
Epoch [   27/   50] | d_loss: 0.1281 | g_loss: 0.6114
Epoch [   27/   50] | d_loss: 0.1041 | g_loss: 1.0557
Epoch [   27/   50] | d_loss: 0.1729 | g_loss: 0.6190
Epoch [   27/   50] | d_loss: 0.0807 | g_loss: 0.6590
Epoch [   27/   50] | d_loss: 0.1949 | g_loss: 0.4791
Epoch [   27/   50] | d_loss: 0.1073 | g_loss: 0.6720
Epoch [   27/   50] | d_loss: 0.1243 | g_loss: 0.9215
Epoch [   27/   50] | d_loss: 0.4969 | g_loss: 0.6549
Epoch [   27/   50] | d_loss: 0.1895 | g_loss: 0.5449
Epoch [   27/   50] | d_loss: 0.1126 | g_loss: 0.8428
Epoch [   27/   50] | d_loss: 0.3569 | g_loss: 0.5172
Epoch [   27/   50] | d_loss: 0.1694 | g_loss: 0.7547
Epoch [   27/   50] | d_loss: 0.1062 | g_loss: 0.5651
Epoch [   27/   50] | d_loss: 0.2653 | g_loss: 0.5076
Epoch [   27/   50] | d_loss: 0.1284 | g_loss: 0.6988
Epoch [   27/   50] | d_loss: 0.1099 | g_loss: 0.7310
Epoch [   27/   50] | d_loss: 0.0553 | g_loss: 0.9353
Epoch [   27/   50] | d_loss: 0.0988 | g_loss: 0.6308
Epoch [   27/   50] | d_loss: 0.0948 | g_loss: 1.0282
Epoch [   27/   50] | d_loss: 0.1424 | g_loss: 0.7312
Epoch [   27/   50] | d_loss: 0.1684 | g_loss: 0.6486
Epoch [   27/   50] | d_loss: 0.0928 | g_loss: 0.7988
Epoch [   27/   50] | d_loss: 0.1769 | g_loss: 0.3797
Epoch [   27/   50] | d_loss: 0.0827 | g_loss: 0.7104
Epoch [   27/   50] | d_loss: 0.1576 | g_loss: 0.7408
Epoch [   27/   50] | d_loss: 0.0710 | g_loss: 0.7914
Epoch [   27/   50] | d_loss: 0.1587 | g_loss: 0.8540
Epoch [   27/   50] | d_loss: 0.2365 | g_loss: 0.6991
Epoch [   27/   50] | d_loss: 0.1320 | g_loss: 0.8113
Epoch [   27/   50] | d_loss: 0.1430 | g_loss: 1.0271
Epoch [   27/   50] | d_loss: 0.0440 | g_loss: 1.2450
Epoch [   27/   50] | d_loss: 0.1510 | g_loss: 0.8099
Epoch [   27/   50] | d_loss: 0.1568 | g_loss: 0.3986
Epoch [   27/   50] | d_loss: 0.0886 | g_loss: 0.8511
Epoch [   27/   50] | d_loss: 0.1426 | g_loss: 0.6820
Epoch [   27/   50] | d_loss: 0.1967 | g_loss: 0.6063
Epoch [   27/   50] | d_loss: 0.1307 | g_loss: 0.7501
Epoch [   27/   50] | d_loss: 0.1897 | g_loss: 0.9004
Epoch [   27/   50] | d_loss: 0.1745 | g_loss: 0.4313
Epoch [   27/   50] | d_loss: 0.1012 | g_loss: 1.0554
Epoch [   27/   50] | d_loss: 0.1376 | g_loss: 0.7161
Epoch [   27/   50] | d_loss: 0.0928 | g_loss: 0.7949
Epoch [   27/   50] | d_loss: 0.0707 | g_loss: 0.7460
Epoch [   27/   50] | d_loss: 0.0958 | g_loss: 1.1857
Epoch [   27/   50] | d_loss: 0.0902 | g_loss: 1.0319
Epoch [   27/   50] | d_loss: 0.0574 | g_loss: 0.4798
Epoch [   27/   50] | d_loss: 0.0946 | g_loss: 1.2161
Epoch [   28/   50] | d_loss: 0.2357 | g_loss: 0.7475
Epoch [   28/   50] | d_loss: 0.0769 | g_loss: 0.6681
Epoch [   28/   50] | d_loss: 0.1061 | g_loss: 0.9143
Epoch [   28/   50] | d_loss: 0.0404 | g_loss: 0.8754
Epoch [   28/   50] | d_loss: 0.0664 | g_loss: 0.8378
Epoch [   28/   50] | d_loss: 0.0796 | g_loss: 0.6131
Epoch [   28/   50] | d_loss: 0.0679 | g_loss: 1.2869
Epoch [   28/   50] | d_loss: 0.1191 | g_loss: 1.0296
Epoch [   28/   50] | d_loss: 0.2114 | g_loss: 0.4776
Epoch [   28/   50] | d_loss: 0.1038 | g_loss: 0.6674
Epoch [   28/   50] | d_loss: 0.1037 | g_loss: 1.1536
Epoch [   28/   50] | d_loss: 0.1244 | g_loss: 0.4814
Epoch [   28/   50] | d_loss: 0.1684 | g_loss: 0.4344
Epoch [   28/   50] | d_loss: 0.2532 | g_loss: 0.8645
Epoch [   28/   50] | d_loss: 0.1791 | g_loss: 0.7374
Epoch [   28/   50] | d_loss: 0.1264 | g_loss: 0.8054
Epoch [   28/   50] | d_loss: 0.1332 | g_loss: 0.8612
Epoch [   28/   50] | d_loss: 0.1336 | g_loss: 0.8019
Epoch [   28/   50] | d_loss: 0.1296 | g_loss: 0.9674
Epoch [   28/   50] | d_loss: 0.1426 | g_loss: 0.8468
Epoch [   28/   50] | d_loss: 0.0714 | g_loss: 0.9788
Epoch [   28/   50] | d_loss: 0.0498 | g_loss: 0.9301
Epoch [   28/   50] | d_loss: 0.0935 | g_loss: 1.0713
Epoch [   28/   50] | d_loss: 0.1557 | g_loss: 0.4073
Epoch [   28/   50] | d_loss: 0.1053 | g_loss: 0.7950
Epoch [   28/   50] | d_loss: 0.2062 | g_loss: 0.7584
Epoch [   28/   50] | d_loss: 0.1338 | g_loss: 0.5679
Epoch [   28/   50] | d_loss: 0.0873 | g_loss: 0.7686
Epoch [   28/   50] | d_loss: 0.0986 | g_loss: 0.3736
Epoch [   28/   50] | d_loss: 0.1942 | g_loss: 1.5288
Epoch [   28/   50] | d_loss: 0.0690 | g_loss: 0.9002
Epoch [   28/   50] | d_loss: 0.1487 | g_loss: 0.4243
Epoch [   28/   50] | d_loss: 0.2734 | g_loss: 1.6411
Epoch [   28/   50] | d_loss: 0.0754 | g_loss: 0.8791
Epoch [   28/   50] | d_loss: 0.0689 | g_loss: 0.6560
Epoch [   28/   50] | d_loss: 0.1548 | g_loss: 1.1106
Epoch [   28/   50] | d_loss: 0.3918 | g_loss: 0.8867
Epoch [   28/   50] | d_loss: 0.1252 | g_loss: 1.0918
Epoch [   28/   50] | d_loss: 0.0882 | g_loss: 0.9966
Epoch [   28/   50] | d_loss: 0.1661 | g_loss: 0.7939
Epoch [   28/   50] | d_loss: 0.0926 | g_loss: 0.5684
Epoch [   28/   50] | d_loss: 0.1571 | g_loss: 0.5265
Epoch [   28/   50] | d_loss: 0.1527 | g_loss: 0.4178
Epoch [   28/   50] | d_loss: 0.0993 | g_loss: 0.7230
Epoch [   28/   50] | d_loss: 0.1708 | g_loss: 1.1897
Epoch [   28/   50] | d_loss: 0.1440 | g_loss: 0.7829
Epoch [   28/   50] | d_loss: 0.0913 | g_loss: 0.8453
Epoch [   28/   50] | d_loss: 0.1944 | g_loss: 1.0464
Epoch [   28/   50] | d_loss: 0.1627 | g_loss: 0.9637
Epoch [   28/   50] | d_loss: 0.1528 | g_loss: 0.6849
Epoch [   28/   50] | d_loss: 0.0802 | g_loss: 1.0961
Epoch [   28/   50] | d_loss: 0.1648 | g_loss: 0.7448
Epoch [   28/   50] | d_loss: 0.1378 | g_loss: 0.3334
Epoch [   28/   50] | d_loss: 0.1468 | g_loss: 0.9302
Epoch [   28/   50] | d_loss: 0.1051 | g_loss: 0.9602
Epoch [   28/   50] | d_loss: 0.2584 | g_loss: 0.7493
Epoch [   28/   50] | d_loss: 0.1755 | g_loss: 1.0019
Epoch [   29/   50] | d_loss: 0.0822 | g_loss: 0.9100
Epoch [   29/   50] | d_loss: 0.0762 | g_loss: 0.9819
Epoch [   29/   50] | d_loss: 0.4568 | g_loss: 0.8069
Epoch [   29/   50] | d_loss: 0.0645 | g_loss: 0.9396
Epoch [   29/   50] | d_loss: 0.0808 | g_loss: 0.9360
Epoch [   29/   50] | d_loss: 0.0953 | g_loss: 0.7846
Epoch [   29/   50] | d_loss: 0.1814 | g_loss: 0.6684
Epoch [   29/   50] | d_loss: 0.0631 | g_loss: 1.0319
Epoch [   29/   50] | d_loss: 0.0686 | g_loss: 0.6933
Epoch [   29/   50] | d_loss: 0.1262 | g_loss: 0.8230
Epoch [   29/   50] | d_loss: 0.0829 | g_loss: 0.6129
Epoch [   29/   50] | d_loss: 0.1179 | g_loss: 0.8837
Epoch [   29/   50] | d_loss: 0.1425 | g_loss: 0.9228
Epoch [   29/   50] | d_loss: 0.1286 | g_loss: 0.7267
Epoch [   29/   50] | d_loss: 0.2406 | g_loss: 0.7195
Epoch [   29/   50] | d_loss: 0.1597 | g_loss: 0.9673
Epoch [   29/   50] | d_loss: 0.0781 | g_loss: 0.6272
Epoch [   29/   50] | d_loss: 0.1896 | g_loss: 0.9472
Epoch [   29/   50] | d_loss: 0.3225 | g_loss: 0.6125
Epoch [   29/   50] | d_loss: 0.1020 | g_loss: 0.8372
Epoch [   29/   50] | d_loss: 0.1330 | g_loss: 0.5288
Epoch [   29/   50] | d_loss: 0.1574 | g_loss: 1.2910
Epoch [   29/   50] | d_loss: 0.0916 | g_loss: 0.6746
Epoch [   29/   50] | d_loss: 0.0907 | g_loss: 0.5631
Epoch [   29/   50] | d_loss: 0.1468 | g_loss: 0.6768
Epoch [   29/   50] | d_loss: 0.0680 | g_loss: 0.9252
Epoch [   29/   50] | d_loss: 0.1510 | g_loss: 0.9899
Epoch [   29/   50] | d_loss: 0.1951 | g_loss: 0.8040
Epoch [   29/   50] | d_loss: 0.0617 | g_loss: 0.5616
Epoch [   29/   50] | d_loss: 0.1199 | g_loss: 0.9955
Epoch [   29/   50] | d_loss: 0.1354 | g_loss: 0.8882
Epoch [   29/   50] | d_loss: 0.1545 | g_loss: 1.1597
Epoch [   29/   50] | d_loss: 0.1387 | g_loss: 0.3291
Epoch [   29/   50] | d_loss: 0.2415 | g_loss: 0.6525
Epoch [   29/   50] | d_loss: 0.1631 | g_loss: 0.4601
Epoch [   29/   50] | d_loss: 0.0816 | g_loss: 1.2459
Epoch [   29/   50] | d_loss: 0.1405 | g_loss: 0.8419
Epoch [   29/   50] | d_loss: 0.2011 | g_loss: 0.4966
Epoch [   29/   50] | d_loss: 0.2436 | g_loss: 0.7157
Epoch [   29/   50] | d_loss: 0.1461 | g_loss: 0.8611
Epoch [   29/   50] | d_loss: 0.1269 | g_loss: 0.8380
Epoch [   29/   50] | d_loss: 0.2576 | g_loss: 0.5973
Epoch [   29/   50] | d_loss: 0.1053 | g_loss: 0.8617
Epoch [   29/   50] | d_loss: 0.0976 | g_loss: 0.7653
Epoch [   29/   50] | d_loss: 0.0836 | g_loss: 0.8842
Epoch [   29/   50] | d_loss: 0.0852 | g_loss: 0.8816
Epoch [   29/   50] | d_loss: 0.1209 | g_loss: 0.8475
Epoch [   29/   50] | d_loss: 0.1437 | g_loss: 0.7689
Epoch [   29/   50] | d_loss: 0.0905 | g_loss: 0.6014
Epoch [   29/   50] | d_loss: 0.1280 | g_loss: 0.8306
Epoch [   29/   50] | d_loss: 0.1999 | g_loss: 0.7299
Epoch [   29/   50] | d_loss: 0.1506 | g_loss: 0.7769
Epoch [   29/   50] | d_loss: 0.1995 | g_loss: 0.6543
Epoch [   29/   50] | d_loss: 0.1266 | g_loss: 0.9117
Epoch [   29/   50] | d_loss: 0.0682 | g_loss: 1.0592
Epoch [   29/   50] | d_loss: 0.1229 | g_loss: 0.9113
Epoch [   29/   50] | d_loss: 0.2063 | g_loss: 0.3260
Epoch [   30/   50] | d_loss: 0.1946 | g_loss: 0.6214
Epoch [   30/   50] | d_loss: 0.1731 | g_loss: 0.6796
Epoch [   30/   50] | d_loss: 0.1916 | g_loss: 0.6947
Epoch [   30/   50] | d_loss: 0.0915 | g_loss: 0.8481
Epoch [   30/   50] | d_loss: 0.0562 | g_loss: 0.7974
Epoch [   30/   50] | d_loss: 0.0964 | g_loss: 0.7104
Epoch [   30/   50] | d_loss: 0.1509 | g_loss: 1.0847
Epoch [   30/   50] | d_loss: 0.2231 | g_loss: 0.7035
Epoch [   30/   50] | d_loss: 0.2060 | g_loss: 0.5445
Epoch [   30/   50] | d_loss: 0.0891 | g_loss: 0.9308
Epoch [   30/   50] | d_loss: 0.1216 | g_loss: 0.9775
Epoch [   30/   50] | d_loss: 0.1679 | g_loss: 0.4539
Epoch [   30/   50] | d_loss: 0.1776 | g_loss: 1.2040
Epoch [   30/   50] | d_loss: 0.1226 | g_loss: 1.1065
Epoch [   30/   50] | d_loss: 0.1121 | g_loss: 0.5462
Epoch [   30/   50] | d_loss: 0.0857 | g_loss: 0.5999
Epoch [   30/   50] | d_loss: 0.0825 | g_loss: 0.6465
Epoch [   30/   50] | d_loss: 0.2070 | g_loss: 1.0225
Epoch [   30/   50] | d_loss: 0.1247 | g_loss: 0.9666
Epoch [   30/   50] | d_loss: 0.1002 | g_loss: 0.7540
Epoch [   30/   50] | d_loss: 0.1584 | g_loss: 0.8741
Epoch [   30/   50] | d_loss: 0.0549 | g_loss: 0.9441
Epoch [   30/   50] | d_loss: 0.1330 | g_loss: 0.7752
Epoch [   30/   50] | d_loss: 0.1953 | g_loss: 0.6500
Epoch [   30/   50] | d_loss: 0.1823 | g_loss: 0.3855
Epoch [   30/   50] | d_loss: 0.1276 | g_loss: 0.5990
Epoch [   30/   50] | d_loss: 0.0909 | g_loss: 1.0216
Epoch [   30/   50] | d_loss: 0.1010 | g_loss: 0.8038
Epoch [   30/   50] | d_loss: 0.0977 | g_loss: 0.4387
Epoch [   30/   50] | d_loss: 0.0966 | g_loss: 0.9960
Epoch [   30/   50] | d_loss: 0.1764 | g_loss: 0.8316
Epoch [   30/   50] | d_loss: 0.1028 | g_loss: 0.8211
Epoch [   30/   50] | d_loss: 0.1244 | g_loss: 0.9731
Epoch [   30/   50] | d_loss: 0.0881 | g_loss: 0.8490
Epoch [   30/   50] | d_loss: 0.1159 | g_loss: 1.0745
Epoch [   30/   50] | d_loss: 0.1724 | g_loss: 0.5961
Epoch [   30/   50] | d_loss: 0.0914 | g_loss: 0.8571
Epoch [   30/   50] | d_loss: 0.1006 | g_loss: 0.7739
Epoch [   30/   50] | d_loss: 0.1567 | g_loss: 1.2735
Epoch [   30/   50] | d_loss: 0.1255 | g_loss: 1.1334
Epoch [   30/   50] | d_loss: 0.1351 | g_loss: 1.4120
Epoch [   30/   50] | d_loss: 0.1729 | g_loss: 0.5499
Epoch [   30/   50] | d_loss: 0.1157 | g_loss: 0.6203
Epoch [   30/   50] | d_loss: 0.0994 | g_loss: 0.6057
Epoch [   30/   50] | d_loss: 0.1118 | g_loss: 0.6856
Epoch [   30/   50] | d_loss: 0.0955 | g_loss: 0.6318
Epoch [   30/   50] | d_loss: 0.2325 | g_loss: 0.5423
Epoch [   30/   50] | d_loss: 0.0988 | g_loss: 0.6956
Epoch [   30/   50] | d_loss: 0.1842 | g_loss: 0.8786
Epoch [   30/   50] | d_loss: 0.1387 | g_loss: 1.2793
Epoch [   30/   50] | d_loss: 0.1732 | g_loss: 0.8097
Epoch [   30/   50] | d_loss: 0.2371 | g_loss: 0.9917
Epoch [   30/   50] | d_loss: 0.1199 | g_loss: 0.9581
Epoch [   30/   50] | d_loss: 0.0826 | g_loss: 0.6809
Epoch [   30/   50] | d_loss: 0.1147 | g_loss: 0.8234
Epoch [   30/   50] | d_loss: 0.0950 | g_loss: 0.9395
Epoch [   30/   50] | d_loss: 0.1409 | g_loss: 0.9788
Epoch [   31/   50] | d_loss: 0.2560 | g_loss: 1.1240
Epoch [   31/   50] | d_loss: 0.1153 | g_loss: 0.9991
Epoch [   31/   50] | d_loss: 0.2344 | g_loss: 0.6564
Epoch [   31/   50] | d_loss: 0.1865 | g_loss: 1.1977
Epoch [   31/   50] | d_loss: 0.0986 | g_loss: 0.8539
Epoch [   31/   50] | d_loss: 0.1439 | g_loss: 0.5830
Epoch [   31/   50] | d_loss: 0.0740 | g_loss: 0.8977
Epoch [   31/   50] | d_loss: 0.1026 | g_loss: 0.8547
Epoch [   31/   50] | d_loss: 0.0797 | g_loss: 0.8832
Epoch [   31/   50] | d_loss: 0.1237 | g_loss: 0.9938
Epoch [   31/   50] | d_loss: 0.0870 | g_loss: 0.6582
Epoch [   31/   50] | d_loss: 0.0866 | g_loss: 0.9017
Epoch [   31/   50] | d_loss: 0.0751 | g_loss: 1.0999
Epoch [   31/   50] | d_loss: 0.1265 | g_loss: 0.7791
Epoch [   31/   50] | d_loss: 0.1340 | g_loss: 0.8097
Epoch [   31/   50] | d_loss: 0.0964 | g_loss: 0.9189
Epoch [   31/   50] | d_loss: 0.0694 | g_loss: 0.6905
Epoch [   31/   50] | d_loss: 0.1995 | g_loss: 1.1916
Epoch [   31/   50] | d_loss: 0.1603 | g_loss: 1.3289
Epoch [   31/   50] | d_loss: 0.1324 | g_loss: 0.5299
Epoch [   31/   50] | d_loss: 0.0579 | g_loss: 0.6012
Epoch [   31/   50] | d_loss: 0.1586 | g_loss: 0.4007
Epoch [   31/   50] | d_loss: 0.1038 | g_loss: 0.4545
Epoch [   31/   50] | d_loss: 0.0575 | g_loss: 1.1437
Epoch [   31/   50] | d_loss: 0.0553 | g_loss: 0.7147
Epoch [   31/   50] | d_loss: 0.1128 | g_loss: 0.5195
Epoch [   31/   50] | d_loss: 0.0923 | g_loss: 1.0950
Epoch [   31/   50] | d_loss: 0.1443 | g_loss: 0.7929
Epoch [   31/   50] | d_loss: 0.0651 | g_loss: 0.9038
Epoch [   31/   50] | d_loss: 0.0808 | g_loss: 0.8183
Epoch [   31/   50] | d_loss: 0.0859 | g_loss: 1.0798
Epoch [   31/   50] | d_loss: 0.0842 | g_loss: 0.8337
Epoch [   31/   50] | d_loss: 0.3217 | g_loss: 0.6119
Epoch [   31/   50] | d_loss: 0.1439 | g_loss: 0.8630
Epoch [   31/   50] | d_loss: 0.1233 | g_loss: 0.6900
Epoch [   31/   50] | d_loss: 0.1820 | g_loss: 1.0895
Epoch [   31/   50] | d_loss: 0.1594 | g_loss: 0.8204
Epoch [   31/   50] | d_loss: 0.0933 | g_loss: 0.8118
Epoch [   31/   50] | d_loss: 0.1158 | g_loss: 0.5280
Epoch [   31/   50] | d_loss: 0.1259 | g_loss: 0.9437
Epoch [   31/   50] | d_loss: 0.0847 | g_loss: 0.7228
Epoch [   31/   50] | d_loss: 0.1770 | g_loss: 0.1663
Epoch [   31/   50] | d_loss: 0.1109 | g_loss: 1.0219
Epoch [   31/   50] | d_loss: 0.0770 | g_loss: 0.9298
Epoch [   31/   50] | d_loss: 0.0695 | g_loss: 1.0416
Epoch [   31/   50] | d_loss: 0.1735 | g_loss: 0.6986
Epoch [   31/   50] | d_loss: 0.0770 | g_loss: 0.6124
Epoch [   31/   50] | d_loss: 0.0776 | g_loss: 0.6247
Epoch [   31/   50] | d_loss: 0.2124 | g_loss: 0.7358
Epoch [   31/   50] | d_loss: 0.0544 | g_loss: 1.1215
Epoch [   31/   50] | d_loss: 0.2329 | g_loss: 0.4200
Epoch [   31/   50] | d_loss: 0.5250 | g_loss: 0.6925
Epoch [   31/   50] | d_loss: 0.1280 | g_loss: 0.4377
Epoch [   31/   50] | d_loss: 0.0914 | g_loss: 0.9209
Epoch [   31/   50] | d_loss: 0.2438 | g_loss: 1.3087
Epoch [   31/   50] | d_loss: 0.1699 | g_loss: 0.7624
Epoch [   31/   50] | d_loss: 0.1630 | g_loss: 0.6879
Epoch [   32/   50] | d_loss: 0.2292 | g_loss: 0.8321
Epoch [   32/   50] | d_loss: 0.0702 | g_loss: 0.8521
Epoch [   32/   50] | d_loss: 0.0875 | g_loss: 1.4459
Epoch [   32/   50] | d_loss: 0.1962 | g_loss: 0.6184
Epoch [   32/   50] | d_loss: 0.0818 | g_loss: 0.9953
Epoch [   32/   50] | d_loss: 0.1412 | g_loss: 0.9225
Epoch [   32/   50] | d_loss: 0.0897 | g_loss: 0.6893
Epoch [   32/   50] | d_loss: 0.2819 | g_loss: 1.1271
Epoch [   32/   50] | d_loss: 0.1628 | g_loss: 0.5995
Epoch [   32/   50] | d_loss: 0.0854 | g_loss: 0.9975
Epoch [   32/   50] | d_loss: 0.1291 | g_loss: 0.5684
Epoch [   32/   50] | d_loss: 0.1372 | g_loss: 0.7861
Epoch [   32/   50] | d_loss: 0.1427 | g_loss: 0.7459
Epoch [   32/   50] | d_loss: 0.0783 | g_loss: 0.8527
Epoch [   32/   50] | d_loss: 0.0943 | g_loss: 0.5216
Epoch [   32/   50] | d_loss: 0.1349 | g_loss: 0.7398
Epoch [   32/   50] | d_loss: 0.1539 | g_loss: 0.7065
Epoch [   32/   50] | d_loss: 0.0879 | g_loss: 0.8222
Epoch [   32/   50] | d_loss: 0.1026 | g_loss: 0.5818
Epoch [   32/   50] | d_loss: 0.2315 | g_loss: 0.8981
Epoch [   32/   50] | d_loss: 0.0635 | g_loss: 1.1186
Epoch [   32/   50] | d_loss: 0.0776 | g_loss: 0.8691
Epoch [   32/   50] | d_loss: 0.1668 | g_loss: 0.9589
Epoch [   32/   50] | d_loss: 0.0664 | g_loss: 0.6886
Epoch [   32/   50] | d_loss: 0.1253 | g_loss: 0.9789
Epoch [   32/   50] | d_loss: 0.0867 | g_loss: 0.8259
Epoch [   32/   50] | d_loss: 0.1116 | g_loss: 0.6498
Epoch [   32/   50] | d_loss: 0.1085 | g_loss: 0.6758
Epoch [   32/   50] | d_loss: 0.0768 | g_loss: 0.8550
Epoch [   32/   50] | d_loss: 0.0659 | g_loss: 0.5327
Epoch [   32/   50] | d_loss: 0.1348 | g_loss: 1.0788
Epoch [   32/   50] | d_loss: 0.0970 | g_loss: 0.9964
Epoch [   32/   50] | d_loss: 0.2044 | g_loss: 0.7251
Epoch [   32/   50] | d_loss: 0.1515 | g_loss: 1.0124
Epoch [   32/   50] | d_loss: 0.1088 | g_loss: 0.4216
Epoch [   32/   50] | d_loss: 0.1767 | g_loss: 1.2810
Epoch [   32/   50] | d_loss: 0.2536 | g_loss: 0.8083
Epoch [   32/   50] | d_loss: 0.1020 | g_loss: 0.7664
Epoch [   32/   50] | d_loss: 0.1813 | g_loss: 0.9621
Epoch [   32/   50] | d_loss: 0.0485 | g_loss: 0.9582
Epoch [   32/   50] | d_loss: 0.1292 | g_loss: 0.4368
Epoch [   32/   50] | d_loss: 0.2462 | g_loss: 1.2203
Epoch [   32/   50] | d_loss: 0.1487 | g_loss: 0.6933
Epoch [   32/   50] | d_loss: 0.0832 | g_loss: 0.9621
Epoch [   32/   50] | d_loss: 0.2921 | g_loss: 0.4746
Epoch [   32/   50] | d_loss: 0.0917 | g_loss: 0.6037
Epoch [   32/   50] | d_loss: 0.2409 | g_loss: 1.1857
Epoch [   32/   50] | d_loss: 0.0575 | g_loss: 0.9439
Epoch [   32/   50] | d_loss: 0.1056 | g_loss: 0.7869
Epoch [   32/   50] | d_loss: 0.1784 | g_loss: 0.7148
Epoch [   32/   50] | d_loss: 0.3005 | g_loss: 0.5911
Epoch [   32/   50] | d_loss: 0.1816 | g_loss: 0.6715
Epoch [   32/   50] | d_loss: 0.2427 | g_loss: 0.7507
Epoch [   32/   50] | d_loss: 0.1121 | g_loss: 1.2118
Epoch [   32/   50] | d_loss: 0.0863 | g_loss: 1.0131
Epoch [   32/   50] | d_loss: 0.0694 | g_loss: 0.7223
Epoch [   32/   50] | d_loss: 0.1210 | g_loss: 0.9435
Epoch [   33/   50] | d_loss: 0.1384 | g_loss: 0.7378
Epoch [   33/   50] | d_loss: 0.1476 | g_loss: 0.6810
Epoch [   33/   50] | d_loss: 0.1690 | g_loss: 1.2074
Epoch [   33/   50] | d_loss: 0.1099 | g_loss: 0.8573
Epoch [   33/   50] | d_loss: 0.1466 | g_loss: 0.4888
Epoch [   33/   50] | d_loss: 0.1320 | g_loss: 0.4525
Epoch [   33/   50] | d_loss: 0.0999 | g_loss: 0.5546
Epoch [   33/   50] | d_loss: 0.0926 | g_loss: 0.9667
Epoch [   33/   50] | d_loss: 0.0824 | g_loss: 0.7753
Epoch [   33/   50] | d_loss: 0.1032 | g_loss: 0.7210
Epoch [   33/   50] | d_loss: 0.0779 | g_loss: 0.7081
Epoch [   33/   50] | d_loss: 0.0879 | g_loss: 1.0271
Epoch [   33/   50] | d_loss: 0.0884 | g_loss: 1.3369
Epoch [   33/   50] | d_loss: 0.0634 | g_loss: 0.6757
Epoch [   33/   50] | d_loss: 0.1260 | g_loss: 0.8176
Epoch [   33/   50] | d_loss: 0.0767 | g_loss: 0.5839
Epoch [   33/   50] | d_loss: 0.1311 | g_loss: 0.9701
Epoch [   33/   50] | d_loss: 0.1084 | g_loss: 0.7480
Epoch [   33/   50] | d_loss: 0.1451 | g_loss: 0.7904
Epoch [   33/   50] | d_loss: 0.0625 | g_loss: 0.8223
Epoch [   33/   50] | d_loss: 0.1430 | g_loss: 0.7693
Epoch [   33/   50] | d_loss: 0.0623 | g_loss: 1.1320
Epoch [   33/   50] | d_loss: 0.0687 | g_loss: 1.0200
Epoch [   33/   50] | d_loss: 0.1717 | g_loss: 0.8039
Epoch [   33/   50] | d_loss: 0.0703 | g_loss: 0.5238
Epoch [   33/   50] | d_loss: 0.2606 | g_loss: 0.6107
Epoch [   33/   50] | d_loss: 0.1004 | g_loss: 1.2301
Epoch [   33/   50] | d_loss: 0.0711 | g_loss: 0.9048
Epoch [   33/   50] | d_loss: 0.1085 | g_loss: 1.1320
Epoch [   33/   50] | d_loss: 0.0846 | g_loss: 0.9861
Epoch [   33/   50] | d_loss: 0.1676 | g_loss: 0.7274
Epoch [   33/   50] | d_loss: 0.2660 | g_loss: 1.1597
Epoch [   33/   50] | d_loss: 0.1857 | g_loss: 0.5871
Epoch [   33/   50] | d_loss: 0.1991 | g_loss: 0.9992
Epoch [   33/   50] | d_loss: 0.0983 | g_loss: 1.0234
Epoch [   33/   50] | d_loss: 0.2611 | g_loss: 0.9089
Epoch [   33/   50] | d_loss: 0.0685 | g_loss: 0.8842
Epoch [   33/   50] | d_loss: 0.0911 | g_loss: 1.1033
Epoch [   33/   50] | d_loss: 0.1331 | g_loss: 0.4998
Epoch [   33/   50] | d_loss: 0.1358 | g_loss: 0.8254
Epoch [   33/   50] | d_loss: 0.1103 | g_loss: 0.6724
Epoch [   33/   50] | d_loss: 0.0826 | g_loss: 0.7443
Epoch [   33/   50] | d_loss: 0.1103 | g_loss: 0.5945
Epoch [   33/   50] | d_loss: 0.0793 | g_loss: 1.0950
Epoch [   33/   50] | d_loss: 0.0456 | g_loss: 1.2326
Epoch [   33/   50] | d_loss: 0.1329 | g_loss: 0.7224
Epoch [   33/   50] | d_loss: 0.2980 | g_loss: 0.5205
Epoch [   33/   50] | d_loss: 0.0652 | g_loss: 1.7270
Epoch [   33/   50] | d_loss: 0.0832 | g_loss: 1.3685
Epoch [   33/   50] | d_loss: 0.2069 | g_loss: 0.9008
Epoch [   33/   50] | d_loss: 0.0587 | g_loss: 0.8701
Epoch [   33/   50] | d_loss: 0.1543 | g_loss: 0.5913
Epoch [   33/   50] | d_loss: 0.1019 | g_loss: 0.3920
Epoch [   33/   50] | d_loss: 0.1968 | g_loss: 1.0571
Epoch [   33/   50] | d_loss: 0.1138 | g_loss: 0.7333
Epoch [   33/   50] | d_loss: 0.0985 | g_loss: 0.7875
Epoch [   33/   50] | d_loss: 0.0850 | g_loss: 0.4998
Epoch [   34/   50] | d_loss: 0.2305 | g_loss: 0.9663
Epoch [   34/   50] | d_loss: 0.1826 | g_loss: 0.7270
Epoch [   34/   50] | d_loss: 0.1049 | g_loss: 0.5345
Epoch [   34/   50] | d_loss: 0.0646 | g_loss: 0.9766
Epoch [   34/   50] | d_loss: 0.1284 | g_loss: 0.9990
Epoch [   34/   50] | d_loss: 0.1063 | g_loss: 0.6442
Epoch [   34/   50] | d_loss: 0.1115 | g_loss: 0.7093
Epoch [   34/   50] | d_loss: 0.1708 | g_loss: 1.1212
Epoch [   34/   50] | d_loss: 0.1122 | g_loss: 1.1732
Epoch [   34/   50] | d_loss: 0.0648 | g_loss: 1.0164
Epoch [   34/   50] | d_loss: 0.0646 | g_loss: 0.9959
Epoch [   34/   50] | d_loss: 0.1600 | g_loss: 0.4232
Epoch [   34/   50] | d_loss: 0.1510 | g_loss: 0.6230
Epoch [   34/   50] | d_loss: 0.1223 | g_loss: 1.0453
Epoch [   34/   50] | d_loss: 0.1337 | g_loss: 0.7856
Epoch [   34/   50] | d_loss: 0.2084 | g_loss: 0.6566
Epoch [   34/   50] | d_loss: 0.1338 | g_loss: 0.7985
Epoch [   34/   50] | d_loss: 0.0484 | g_loss: 0.9160
Epoch [   34/   50] | d_loss: 0.1236 | g_loss: 0.7190
Epoch [   34/   50] | d_loss: 0.0988 | g_loss: 1.4325
Epoch [   34/   50] | d_loss: 0.1284 | g_loss: 0.8203
Epoch [   34/   50] | d_loss: 0.1112 | g_loss: 0.7643
Epoch [   34/   50] | d_loss: 0.1640 | g_loss: 0.9015
Epoch [   34/   50] | d_loss: 0.2028 | g_loss: 0.8225
Epoch [   34/   50] | d_loss: 0.4785 | g_loss: 0.8704
Epoch [   34/   50] | d_loss: 0.1922 | g_loss: 0.6780
Epoch [   34/   50] | d_loss: 0.1447 | g_loss: 0.4604
Epoch [   34/   50] | d_loss: 0.1475 | g_loss: 0.7273
Epoch [   34/   50] | d_loss: 0.0904 | g_loss: 0.6518
Epoch [   34/   50] | d_loss: 0.0581 | g_loss: 0.6682
Epoch [   34/   50] | d_loss: 0.2351 | g_loss: 0.7581
Epoch [   34/   50] | d_loss: 0.1485 | g_loss: 0.6510
Epoch [   34/   50] | d_loss: 0.1543 | g_loss: 1.1278
Epoch [   34/   50] | d_loss: 0.1092 | g_loss: 1.2268
Epoch [   34/   50] | d_loss: 0.0693 | g_loss: 1.1238
Epoch [   34/   50] | d_loss: 0.0502 | g_loss: 1.0983
Epoch [   34/   50] | d_loss: 0.2017 | g_loss: 0.6431
Epoch [   34/   50] | d_loss: 0.0982 | g_loss: 0.6387
Epoch [   34/   50] | d_loss: 0.2270 | g_loss: 1.0838
Epoch [   34/   50] | d_loss: 0.1184 | g_loss: 0.7006
Epoch [   34/   50] | d_loss: 0.1044 | g_loss: 0.7455
Epoch [   34/   50] | d_loss: 0.1055 | g_loss: 0.6161
Epoch [   34/   50] | d_loss: 0.3051 | g_loss: 0.9172
Epoch [   34/   50] | d_loss: 0.1528 | g_loss: 0.7053
Epoch [   34/   50] | d_loss: 0.0788 | g_loss: 0.9268
Epoch [   34/   50] | d_loss: 0.1392 | g_loss: 0.8243
Epoch [   34/   50] | d_loss: 0.0617 | g_loss: 0.7479
Epoch [   34/   50] | d_loss: 0.1400 | g_loss: 0.6883
Epoch [   34/   50] | d_loss: 0.1846 | g_loss: 0.8864
Epoch [   34/   50] | d_loss: 0.1392 | g_loss: 0.7185
Epoch [   34/   50] | d_loss: 0.2350 | g_loss: 0.6388
Epoch [   34/   50] | d_loss: 0.1270 | g_loss: 0.7658
Epoch [   34/   50] | d_loss: 0.0761 | g_loss: 0.5992
Epoch [   34/   50] | d_loss: 0.2169 | g_loss: 0.5152
Epoch [   34/   50] | d_loss: 0.1423 | g_loss: 0.7714
Epoch [   34/   50] | d_loss: 0.2570 | g_loss: 1.0169
Epoch [   34/   50] | d_loss: 0.0844 | g_loss: 0.8172
Epoch [   35/   50] | d_loss: 0.0946 | g_loss: 0.8797
Epoch [   35/   50] | d_loss: 0.1482 | g_loss: 1.0598
Epoch [   35/   50] | d_loss: 0.1519 | g_loss: 1.0450
Epoch [   35/   50] | d_loss: 0.1015 | g_loss: 0.7694
Epoch [   35/   50] | d_loss: 0.0439 | g_loss: 0.7944
Epoch [   35/   50] | d_loss: 0.1176 | g_loss: 0.9027
Epoch [   35/   50] | d_loss: 0.1104 | g_loss: 0.7317
Epoch [   35/   50] | d_loss: 0.1602 | g_loss: 0.4994
Epoch [   35/   50] | d_loss: 0.1237 | g_loss: 0.5130
Epoch [   35/   50] | d_loss: 0.0922 | g_loss: 0.6872
Epoch [   35/   50] | d_loss: 0.0948 | g_loss: 0.8720
Epoch [   35/   50] | d_loss: 0.2279 | g_loss: 0.4905
Epoch [   35/   50] | d_loss: 0.1488 | g_loss: 1.1077
Epoch [   35/   50] | d_loss: 0.2249 | g_loss: 0.8068
Epoch [   35/   50] | d_loss: 0.0742 | g_loss: 0.6733
Epoch [   35/   50] | d_loss: 0.1744 | g_loss: 0.4931
Epoch [   35/   50] | d_loss: 0.0953 | g_loss: 0.5844
Epoch [   35/   50] | d_loss: 0.1712 | g_loss: 0.5724
Epoch [   35/   50] | d_loss: 0.0635 | g_loss: 1.1394
Epoch [   35/   50] | d_loss: 0.1710 | g_loss: 0.6226
Epoch [   35/   50] | d_loss: 0.0720 | g_loss: 0.5944
Epoch [   35/   50] | d_loss: 0.1255 | g_loss: 0.9762
Epoch [   35/   50] | d_loss: 0.2066 | g_loss: 0.5244
Epoch [   35/   50] | d_loss: 0.2457 | g_loss: 0.7599
Epoch [   35/   50] | d_loss: 0.2011 | g_loss: 1.1531
Epoch [   35/   50] | d_loss: 0.0611 | g_loss: 1.3744
Epoch [   35/   50] | d_loss: 0.1619 | g_loss: 0.6439
Epoch [   35/   50] | d_loss: 0.6117 | g_loss: 0.4227
Epoch [   35/   50] | d_loss: 0.2591 | g_loss: 0.9147
Epoch [   35/   50] | d_loss: 0.1076 | g_loss: 0.9729
Epoch [   35/   50] | d_loss: 0.1277 | g_loss: 0.8495
Epoch [   35/   50] | d_loss: 0.0694 | g_loss: 0.8979
Epoch [   35/   50] | d_loss: 0.1922 | g_loss: 0.5342
Epoch [   35/   50] | d_loss: 0.1155 | g_loss: 0.8993
Epoch [   35/   50] | d_loss: 0.0729 | g_loss: 0.7148
Epoch [   35/   50] | d_loss: 0.1316 | g_loss: 0.5252
Epoch [   35/   50] | d_loss: 0.0686 | g_loss: 0.7535
Epoch [   35/   50] | d_loss: 0.0602 | g_loss: 1.3311
Epoch [   35/   50] | d_loss: 0.0914 | g_loss: 0.8523
Epoch [   35/   50] | d_loss: 0.0640 | g_loss: 0.8867
Epoch [   35/   50] | d_loss: 0.1186 | g_loss: 0.6905
Epoch [   35/   50] | d_loss: 0.0862 | g_loss: 0.5933
Epoch [   35/   50] | d_loss: 0.1726 | g_loss: 0.8292
Epoch [   35/   50] | d_loss: 0.0923 | g_loss: 0.5728
Epoch [   35/   50] | d_loss: 0.0741 | g_loss: 1.0296
Epoch [   35/   50] | d_loss: 0.1156 | g_loss: 0.6220
Epoch [   35/   50] | d_loss: 0.1684 | g_loss: 0.3923
Epoch [   35/   50] | d_loss: 0.0496 | g_loss: 1.0217
Epoch [   35/   50] | d_loss: 0.2746 | g_loss: 0.6831
Epoch [   35/   50] | d_loss: 0.1377 | g_loss: 1.1958
Epoch [   35/   50] | d_loss: 0.1289 | g_loss: 0.4890
Epoch [   35/   50] | d_loss: 0.0920 | g_loss: 0.7325
Epoch [   35/   50] | d_loss: 0.0615 | g_loss: 0.8300
Epoch [   35/   50] | d_loss: 0.2115 | g_loss: 0.6833
Epoch [   35/   50] | d_loss: 0.0851 | g_loss: 0.9252
Epoch [   35/   50] | d_loss: 0.1043 | g_loss: 0.5641
Epoch [   35/   50] | d_loss: 0.1487 | g_loss: 0.8078
Epoch [   36/   50] | d_loss: 0.0828 | g_loss: 0.8601
Epoch [   36/   50] | d_loss: 0.1129 | g_loss: 0.8099
Epoch [   36/   50] | d_loss: 0.1215 | g_loss: 1.0265
Epoch [   36/   50] | d_loss: 0.1504 | g_loss: 0.7917
Epoch [   36/   50] | d_loss: 0.1174 | g_loss: 0.6191
Epoch [   36/   50] | d_loss: 0.1186 | g_loss: 0.8216
Epoch [   36/   50] | d_loss: 0.1182 | g_loss: 0.9714
Epoch [   36/   50] | d_loss: 0.1062 | g_loss: 1.1275
Epoch [   36/   50] | d_loss: 0.0712 | g_loss: 0.6526
Epoch [   36/   50] | d_loss: 0.0846 | g_loss: 1.1065
Epoch [   36/   50] | d_loss: 0.1043 | g_loss: 0.6381
Epoch [   36/   50] | d_loss: 0.0711 | g_loss: 0.7106
Epoch [   36/   50] | d_loss: 0.0722 | g_loss: 0.6194
Epoch [   36/   50] | d_loss: 0.2378 | g_loss: 1.0856
Epoch [   36/   50] | d_loss: 0.0599 | g_loss: 0.9665
Epoch [   36/   50] | d_loss: 0.0914 | g_loss: 0.7933
Epoch [   36/   50] | d_loss: 0.1929 | g_loss: 0.5184
Epoch [   36/   50] | d_loss: 0.1936 | g_loss: 0.5647
Epoch [   36/   50] | d_loss: 0.1723 | g_loss: 0.8379
Epoch [   36/   50] | d_loss: 0.0757 | g_loss: 0.6165
Epoch [   36/   50] | d_loss: 0.0855 | g_loss: 0.6825
Epoch [   36/   50] | d_loss: 0.2095 | g_loss: 0.6337
Epoch [   36/   50] | d_loss: 0.1363 | g_loss: 0.8960
Epoch [   36/   50] | d_loss: 0.0739 | g_loss: 0.8683
Epoch [   36/   50] | d_loss: 0.1037 | g_loss: 0.6789
Epoch [   36/   50] | d_loss: 0.1163 | g_loss: 0.9054
Epoch [   36/   50] | d_loss: 0.2148 | g_loss: 0.6312
Epoch [   36/   50] | d_loss: 0.1039 | g_loss: 1.0020
Epoch [   36/   50] | d_loss: 0.0855 | g_loss: 0.9962
Epoch [   36/   50] | d_loss: 0.1339 | g_loss: 0.4587
Epoch [   36/   50] | d_loss: 0.1709 | g_loss: 0.4200
Epoch [   36/   50] | d_loss: 0.0652 | g_loss: 0.8980
Epoch [   36/   50] | d_loss: 0.1917 | g_loss: 0.6304
Epoch [   36/   50] | d_loss: 0.0996 | g_loss: 1.0558
Epoch [   36/   50] | d_loss: 0.3365 | g_loss: 0.8044
Epoch [   36/   50] | d_loss: 0.1747 | g_loss: 0.6893
Epoch [   36/   50] | d_loss: 0.1435 | g_loss: 0.8543
Epoch [   36/   50] | d_loss: 0.0976 | g_loss: 1.0106
Epoch [   36/   50] | d_loss: 0.1178 | g_loss: 1.5922
Epoch [   36/   50] | d_loss: 0.2157 | g_loss: 0.7489
Epoch [   36/   50] | d_loss: 0.1215 | g_loss: 1.1207
Epoch [   36/   50] | d_loss: 0.1271 | g_loss: 0.8499
Epoch [   36/   50] | d_loss: 0.0642 | g_loss: 1.1155
Epoch [   36/   50] | d_loss: 0.0969 | g_loss: 1.1822
Epoch [   36/   50] | d_loss: 0.2060 | g_loss: 0.6349
Epoch [   36/   50] | d_loss: 0.1454 | g_loss: 0.6150
Epoch [   36/   50] | d_loss: 0.0592 | g_loss: 0.7354
Epoch [   36/   50] | d_loss: 0.1732 | g_loss: 1.0022
Epoch [   36/   50] | d_loss: 0.0942 | g_loss: 0.6606
Epoch [   36/   50] | d_loss: 0.2012 | g_loss: 1.1448
Epoch [   36/   50] | d_loss: 0.0935 | g_loss: 0.6940
Epoch [   36/   50] | d_loss: 0.5171 | g_loss: 0.3084
Epoch [   36/   50] | d_loss: 0.1690 | g_loss: 0.7261
Epoch [   36/   50] | d_loss: 0.1146 | g_loss: 0.7821
Epoch [   36/   50] | d_loss: 0.2537 | g_loss: 0.4034
Epoch [   36/   50] | d_loss: 0.1592 | g_loss: 1.1023
Epoch [   36/   50] | d_loss: 0.1715 | g_loss: 0.6335
Epoch [   37/   50] | d_loss: 0.0712 | g_loss: 0.8540
Epoch [   37/   50] | d_loss: 0.1057 | g_loss: 1.0785
Epoch [   37/   50] | d_loss: 0.2074 | g_loss: 0.9125
Epoch [   37/   50] | d_loss: 0.1179 | g_loss: 0.7677
Epoch [   37/   50] | d_loss: 0.0762 | g_loss: 0.7216
Epoch [   37/   50] | d_loss: 0.0811 | g_loss: 0.8136
Epoch [   37/   50] | d_loss: 0.0910 | g_loss: 0.9738
Epoch [   37/   50] | d_loss: 0.1515 | g_loss: 0.3503
Epoch [   37/   50] | d_loss: 0.0703 | g_loss: 0.8309
Epoch [   37/   50] | d_loss: 0.2068 | g_loss: 0.7694
Epoch [   37/   50] | d_loss: 0.3058 | g_loss: 1.2728
Epoch [   37/   50] | d_loss: 0.1665 | g_loss: 0.5974
Epoch [   37/   50] | d_loss: 0.1374 | g_loss: 0.6198
Epoch [   37/   50] | d_loss: 0.0754 | g_loss: 0.7815
Epoch [   37/   50] | d_loss: 0.0913 | g_loss: 0.7803
Epoch [   37/   50] | d_loss: 0.1205 | g_loss: 0.9258
Epoch [   37/   50] | d_loss: 0.0640 | g_loss: 0.8679
Epoch [   37/   50] | d_loss: 0.2042 | g_loss: 0.5356
Epoch [   37/   50] | d_loss: 0.1367 | g_loss: 1.0059
Epoch [   37/   50] | d_loss: 0.0736 | g_loss: 0.8021
Epoch [   37/   50] | d_loss: 0.0537 | g_loss: 0.8414
Epoch [   37/   50] | d_loss: 0.0978 | g_loss: 0.8729
Epoch [   37/   50] | d_loss: 0.0947 | g_loss: 0.7945
Epoch [   37/   50] | d_loss: 0.1105 | g_loss: 0.5656
Epoch [   37/   50] | d_loss: 0.0737 | g_loss: 0.9007
Epoch [   37/   50] | d_loss: 0.0773 | g_loss: 0.7181
Epoch [   37/   50] | d_loss: 0.1464 | g_loss: 0.9033
Epoch [   37/   50] | d_loss: 0.1017 | g_loss: 0.7815
Epoch [   37/   50] | d_loss: 0.0730 | g_loss: 1.1330
Epoch [   37/   50] | d_loss: 0.1051 | g_loss: 0.7745
Epoch [   37/   50] | d_loss: 0.1520 | g_loss: 0.6644
Epoch [   37/   50] | d_loss: 0.1774 | g_loss: 0.5306
Epoch [   37/   50] | d_loss: 0.1205 | g_loss: 0.3623
Epoch [   37/   50] | d_loss: 0.2975 | g_loss: 0.8699
Epoch [   37/   50] | d_loss: 0.1129 | g_loss: 0.6775
Epoch [   37/   50] | d_loss: 0.0925 | g_loss: 0.7915
Epoch [   37/   50] | d_loss: 0.1679 | g_loss: 0.7139
Epoch [   37/   50] | d_loss: 0.0978 | g_loss: 0.9491
Epoch [   37/   50] | d_loss: 0.0706 | g_loss: 0.5817
Epoch [   37/   50] | d_loss: 0.0741 | g_loss: 0.7463
Epoch [   37/   50] | d_loss: 0.0849 | g_loss: 0.5266
Epoch [   37/   50] | d_loss: 0.2385 | g_loss: 1.1517
Epoch [   37/   50] | d_loss: 0.0877 | g_loss: 0.7306
Epoch [   37/   50] | d_loss: 0.0731 | g_loss: 0.9725
Epoch [   37/   50] | d_loss: 0.1077 | g_loss: 0.5464
Epoch [   37/   50] | d_loss: 0.0674 | g_loss: 1.2701
Epoch [   37/   50] | d_loss: 0.2256 | g_loss: 0.7005
Epoch [   37/   50] | d_loss: 0.1168 | g_loss: 0.8049
Epoch [   37/   50] | d_loss: 0.1105 | g_loss: 0.8132
Epoch [   37/   50] | d_loss: 0.0811 | g_loss: 0.6350
Epoch [   37/   50] | d_loss: 0.1009 | g_loss: 1.0288
Epoch [   37/   50] | d_loss: 0.0920 | g_loss: 0.9017
Epoch [   37/   50] | d_loss: 0.0712 | g_loss: 0.7870
Epoch [   37/   50] | d_loss: 0.1428 | g_loss: 0.5975
Epoch [   37/   50] | d_loss: 0.2035 | g_loss: 0.7364
Epoch [   37/   50] | d_loss: 0.1699 | g_loss: 0.9479
Epoch [   37/   50] | d_loss: 0.0531 | g_loss: 1.0863
Epoch [   38/   50] | d_loss: 0.2470 | g_loss: 0.9474
Epoch [   38/   50] | d_loss: 0.1263 | g_loss: 0.9689
Epoch [   38/   50] | d_loss: 0.1237 | g_loss: 0.7961
Epoch [   38/   50] | d_loss: 0.1998 | g_loss: 0.5760
Epoch [   38/   50] | d_loss: 0.1851 | g_loss: 0.6368
Epoch [   38/   50] | d_loss: 0.0874 | g_loss: 0.6671
Epoch [   38/   50] | d_loss: 0.0806 | g_loss: 0.7802
Epoch [   38/   50] | d_loss: 0.0610 | g_loss: 1.0893
Epoch [   38/   50] | d_loss: 0.1756 | g_loss: 0.4664
Epoch [   38/   50] | d_loss: 0.0697 | g_loss: 0.7657
Epoch [   38/   50] | d_loss: 0.1384 | g_loss: 0.7458
Epoch [   38/   50] | d_loss: 0.1876 | g_loss: 0.5698
Epoch [   38/   50] | d_loss: 0.1238 | g_loss: 0.7612
Epoch [   38/   50] | d_loss: 0.1819 | g_loss: 0.7002
Epoch [   38/   50] | d_loss: 0.0788 | g_loss: 0.9978
Epoch [   38/   50] | d_loss: 0.1244 | g_loss: 0.5400
Epoch [   38/   50] | d_loss: 0.0710 | g_loss: 1.0286
Epoch [   38/   50] | d_loss: 0.0922 | g_loss: 0.7430
Epoch [   38/   50] | d_loss: 0.0615 | g_loss: 1.1494
Epoch [   38/   50] | d_loss: 0.0937 | g_loss: 1.0322
Epoch [   38/   50] | d_loss: 0.0960 | g_loss: 0.9262
Epoch [   38/   50] | d_loss: 0.0798 | g_loss: 0.7650
Epoch [   38/   50] | d_loss: 0.0633 | g_loss: 0.7545
Epoch [   38/   50] | d_loss: 0.0677 | g_loss: 1.0526
Epoch [   38/   50] | d_loss: 0.1625 | g_loss: 0.9476
Epoch [   38/   50] | d_loss: 0.0934 | g_loss: 1.0536
Epoch [   38/   50] | d_loss: 0.0677 | g_loss: 1.0713
Epoch [   38/   50] | d_loss: 0.0664 | g_loss: 1.1214
Epoch [   38/   50] | d_loss: 0.0841 | g_loss: 0.9960
Epoch [   38/   50] | d_loss: 0.0663 | g_loss: 1.0130
Epoch [   38/   50] | d_loss: 0.1032 | g_loss: 0.6355
Epoch [   38/   50] | d_loss: 0.0632 | g_loss: 0.7309
Epoch [   38/   50] | d_loss: 0.0664 | g_loss: 1.0273
Epoch [   38/   50] | d_loss: 0.0682 | g_loss: 1.0593
Epoch [   38/   50] | d_loss: 0.0924 | g_loss: 0.7405
Epoch [   38/   50] | d_loss: 0.1050 | g_loss: 0.8530
Epoch [   38/   50] | d_loss: 0.1387 | g_loss: 0.7542
Epoch [   38/   50] | d_loss: 0.1055 | g_loss: 0.8251
Epoch [   38/   50] | d_loss: 0.0744 | g_loss: 1.0208
Epoch [   38/   50] | d_loss: 0.1181 | g_loss: 0.4743
Epoch [   38/   50] | d_loss: 0.1117 | g_loss: 0.5055
Epoch [   38/   50] | d_loss: 0.0471 | g_loss: 1.0197
Epoch [   38/   50] | d_loss: 0.0738 | g_loss: 0.5827
Epoch [   38/   50] | d_loss: 0.1638 | g_loss: 0.7982
Epoch [   38/   50] | d_loss: 0.0926 | g_loss: 0.5910
Epoch [   38/   50] | d_loss: 0.2582 | g_loss: 0.4334
Epoch [   38/   50] | d_loss: 0.1192 | g_loss: 1.1342
Epoch [   38/   50] | d_loss: 0.0768 | g_loss: 0.6664
Epoch [   38/   50] | d_loss: 0.0843 | g_loss: 0.8273
Epoch [   38/   50] | d_loss: 0.1389 | g_loss: 0.7463
Epoch [   38/   50] | d_loss: 0.2396 | g_loss: 1.4041
Epoch [   38/   50] | d_loss: 0.1327 | g_loss: 0.4649
Epoch [   38/   50] | d_loss: 0.0769 | g_loss: 0.9303
Epoch [   38/   50] | d_loss: 0.1050 | g_loss: 1.0935
Epoch [   38/   50] | d_loss: 0.0563 | g_loss: 0.8542
Epoch [   38/   50] | d_loss: 0.1782 | g_loss: 1.1066
Epoch [   38/   50] | d_loss: 0.1951 | g_loss: 1.0134
Epoch [   39/   50] | d_loss: 0.1120 | g_loss: 0.7566
Epoch [   39/   50] | d_loss: 0.1307 | g_loss: 0.8839
Epoch [   39/   50] | d_loss: 0.0556 | g_loss: 1.1146
Epoch [   39/   50] | d_loss: 0.1264 | g_loss: 0.8711
Epoch [   39/   50] | d_loss: 0.1078 | g_loss: 0.6667
Epoch [   39/   50] | d_loss: 0.1382 | g_loss: 1.3381
Epoch [   39/   50] | d_loss: 0.1432 | g_loss: 0.5261
Epoch [   39/   50] | d_loss: 0.1474 | g_loss: 0.5140
Epoch [   39/   50] | d_loss: 0.0573 | g_loss: 0.3572
Epoch [   39/   50] | d_loss: 0.1733 | g_loss: 0.8115
Epoch [   39/   50] | d_loss: 0.0473 | g_loss: 0.7551
Epoch [   39/   50] | d_loss: 0.2287 | g_loss: 0.5274
Epoch [   39/   50] | d_loss: 0.0815 | g_loss: 0.8041
Epoch [   39/   50] | d_loss: 0.0759 | g_loss: 0.6535
Epoch [   39/   50] | d_loss: 0.0326 | g_loss: 1.8252
Epoch [   39/   50] | d_loss: 0.0501 | g_loss: 1.0355
Epoch [   39/   50] | d_loss: 0.0850 | g_loss: 1.1206
Epoch [   39/   50] | d_loss: 0.0698 | g_loss: 0.7902
Epoch [   39/   50] | d_loss: 0.0700 | g_loss: 0.7401
Epoch [   39/   50] | d_loss: 0.1112 | g_loss: 1.0091
Epoch [   39/   50] | d_loss: 0.0820 | g_loss: 0.6787
Epoch [   39/   50] | d_loss: 0.1462 | g_loss: 0.5964
Epoch [   39/   50] | d_loss: 0.0997 | g_loss: 1.1544
Epoch [   39/   50] | d_loss: 0.1239 | g_loss: 1.0392
Epoch [   39/   50] | d_loss: 0.1872 | g_loss: 0.5673
Epoch [   39/   50] | d_loss: 0.0790 | g_loss: 0.9261
Epoch [   39/   50] | d_loss: 0.0937 | g_loss: 0.8233
Epoch [   39/   50] | d_loss: 0.0809 | g_loss: 0.9234
Epoch [   39/   50] | d_loss: 0.1077 | g_loss: 0.8431
Epoch [   39/   50] | d_loss: 0.0452 | g_loss: 1.0456
Epoch [   39/   50] | d_loss: 0.1447 | g_loss: 0.6810
Epoch [   39/   50] | d_loss: 0.1210 | g_loss: 1.0876
Epoch [   39/   50] | d_loss: 0.0849 | g_loss: 0.7532
Epoch [   39/   50] | d_loss: 0.0856 | g_loss: 0.5274
Epoch [   39/   50] | d_loss: 0.0806 | g_loss: 0.9869
Epoch [   39/   50] | d_loss: 0.1046 | g_loss: 0.8889
Epoch [   39/   50] | d_loss: 0.1278 | g_loss: 0.8421
Epoch [   39/   50] | d_loss: 0.0492 | g_loss: 1.1942
Epoch [   39/   50] | d_loss: 0.1049 | g_loss: 1.0253
Epoch [   39/   50] | d_loss: 0.1648 | g_loss: 0.8367
Epoch [   39/   50] | d_loss: 0.2699 | g_loss: 0.6721
Epoch [   39/   50] | d_loss: 0.1136 | g_loss: 0.7367
Epoch [   39/   50] | d_loss: 0.0965 | g_loss: 0.9860
Epoch [   39/   50] | d_loss: 0.2699 | g_loss: 0.6076
Epoch [   39/   50] | d_loss: 0.1511 | g_loss: 0.6485
Epoch [   39/   50] | d_loss: 0.0756 | g_loss: 1.1037
Epoch [   39/   50] | d_loss: 0.3131 | g_loss: 0.8408
Epoch [   39/   50] | d_loss: 0.0791 | g_loss: 1.0726
Epoch [   39/   50] | d_loss: 0.0788 | g_loss: 0.7035
Epoch [   39/   50] | d_loss: 0.1111 | g_loss: 0.6844
Epoch [   39/   50] | d_loss: 0.1721 | g_loss: 0.7477
Epoch [   39/   50] | d_loss: 0.0929 | g_loss: 0.7936
Epoch [   39/   50] | d_loss: 0.1168 | g_loss: 0.9196
Epoch [   39/   50] | d_loss: 0.0733 | g_loss: 1.1310
Epoch [   39/   50] | d_loss: 0.2425 | g_loss: 0.3864
Epoch [   39/   50] | d_loss: 0.1374 | g_loss: 0.5370
Epoch [   39/   50] | d_loss: 0.1715 | g_loss: 0.8006
Epoch [   40/   50] | d_loss: 0.1238 | g_loss: 0.5989
Epoch [   40/   50] | d_loss: 0.0830 | g_loss: 1.0055
Epoch [   40/   50] | d_loss: 0.1047 | g_loss: 0.6626
Epoch [   40/   50] | d_loss: 0.0952 | g_loss: 1.2452
Epoch [   40/   50] | d_loss: 0.0572 | g_loss: 0.6416
Epoch [   40/   50] | d_loss: 0.1361 | g_loss: 0.7439
Epoch [   40/   50] | d_loss: 0.0472 | g_loss: 0.9963
Epoch [   40/   50] | d_loss: 0.0527 | g_loss: 1.1663
Epoch [   40/   50] | d_loss: 0.0995 | g_loss: 0.5444
Epoch [   40/   50] | d_loss: 0.0754 | g_loss: 1.0555
Epoch [   40/   50] | d_loss: 0.0562 | g_loss: 1.2909
Epoch [   40/   50] | d_loss: 0.1194 | g_loss: 0.4202
Epoch [   40/   50] | d_loss: 0.1295 | g_loss: 0.8556
Epoch [   40/   50] | d_loss: 0.1628 | g_loss: 1.2863
Epoch [   40/   50] | d_loss: 0.1283 | g_loss: 1.0428
Epoch [   40/   50] | d_loss: 0.0628 | g_loss: 1.0686
Epoch [   40/   50] | d_loss: 0.1868 | g_loss: 0.6174
Epoch [   40/   50] | d_loss: 0.0709 | g_loss: 0.9771
Epoch [   40/   50] | d_loss: 0.1365 | g_loss: 0.6699
Epoch [   40/   50] | d_loss: 0.1227 | g_loss: 0.6863
Epoch [   40/   50] | d_loss: 0.2040 | g_loss: 0.3846
Epoch [   40/   50] | d_loss: 0.0832 | g_loss: 0.9678
Epoch [   40/   50] | d_loss: 0.1806 | g_loss: 1.0921
Epoch [   40/   50] | d_loss: 0.0972 | g_loss: 0.7939
Epoch [   40/   50] | d_loss: 0.0685 | g_loss: 0.9331
Epoch [   40/   50] | d_loss: 0.0872 | g_loss: 0.7507
Epoch [   40/   50] | d_loss: 0.0954 | g_loss: 1.1462
Epoch [   40/   50] | d_loss: 0.0758 | g_loss: 0.8152
Epoch [   40/   50] | d_loss: 0.2049 | g_loss: 0.8784
Epoch [   40/   50] | d_loss: 0.0655 | g_loss: 1.0889
Epoch [   40/   50] | d_loss: 0.0648 | g_loss: 0.7913
Epoch [   40/   50] | d_loss: 0.0778 | g_loss: 0.6209
Epoch [   40/   50] | d_loss: 0.0557 | g_loss: 0.9532
Epoch [   40/   50] | d_loss: 0.0559 | g_loss: 1.0402
Epoch [   40/   50] | d_loss: 0.1894 | g_loss: 0.6882
Epoch [   40/   50] | d_loss: 0.1295 | g_loss: 0.6376
Epoch [   40/   50] | d_loss: 0.2217 | g_loss: 0.7707
Epoch [   40/   50] | d_loss: 0.0811 | g_loss: 0.7565
Epoch [   40/   50] | d_loss: 0.1018 | g_loss: 0.7805
Epoch [   40/   50] | d_loss: 0.1047 | g_loss: 0.8336
Epoch [   40/   50] | d_loss: 0.1131 | g_loss: 1.0991
Epoch [   40/   50] | d_loss: 0.1204 | g_loss: 1.2202
Epoch [   40/   50] | d_loss: 0.0510 | g_loss: 0.9929
Epoch [   40/   50] | d_loss: 0.0731 | g_loss: 0.4603
Epoch [   40/   50] | d_loss: 0.1769 | g_loss: 1.1963
Epoch [   40/   50] | d_loss: 0.0438 | g_loss: 0.7020
Epoch [   40/   50] | d_loss: 0.1526 | g_loss: 1.0480
Epoch [   40/   50] | d_loss: 0.0757 | g_loss: 0.9580
Epoch [   40/   50] | d_loss: 0.2161 | g_loss: 0.9110
Epoch [   40/   50] | d_loss: 0.1592 | g_loss: 0.7748
Epoch [   40/   50] | d_loss: 0.1161 | g_loss: 1.5144
Epoch [   40/   50] | d_loss: 0.1281 | g_loss: 0.9761
Epoch [   40/   50] | d_loss: 0.0535 | g_loss: 0.8557
Epoch [   40/   50] | d_loss: 0.0644 | g_loss: 0.8275
Epoch [   40/   50] | d_loss: 0.1183 | g_loss: 0.6205
Epoch [   40/   50] | d_loss: 0.0860 | g_loss: 0.8618
Epoch [   40/   50] | d_loss: 0.0865 | g_loss: 1.0367
Epoch [   41/   50] | d_loss: 0.4496 | g_loss: 1.2143
Epoch [   41/   50] | d_loss: 0.0466 | g_loss: 0.8591
Epoch [   41/   50] | d_loss: 0.1366 | g_loss: 0.4275
Epoch [   41/   50] | d_loss: 0.1885 | g_loss: 0.5852
Epoch [   41/   50] | d_loss: 0.3569 | g_loss: 0.6165
Epoch [   41/   50] | d_loss: 0.0550 | g_loss: 0.7750
Epoch [   41/   50] | d_loss: 0.0683 | g_loss: 0.8694
Epoch [   41/   50] | d_loss: 0.1610 | g_loss: 0.7259
Epoch [   41/   50] | d_loss: 0.0455 | g_loss: 0.7370
Epoch [   41/   50] | d_loss: 0.0693 | g_loss: 0.7000
Epoch [   41/   50] | d_loss: 0.1377 | g_loss: 0.7118
Epoch [   41/   50] | d_loss: 0.1373 | g_loss: 0.8387
Epoch [   41/   50] | d_loss: 0.2024 | g_loss: 0.6887
Epoch [   41/   50] | d_loss: 0.1373 | g_loss: 0.4731
Epoch [   41/   50] | d_loss: 0.0786 | g_loss: 1.0296
Epoch [   41/   50] | d_loss: 0.0684 | g_loss: 0.6425
Epoch [   41/   50] | d_loss: 0.2502 | g_loss: 1.2216
Epoch [   41/   50] | d_loss: 0.1122 | g_loss: 0.5017
Epoch [   41/   50] | d_loss: 0.0803 | g_loss: 0.7268
Epoch [   41/   50] | d_loss: 0.1018 | g_loss: 0.4792
Epoch [   41/   50] | d_loss: 0.1844 | g_loss: 0.8396
Epoch [   41/   50] | d_loss: 0.0692 | g_loss: 1.1378
Epoch [   41/   50] | d_loss: 0.2006 | g_loss: 0.5210
Epoch [   41/   50] | d_loss: 0.0986 | g_loss: 1.1697
Epoch [   41/   50] | d_loss: 0.0544 | g_loss: 0.8926
Epoch [   41/   50] | d_loss: 0.0736 | g_loss: 0.9170
Epoch [   41/   50] | d_loss: 0.1107 | g_loss: 0.6858
Epoch [   41/   50] | d_loss: 0.1833 | g_loss: 0.5922
Epoch [   41/   50] | d_loss: 0.0842 | g_loss: 0.7628
Epoch [   41/   50] | d_loss: 0.1822 | g_loss: 0.9408
Epoch [   41/   50] | d_loss: 0.2423 | g_loss: 0.8913
Epoch [   41/   50] | d_loss: 0.1098 | g_loss: 0.5501
Epoch [   41/   50] | d_loss: 0.0952 | g_loss: 0.5866
Epoch [   41/   50] | d_loss: 0.0864 | g_loss: 0.7442
Epoch [   41/   50] | d_loss: 0.2368 | g_loss: 0.6172
Epoch [   41/   50] | d_loss: 0.1795 | g_loss: 0.5910
Epoch [   41/   50] | d_loss: 0.1742 | g_loss: 0.7060
Epoch [   41/   50] | d_loss: 0.2570 | g_loss: 1.2317
Epoch [   41/   50] | d_loss: 0.0815 | g_loss: 0.7098
Epoch [   41/   50] | d_loss: 0.1469 | g_loss: 1.3368
Epoch [   41/   50] | d_loss: 0.0964 | g_loss: 0.9285
Epoch [   41/   50] | d_loss: 0.1052 | g_loss: 0.5472
Epoch [   41/   50] | d_loss: 0.2171 | g_loss: 0.4065
Epoch [   41/   50] | d_loss: 0.1425 | g_loss: 0.6621
Epoch [   41/   50] | d_loss: 0.0879 | g_loss: 0.7451
Epoch [   41/   50] | d_loss: 0.1792 | g_loss: 0.8470
Epoch [   41/   50] | d_loss: 0.0924 | g_loss: 0.8315
Epoch [   41/   50] | d_loss: 0.1461 | g_loss: 0.6174
Epoch [   41/   50] | d_loss: 0.0856 | g_loss: 0.9626
Epoch [   41/   50] | d_loss: 0.1114 | g_loss: 0.5812
Epoch [   41/   50] | d_loss: 0.0539 | g_loss: 1.0913
Epoch [   41/   50] | d_loss: 0.0772 | g_loss: 0.5764
Epoch [   41/   50] | d_loss: 0.0477 | g_loss: 0.8105
Epoch [   41/   50] | d_loss: 0.0784 | g_loss: 0.5407
Epoch [   41/   50] | d_loss: 0.1348 | g_loss: 0.8201
Epoch [   41/   50] | d_loss: 0.1139 | g_loss: 1.0953
Epoch [   41/   50] | d_loss: 0.1068 | g_loss: 0.7557
Epoch [   42/   50] | d_loss: 0.1974 | g_loss: 0.6739
Epoch [   42/   50] | d_loss: 0.0629 | g_loss: 0.9897
Epoch [   42/   50] | d_loss: 0.0507 | g_loss: 0.8463
Epoch [   42/   50] | d_loss: 0.2631 | g_loss: 0.7262
Epoch [   42/   50] | d_loss: 0.1536 | g_loss: 0.3045
Epoch [   42/   50] | d_loss: 0.0854 | g_loss: 0.5443
Epoch [   42/   50] | d_loss: 0.0787 | g_loss: 1.4358
Epoch [   42/   50] | d_loss: 0.2126 | g_loss: 1.6493
Epoch [   42/   50] | d_loss: 0.0785 | g_loss: 0.9434
Epoch [   42/   50] | d_loss: 0.0730 | g_loss: 0.6170
Epoch [   42/   50] | d_loss: 0.2768 | g_loss: 0.8875
Epoch [   42/   50] | d_loss: 0.1152 | g_loss: 1.0590
Epoch [   42/   50] | d_loss: 0.1717 | g_loss: 0.9140
Epoch [   42/   50] | d_loss: 0.0625 | g_loss: 0.4839
Epoch [   42/   50] | d_loss: 0.1627 | g_loss: 0.8732
Epoch [   42/   50] | d_loss: 0.0579 | g_loss: 0.8176
Epoch [   42/   50] | d_loss: 0.0728 | g_loss: 0.7232
Epoch [   42/   50] | d_loss: 0.0724 | g_loss: 0.4458
Epoch [   42/   50] | d_loss: 0.2546 | g_loss: 0.5493
Epoch [   42/   50] | d_loss: 0.2066 | g_loss: 0.3744
Epoch [   42/   50] | d_loss: 0.0567 | g_loss: 0.6975
Epoch [   42/   50] | d_loss: 0.0625 | g_loss: 1.0635
Epoch [   42/   50] | d_loss: 0.0503 | g_loss: 0.9411
Epoch [   42/   50] | d_loss: 0.0776 | g_loss: 0.8102
Epoch [   42/   50] | d_loss: 0.3510 | g_loss: 0.4170
Epoch [   42/   50] | d_loss: 0.1035 | g_loss: 0.7006
Epoch [   42/   50] | d_loss: 0.0830 | g_loss: 0.8451
Epoch [   42/   50] | d_loss: 0.3052 | g_loss: 0.4633
Epoch [   42/   50] | d_loss: 0.0624 | g_loss: 0.9964
Epoch [   42/   50] | d_loss: 0.0600 | g_loss: 1.0167
Epoch [   42/   50] | d_loss: 0.0934 | g_loss: 0.8245
Epoch [   42/   50] | d_loss: 0.1953 | g_loss: 1.1422
Epoch [   42/   50] | d_loss: 0.1460 | g_loss: 0.6954
Epoch [   42/   50] | d_loss: 0.0578 | g_loss: 0.5293
Epoch [   42/   50] | d_loss: 0.1808 | g_loss: 0.9787
Epoch [   42/   50] | d_loss: 0.0838 | g_loss: 1.3154
Epoch [   42/   50] | d_loss: 0.1645 | g_loss: 0.5752
Epoch [   42/   50] | d_loss: 0.0806 | g_loss: 0.7533
Epoch [   42/   50] | d_loss: 0.1218 | g_loss: 1.2563
Epoch [   42/   50] | d_loss: 0.1114 | g_loss: 0.4026
Epoch [   42/   50] | d_loss: 0.0896 | g_loss: 0.8588
Epoch [   42/   50] | d_loss: 0.0565 | g_loss: 0.5668
Epoch [   42/   50] | d_loss: 0.1025 | g_loss: 0.6824
Epoch [   42/   50] | d_loss: 0.1647 | g_loss: 0.6109
Epoch [   42/   50] | d_loss: 0.0484 | g_loss: 0.9285
Epoch [   42/   50] | d_loss: 0.0771 | g_loss: 0.7471
Epoch [   42/   50] | d_loss: 0.1053 | g_loss: 0.9654
Epoch [   42/   50] | d_loss: 0.0652 | g_loss: 0.9820
Epoch [   42/   50] | d_loss: 0.1414 | g_loss: 0.8395
Epoch [   42/   50] | d_loss: 0.1069 | g_loss: 0.9615
Epoch [   42/   50] | d_loss: 0.0681 | g_loss: 0.6593
Epoch [   42/   50] | d_loss: 0.1541 | g_loss: 0.7520
Epoch [   42/   50] | d_loss: 0.2189 | g_loss: 0.8154
Epoch [   42/   50] | d_loss: 0.1404 | g_loss: 0.8564
Epoch [   42/   50] | d_loss: 0.2219 | g_loss: 0.3582
Epoch [   42/   50] | d_loss: 0.1643 | g_loss: 1.1581
Epoch [   42/   50] | d_loss: 0.1447 | g_loss: 0.9964
Epoch [   43/   50] | d_loss: 0.1383 | g_loss: 0.4216
Epoch [   43/   50] | d_loss: 0.5391 | g_loss: 0.2155
Epoch [   43/   50] | d_loss: 0.2424 | g_loss: 1.2903
Epoch [   43/   50] | d_loss: 0.1785 | g_loss: 0.2792
Epoch [   43/   50] | d_loss: 0.0846 | g_loss: 0.5062
Epoch [   43/   50] | d_loss: 0.1641 | g_loss: 0.4225
Epoch [   43/   50] | d_loss: 0.0727 | g_loss: 1.0042
Epoch [   43/   50] | d_loss: 0.0817 | g_loss: 0.9691
Epoch [   43/   50] | d_loss: 0.1029 | g_loss: 0.6958
Epoch [   43/   50] | d_loss: 0.0887 | g_loss: 1.0232
Epoch [   43/   50] | d_loss: 0.0604 | g_loss: 0.9286
Epoch [   43/   50] | d_loss: 0.1128 | g_loss: 0.9296
Epoch [   43/   50] | d_loss: 0.1333 | g_loss: 0.8830
Epoch [   43/   50] | d_loss: 0.1307 | g_loss: 0.6803
Epoch [   43/   50] | d_loss: 0.0751 | g_loss: 1.2415
Epoch [   43/   50] | d_loss: 0.1512 | g_loss: 1.0023
Epoch [   43/   50] | d_loss: 0.1243 | g_loss: 0.4096
Epoch [   43/   50] | d_loss: 0.0530 | g_loss: 0.8546
Epoch [   43/   50] | d_loss: 0.1275 | g_loss: 0.8891
Epoch [   43/   50] | d_loss: 0.2040 | g_loss: 0.6784
Epoch [   43/   50] | d_loss: 0.1527 | g_loss: 1.1501
Epoch [   43/   50] | d_loss: 0.1029 | g_loss: 0.5768
Epoch [   43/   50] | d_loss: 0.0846 | g_loss: 0.7203
Epoch [   43/   50] | d_loss: 0.0829 | g_loss: 0.8968
Epoch [   43/   50] | d_loss: 0.2161 | g_loss: 1.0627
Epoch [   43/   50] | d_loss: 0.1396 | g_loss: 0.6799
Epoch [   43/   50] | d_loss: 0.1527 | g_loss: 0.6710
Epoch [   43/   50] | d_loss: 0.1282 | g_loss: 0.9495
Epoch [   43/   50] | d_loss: 0.0997 | g_loss: 1.2063
Epoch [   43/   50] | d_loss: 0.0674 | g_loss: 0.9010
Epoch [   43/   50] | d_loss: 0.1159 | g_loss: 1.1794
Epoch [   43/   50] | d_loss: 0.0879 | g_loss: 0.4761
Epoch [   43/   50] | d_loss: 0.0785 | g_loss: 0.6767
Epoch [   43/   50] | d_loss: 0.0428 | g_loss: 0.6607
Epoch [   43/   50] | d_loss: 0.1496 | g_loss: 0.8663
Epoch [   43/   50] | d_loss: 0.3012 | g_loss: 0.4288
Epoch [   43/   50] | d_loss: 0.1068 | g_loss: 0.8909
Epoch [   43/   50] | d_loss: 0.1099 | g_loss: 0.5371
Epoch [   43/   50] | d_loss: 0.1160 | g_loss: 0.7486
Epoch [   43/   50] | d_loss: 0.0769 | g_loss: 1.1986
Epoch [   43/   50] | d_loss: 0.1473 | g_loss: 0.5845
Epoch [   43/   50] | d_loss: 0.0804 | g_loss: 0.6391
Epoch [   43/   50] | d_loss: 0.0527 | g_loss: 0.9799
Epoch [   43/   50] | d_loss: 0.0543 | g_loss: 1.0388
Epoch [   43/   50] | d_loss: 0.1202 | g_loss: 0.7780
Epoch [   43/   50] | d_loss: 0.2449 | g_loss: 0.4105
Epoch [   43/   50] | d_loss: 0.1404 | g_loss: 0.9794
Epoch [   43/   50] | d_loss: 0.0811 | g_loss: 0.9588
Epoch [   43/   50] | d_loss: 0.1269 | g_loss: 0.5335
Epoch [   43/   50] | d_loss: 0.1274 | g_loss: 0.7580
Epoch [   43/   50] | d_loss: 0.0512 | g_loss: 0.7771
Epoch [   43/   50] | d_loss: 0.1792 | g_loss: 1.3044
Epoch [   43/   50] | d_loss: 0.0347 | g_loss: 0.9973
Epoch [   43/   50] | d_loss: 0.0779 | g_loss: 0.7963
Epoch [   43/   50] | d_loss: 0.1032 | g_loss: 0.9473
Epoch [   43/   50] | d_loss: 0.0860 | g_loss: 0.7471
Epoch [   43/   50] | d_loss: 0.1280 | g_loss: 0.8400
Epoch [   44/   50] | d_loss: 0.2529 | g_loss: 0.8273
Epoch [   44/   50] | d_loss: 0.0870 | g_loss: 0.7037
Epoch [   44/   50] | d_loss: 0.0871 | g_loss: 1.0453
Epoch [   44/   50] | d_loss: 0.1514 | g_loss: 0.4755
Epoch [   44/   50] | d_loss: 0.2108 | g_loss: 1.0700
Epoch [   44/   50] | d_loss: 0.1027 | g_loss: 0.8462
Epoch [   44/   50] | d_loss: 0.0832 | g_loss: 0.6564
Epoch [   44/   50] | d_loss: 0.2593 | g_loss: 0.4610
Epoch [   44/   50] | d_loss: 0.0650 | g_loss: 0.9721
Epoch [   44/   50] | d_loss: 0.2210 | g_loss: 0.6876
Epoch [   44/   50] | d_loss: 0.0330 | g_loss: 0.8241
Epoch [   44/   50] | d_loss: 0.0855 | g_loss: 1.1496
Epoch [   44/   50] | d_loss: 0.0587 | g_loss: 1.0423
Epoch [   44/   50] | d_loss: 0.1194 | g_loss: 1.0528
Epoch [   44/   50] | d_loss: 0.0534 | g_loss: 1.0141
Epoch [   44/   50] | d_loss: 0.1102 | g_loss: 0.8313
Epoch [   44/   50] | d_loss: 0.1263 | g_loss: 0.5798
Epoch [   44/   50] | d_loss: 0.0709 | g_loss: 0.7018
Epoch [   44/   50] | d_loss: 0.3311 | g_loss: 1.4922
Epoch [   44/   50] | d_loss: 0.0959 | g_loss: 1.0438
Epoch [   44/   50] | d_loss: 0.0964 | g_loss: 0.9759
Epoch [   44/   50] | d_loss: 0.1075 | g_loss: 0.5498
Epoch [   44/   50] | d_loss: 0.0568 | g_loss: 0.9957
Epoch [   44/   50] | d_loss: 0.1690 | g_loss: 0.8069
Epoch [   44/   50] | d_loss: 0.0689 | g_loss: 1.2542
Epoch [   44/   50] | d_loss: 0.0728 | g_loss: 1.0934
Epoch [   44/   50] | d_loss: 0.1262 | g_loss: 0.7021
Epoch [   44/   50] | d_loss: 0.0934 | g_loss: 0.6812
Epoch [   44/   50] | d_loss: 0.0486 | g_loss: 0.7154
Epoch [   44/   50] | d_loss: 0.0737 | g_loss: 0.5170
Epoch [   44/   50] | d_loss: 0.0720 | g_loss: 0.7360
Epoch [   44/   50] | d_loss: 0.2591 | g_loss: 0.6714
Epoch [   44/   50] | d_loss: 0.0475 | g_loss: 0.8768
Epoch [   44/   50] | d_loss: 0.0794 | g_loss: 0.6062
Epoch [   44/   50] | d_loss: 0.0708 | g_loss: 0.7828
Epoch [   44/   50] | d_loss: 0.1878 | g_loss: 1.2227
Epoch [   44/   50] | d_loss: 0.1247 | g_loss: 0.7317
Epoch [   44/   50] | d_loss: 0.0964 | g_loss: 0.7304
Epoch [   44/   50] | d_loss: 0.0818 | g_loss: 0.7856
Epoch [   44/   50] | d_loss: 0.1503 | g_loss: 0.8513
Epoch [   44/   50] | d_loss: 0.1226 | g_loss: 0.5689
Epoch [   44/   50] | d_loss: 0.1267 | g_loss: 0.8939
Epoch [   44/   50] | d_loss: 0.0496 | g_loss: 0.8183
Epoch [   44/   50] | d_loss: 0.1053 | g_loss: 1.0039
Epoch [   44/   50] | d_loss: 0.1836 | g_loss: 1.0807
Epoch [   44/   50] | d_loss: 0.0871 | g_loss: 1.0717
Epoch [   44/   50] | d_loss: 0.1327 | g_loss: 1.0178
Epoch [   44/   50] | d_loss: 0.1282 | g_loss: 0.7525
Epoch [   44/   50] | d_loss: 0.0941 | g_loss: 1.0776
Epoch [   44/   50] | d_loss: 0.0555 | g_loss: 0.8967
Epoch [   44/   50] | d_loss: 0.0601 | g_loss: 0.7502
Epoch [   44/   50] | d_loss: 0.1005 | g_loss: 0.6085
Epoch [   44/   50] | d_loss: 0.2860 | g_loss: 0.8335
Epoch [   44/   50] | d_loss: 0.2273 | g_loss: 0.5357
Epoch [   44/   50] | d_loss: 0.1388 | g_loss: 1.1882
Epoch [   44/   50] | d_loss: 0.0808 | g_loss: 1.0293
Epoch [   44/   50] | d_loss: 0.0840 | g_loss: 0.9575
Epoch [   45/   50] | d_loss: 0.3186 | g_loss: 0.9003
Epoch [   45/   50] | d_loss: 0.2038 | g_loss: 0.4887
Epoch [   45/   50] | d_loss: 0.0668 | g_loss: 0.8986
Epoch [   45/   50] | d_loss: 0.1355 | g_loss: 0.6009
Epoch [   45/   50] | d_loss: 0.0537 | g_loss: 0.9710
Epoch [   45/   50] | d_loss: 0.2083 | g_loss: 0.5562
Epoch [   45/   50] | d_loss: 0.1483 | g_loss: 1.0845
Epoch [   45/   50] | d_loss: 0.0561 | g_loss: 1.2501
Epoch [   45/   50] | d_loss: 0.0472 | g_loss: 0.7138
Epoch [   45/   50] | d_loss: 0.1262 | g_loss: 0.9208
Epoch [   45/   50] | d_loss: 0.1602 | g_loss: 1.2819
Epoch [   45/   50] | d_loss: 0.2193 | g_loss: 0.6232
Epoch [   45/   50] | d_loss: 0.1301 | g_loss: 0.9890
Epoch [   45/   50] | d_loss: 0.1136 | g_loss: 1.1168
Epoch [   45/   50] | d_loss: 0.1589 | g_loss: 0.3214
Epoch [   45/   50] | d_loss: 0.0929 | g_loss: 0.8417
Epoch [   45/   50] | d_loss: 0.2019 | g_loss: 0.6689
Epoch [   45/   50] | d_loss: 0.1600 | g_loss: 0.6366
Epoch [   45/   50] | d_loss: 0.0919 | g_loss: 0.7709
Epoch [   45/   50] | d_loss: 0.0463 | g_loss: 0.6742
Epoch [   45/   50] | d_loss: 0.0484 | g_loss: 0.8252
Epoch [   45/   50] | d_loss: 0.0663 | g_loss: 0.6792
Epoch [   45/   50] | d_loss: 0.0853 | g_loss: 0.5845
Epoch [   45/   50] | d_loss: 0.0520 | g_loss: 0.5721
Epoch [   45/   50] | d_loss: 0.0896 | g_loss: 0.6798
Epoch [   45/   50] | d_loss: 0.1189 | g_loss: 0.5991
Epoch [   45/   50] | d_loss: 0.0907 | g_loss: 0.8753
Epoch [   45/   50] | d_loss: 0.2320 | g_loss: 0.9811
Epoch [   45/   50] | d_loss: 0.0575 | g_loss: 0.7266
Epoch [   45/   50] | d_loss: 0.1185 | g_loss: 1.1209
Epoch [   45/   50] | d_loss: 0.0601 | g_loss: 1.0349
Epoch [   45/   50] | d_loss: 0.0666 | g_loss: 1.0488
Epoch [   45/   50] | d_loss: 0.0556 | g_loss: 0.6261
Epoch [   45/   50] | d_loss: 0.0552 | g_loss: 1.2359
Epoch [   45/   50] | d_loss: 0.1972 | g_loss: 1.0998
Epoch [   45/   50] | d_loss: 0.0893 | g_loss: 0.6452
Epoch [   45/   50] | d_loss: 0.2381 | g_loss: 0.5436
Epoch [   45/   50] | d_loss: 0.2242 | g_loss: 1.0905
Epoch [   45/   50] | d_loss: 0.1316 | g_loss: 0.4010
Epoch [   45/   50] | d_loss: 0.1981 | g_loss: 0.3747
Epoch [   45/   50] | d_loss: 0.4248 | g_loss: 1.3696
Epoch [   45/   50] | d_loss: 0.1782 | g_loss: 1.3279
Epoch [   45/   50] | d_loss: 0.0637 | g_loss: 0.7353
Epoch [   45/   50] | d_loss: 0.0654 | g_loss: 0.5541
Epoch [   45/   50] | d_loss: 0.0483 | g_loss: 0.4315
Epoch [   45/   50] | d_loss: 0.1079 | g_loss: 0.5961
Epoch [   45/   50] | d_loss: 0.2364 | g_loss: 0.9762
Epoch [   45/   50] | d_loss: 0.1404 | g_loss: 0.7655
Epoch [   45/   50] | d_loss: 0.1057 | g_loss: 1.5287
Epoch [   45/   50] | d_loss: 0.0975 | g_loss: 0.7914
Epoch [   45/   50] | d_loss: 0.2358 | g_loss: 1.3412
Epoch [   45/   50] | d_loss: 0.1936 | g_loss: 0.2884
Epoch [   45/   50] | d_loss: 0.0685 | g_loss: 0.7902
Epoch [   45/   50] | d_loss: 0.1272 | g_loss: 0.5839
Epoch [   45/   50] | d_loss: 0.1303 | g_loss: 0.6148
Epoch [   45/   50] | d_loss: 0.0535 | g_loss: 0.7606
Epoch [   45/   50] | d_loss: 0.1297 | g_loss: 0.8340
Epoch [   46/   50] | d_loss: 0.2338 | g_loss: 0.6259
Epoch [   46/   50] | d_loss: 0.1412 | g_loss: 0.5220
Epoch [   46/   50] | d_loss: 0.1021 | g_loss: 0.6660
Epoch [   46/   50] | d_loss: 0.0846 | g_loss: 1.0937
Epoch [   46/   50] | d_loss: 0.1586 | g_loss: 0.9622
Epoch [   46/   50] | d_loss: 0.1301 | g_loss: 0.9011
Epoch [   46/   50] | d_loss: 0.1089 | g_loss: 0.7386
Epoch [   46/   50] | d_loss: 0.1701 | g_loss: 0.6420
Epoch [   46/   50] | d_loss: 0.1448 | g_loss: 0.9449
Epoch [   46/   50] | d_loss: 0.0964 | g_loss: 0.9761
Epoch [   46/   50] | d_loss: 0.1500 | g_loss: 0.6340
Epoch [   46/   50] | d_loss: 0.0987 | g_loss: 0.6131
Epoch [   46/   50] | d_loss: 0.0746 | g_loss: 0.9495
Epoch [   46/   50] | d_loss: 0.1179 | g_loss: 0.9001
Epoch [   46/   50] | d_loss: 0.0493 | g_loss: 0.3917
Epoch [   46/   50] | d_loss: 0.0939 | g_loss: 0.9579
Epoch [   46/   50] | d_loss: 0.2896 | g_loss: 1.1612
Epoch [   46/   50] | d_loss: 0.0570 | g_loss: 1.4020
Epoch [   46/   50] | d_loss: 0.0990 | g_loss: 0.7594
Epoch [   46/   50] | d_loss: 0.0673 | g_loss: 0.8831
Epoch [   46/   50] | d_loss: 0.0892 | g_loss: 1.1771
Epoch [   46/   50] | d_loss: 0.1709 | g_loss: 0.8906
Epoch [   46/   50] | d_loss: 0.1503 | g_loss: 0.9900
Epoch [   46/   50] | d_loss: 0.0585 | g_loss: 1.0810
Epoch [   46/   50] | d_loss: 0.1775 | g_loss: 0.3463
Epoch [   46/   50] | d_loss: 0.1161 | g_loss: 0.9134
Epoch [   46/   50] | d_loss: 0.0886 | g_loss: 0.7909
Epoch [   46/   50] | d_loss: 0.0306 | g_loss: 0.9351
Epoch [   46/   50] | d_loss: 0.0492 | g_loss: 1.1278
Epoch [   46/   50] | d_loss: 0.1229 | g_loss: 0.4967
Epoch [   46/   50] | d_loss: 0.1228 | g_loss: 0.6047
Epoch [   46/   50] | d_loss: 0.1074 | g_loss: 0.9153
Epoch [   46/   50] | d_loss: 0.0775 | g_loss: 0.5386
Epoch [   46/   50] | d_loss: 0.1067 | g_loss: 1.1586
Epoch [   46/   50] | d_loss: 0.0785 | g_loss: 0.7141
Epoch [   46/   50] | d_loss: 0.0863 | g_loss: 0.8752
Epoch [   46/   50] | d_loss: 0.1584 | g_loss: 0.6474
Epoch [   46/   50] | d_loss: 0.0541 | g_loss: 1.0251
Epoch [   46/   50] | d_loss: 0.1062 | g_loss: 1.5087
Epoch [   46/   50] | d_loss: 0.0723 | g_loss: 0.9293
Epoch [   46/   50] | d_loss: 0.0729 | g_loss: 1.0219
Epoch [   46/   50] | d_loss: 0.0742 | g_loss: 0.7492
Epoch [   46/   50] | d_loss: 0.1497 | g_loss: 0.5904
Epoch [   46/   50] | d_loss: 0.1062 | g_loss: 0.8804
Epoch [   46/   50] | d_loss: 0.1054 | g_loss: 0.7189
Epoch [   46/   50] | d_loss: 0.0895 | g_loss: 0.6622
Epoch [   46/   50] | d_loss: 0.2455 | g_loss: 1.1565
Epoch [   46/   50] | d_loss: 0.1333 | g_loss: 0.7353
Epoch [   46/   50] | d_loss: 0.2301 | g_loss: 0.7515
Epoch [   46/   50] | d_loss: 0.0429 | g_loss: 0.7875
Epoch [   46/   50] | d_loss: 0.0518 | g_loss: 0.9910
Epoch [   46/   50] | d_loss: 0.1312 | g_loss: 0.7143
Epoch [   46/   50] | d_loss: 0.2039 | g_loss: 0.7052
Epoch [   46/   50] | d_loss: 0.0934 | g_loss: 0.6203
Epoch [   46/   50] | d_loss: 0.1064 | g_loss: 0.9273
Epoch [   46/   50] | d_loss: 0.1364 | g_loss: 0.6145
Epoch [   46/   50] | d_loss: 0.1461 | g_loss: 1.0332
Epoch [   47/   50] | d_loss: 0.3043 | g_loss: 0.6868
Epoch [   47/   50] | d_loss: 0.1079 | g_loss: 0.8817
Epoch [   47/   50] | d_loss: 0.0632 | g_loss: 1.3532
Epoch [   47/   50] | d_loss: 0.0728 | g_loss: 0.6157
Epoch [   47/   50] | d_loss: 0.2675 | g_loss: 1.4897
Epoch [   47/   50] | d_loss: 0.0624 | g_loss: 1.0526
Epoch [   47/   50] | d_loss: 0.1427 | g_loss: 0.7719
Epoch [   47/   50] | d_loss: 0.0986 | g_loss: 1.1110
Epoch [   47/   50] | d_loss: 0.0385 | g_loss: 1.0676
Epoch [   47/   50] | d_loss: 0.0860 | g_loss: 0.7194
Epoch [   47/   50] | d_loss: 0.1335 | g_loss: 0.8836
Epoch [   47/   50] | d_loss: 0.1381 | g_loss: 0.6325
Epoch [   47/   50] | d_loss: 0.2621 | g_loss: 1.1095
Epoch [   47/   50] | d_loss: 0.1482 | g_loss: 0.8993
Epoch [   47/   50] | d_loss: 0.0992 | g_loss: 0.6534
Epoch [   47/   50] | d_loss: 0.0780 | g_loss: 0.8822
Epoch [   47/   50] | d_loss: 0.1496 | g_loss: 0.7978
Epoch [   47/   50] | d_loss: 0.0998 | g_loss: 0.6454
Epoch [   47/   50] | d_loss: 0.0955 | g_loss: 1.4551
Epoch [   47/   50] | d_loss: 0.0423 | g_loss: 1.1353
Epoch [   47/   50] | d_loss: 0.0809 | g_loss: 0.8592
Epoch [   47/   50] | d_loss: 0.0515 | g_loss: 0.7380
Epoch [   47/   50] | d_loss: 0.0552 | g_loss: 0.6990
Epoch [   47/   50] | d_loss: 0.0971 | g_loss: 1.0552
Epoch [   47/   50] | d_loss: 0.0703 | g_loss: 0.8593
Epoch [   47/   50] | d_loss: 0.1765 | g_loss: 0.9405
Epoch [   47/   50] | d_loss: 0.0957 | g_loss: 0.9219
Epoch [   47/   50] | d_loss: 0.1017 | g_loss: 0.7867
Epoch [   47/   50] | d_loss: 0.0909 | g_loss: 0.7897
Epoch [   47/   50] | d_loss: 0.0615 | g_loss: 0.8907
Epoch [   47/   50] | d_loss: 0.0478 | g_loss: 0.4339
Epoch [   47/   50] | d_loss: 0.1325 | g_loss: 0.8740
Epoch [   47/   50] | d_loss: 0.1047 | g_loss: 1.4289
Epoch [   47/   50] | d_loss: 0.1910 | g_loss: 0.4545
Epoch [   47/   50] | d_loss: 0.1002 | g_loss: 0.8423
Epoch [   47/   50] | d_loss: 0.1981 | g_loss: 0.4502
Epoch [   47/   50] | d_loss: 0.0851 | g_loss: 1.0064
Epoch [   47/   50] | d_loss: 0.1250 | g_loss: 0.5806
Epoch [   47/   50] | d_loss: 0.2805 | g_loss: 0.6114
Epoch [   47/   50] | d_loss: 0.1373 | g_loss: 0.5824
Epoch [   47/   50] | d_loss: 0.0962 | g_loss: 0.9593
Epoch [   47/   50] | d_loss: 0.0660 | g_loss: 0.8210
Epoch [   47/   50] | d_loss: 0.0647 | g_loss: 0.8523
Epoch [   47/   50] | d_loss: 0.0683 | g_loss: 1.1810
Epoch [   47/   50] | d_loss: 0.1296 | g_loss: 0.8857
Epoch [   47/   50] | d_loss: 0.0821 | g_loss: 0.5077
Epoch [   47/   50] | d_loss: 0.1703 | g_loss: 0.8532
Epoch [   47/   50] | d_loss: 0.1340 | g_loss: 0.3932
Epoch [   47/   50] | d_loss: 0.1695 | g_loss: 0.9871
Epoch [   47/   50] | d_loss: 0.0675 | g_loss: 0.8129
Epoch [   47/   50] | d_loss: 0.1758 | g_loss: 0.4269
Epoch [   47/   50] | d_loss: 0.1039 | g_loss: 1.0447
Epoch [   47/   50] | d_loss: 0.0760 | g_loss: 0.8517
Epoch [   47/   50] | d_loss: 0.0888 | g_loss: 0.7360
Epoch [   47/   50] | d_loss: 0.1066 | g_loss: 1.1775
Epoch [   47/   50] | d_loss: 0.1942 | g_loss: 0.8403
Epoch [   47/   50] | d_loss: 0.1644 | g_loss: 1.0075
Epoch [   48/   50] | d_loss: 0.1055 | g_loss: 0.7615
Epoch [   48/   50] | d_loss: 0.0979 | g_loss: 0.9359
Epoch [   48/   50] | d_loss: 0.0481 | g_loss: 1.0855
Epoch [   48/   50] | d_loss: 0.1754 | g_loss: 1.1154
Epoch [   48/   50] | d_loss: 0.0834 | g_loss: 0.9841
Epoch [   48/   50] | d_loss: 0.0754 | g_loss: 0.9151
Epoch [   48/   50] | d_loss: 0.1032 | g_loss: 1.1107
Epoch [   48/   50] | d_loss: 0.1134 | g_loss: 0.7587
Epoch [   48/   50] | d_loss: 0.0775 | g_loss: 0.7472
Epoch [   48/   50] | d_loss: 0.0715 | g_loss: 0.7553
Epoch [   48/   50] | d_loss: 0.0522 | g_loss: 0.8233
Epoch [   48/   50] | d_loss: 0.0810 | g_loss: 1.0358
Epoch [   48/   50] | d_loss: 0.0542 | g_loss: 0.8973
Epoch [   48/   50] | d_loss: 0.1345 | g_loss: 0.8912
Epoch [   48/   50] | d_loss: 0.0852 | g_loss: 1.2879
Epoch [   48/   50] | d_loss: 0.1288 | g_loss: 0.6346
Epoch [   48/   50] | d_loss: 0.0580 | g_loss: 0.9972
Epoch [   48/   50] | d_loss: 0.0968 | g_loss: 1.0840
Epoch [   48/   50] | d_loss: 0.0813 | g_loss: 0.6292
Epoch [   48/   50] | d_loss: 0.1057 | g_loss: 0.5940
Epoch [   48/   50] | d_loss: 0.1063 | g_loss: 0.7801
Epoch [   48/   50] | d_loss: 0.0952 | g_loss: 0.8215
Epoch [   48/   50] | d_loss: 0.0791 | g_loss: 0.6594
Epoch [   48/   50] | d_loss: 0.0523 | g_loss: 1.5978
Epoch [   48/   50] | d_loss: 0.0971 | g_loss: 0.8159
Epoch [   48/   50] | d_loss: 0.0815 | g_loss: 1.0230
Epoch [   48/   50] | d_loss: 0.1141 | g_loss: 0.7138
Epoch [   48/   50] | d_loss: 0.1502 | g_loss: 0.3671
Epoch [   48/   50] | d_loss: 0.1131 | g_loss: 0.6163
Epoch [   48/   50] | d_loss: 0.0877 | g_loss: 0.9830
Epoch [   48/   50] | d_loss: 0.1041 | g_loss: 0.8468
Epoch [   48/   50] | d_loss: 0.1250 | g_loss: 1.6611
Epoch [   48/   50] | d_loss: 0.1425 | g_loss: 0.4913
Epoch [   48/   50] | d_loss: 0.0637 | g_loss: 0.8267
Epoch [   48/   50] | d_loss: 0.0824 | g_loss: 0.7556
Epoch [   48/   50] | d_loss: 0.0786 | g_loss: 0.7403
Epoch [   48/   50] | d_loss: 0.1948 | g_loss: 0.9442
Epoch [   48/   50] | d_loss: 0.1192 | g_loss: 0.7454
Epoch [   48/   50] | d_loss: 0.1092 | g_loss: 0.7192
Epoch [   48/   50] | d_loss: 0.1108 | g_loss: 0.6050
Epoch [   48/   50] | d_loss: 0.1199 | g_loss: 0.5923
Epoch [   48/   50] | d_loss: 0.0529 | g_loss: 0.9502
Epoch [   48/   50] | d_loss: 0.1573 | g_loss: 1.0736
Epoch [   48/   50] | d_loss: 0.1127 | g_loss: 0.4067
Epoch [   48/   50] | d_loss: 0.0781 | g_loss: 0.8109
Epoch [   48/   50] | d_loss: 0.0580 | g_loss: 1.1493
Epoch [   48/   50] | d_loss: 0.1457 | g_loss: 0.9081
Epoch [   48/   50] | d_loss: 0.0719 | g_loss: 0.9026
Epoch [   48/   50] | d_loss: 0.1810 | g_loss: 1.0079
Epoch [   48/   50] | d_loss: 0.0871 | g_loss: 1.0071
Epoch [   48/   50] | d_loss: 0.1100 | g_loss: 1.4026
Epoch [   48/   50] | d_loss: 0.2583 | g_loss: 1.1671
Epoch [   48/   50] | d_loss: 0.0387 | g_loss: 1.1952
Epoch [   48/   50] | d_loss: 0.2772 | g_loss: 0.2518
Epoch [   48/   50] | d_loss: 0.0673 | g_loss: 0.7050
Epoch [   48/   50] | d_loss: 0.1350 | g_loss: 0.8977
Epoch [   48/   50] | d_loss: 0.0793 | g_loss: 0.6565
Epoch [   49/   50] | d_loss: 0.1383 | g_loss: 1.0662
Epoch [   49/   50] | d_loss: 0.1222 | g_loss: 1.3994
Epoch [   49/   50] | d_loss: 0.0419 | g_loss: 0.8077
Epoch [   49/   50] | d_loss: 0.0789 | g_loss: 0.7598
Epoch [   49/   50] | d_loss: 0.2443 | g_loss: 0.5568
Epoch [   49/   50] | d_loss: 0.0914 | g_loss: 0.9335
Epoch [   49/   50] | d_loss: 0.0739 | g_loss: 0.9304
Epoch [   49/   50] | d_loss: 0.1925 | g_loss: 0.5870
Epoch [   49/   50] | d_loss: 0.1689 | g_loss: 0.8093
Epoch [   49/   50] | d_loss: 0.1287 | g_loss: 0.5180
Epoch [   49/   50] | d_loss: 0.1092 | g_loss: 0.5304
Epoch [   49/   50] | d_loss: 0.0549 | g_loss: 0.7750
Epoch [   49/   50] | d_loss: 0.1537 | g_loss: 1.0550
Epoch [   49/   50] | d_loss: 0.0874 | g_loss: 0.8265
Epoch [   49/   50] | d_loss: 0.0582 | g_loss: 0.8404
Epoch [   49/   50] | d_loss: 0.0887 | g_loss: 0.8410
Epoch [   49/   50] | d_loss: 0.2930 | g_loss: 0.3669
Epoch [   49/   50] | d_loss: 0.0565 | g_loss: 1.0133
Epoch [   49/   50] | d_loss: 0.0732 | g_loss: 1.0263
Epoch [   49/   50] | d_loss: 0.0983 | g_loss: 0.9341
Epoch [   49/   50] | d_loss: 0.0386 | g_loss: 1.3751
Epoch [   49/   50] | d_loss: 0.0761 | g_loss: 0.8914
Epoch [   49/   50] | d_loss: 0.1311 | g_loss: 0.9613
Epoch [   49/   50] | d_loss: 0.0957 | g_loss: 0.8853
Epoch [   49/   50] | d_loss: 0.0383 | g_loss: 0.9014
Epoch [   49/   50] | d_loss: 0.1107 | g_loss: 0.6690
Epoch [   49/   50] | d_loss: 0.1725 | g_loss: 0.6185
Epoch [   49/   50] | d_loss: 0.0774 | g_loss: 0.5763
Epoch [   49/   50] | d_loss: 0.1331 | g_loss: 0.6863
Epoch [   49/   50] | d_loss: 0.1146 | g_loss: 0.5413
Epoch [   49/   50] | d_loss: 0.0634 | g_loss: 0.7257
Epoch [   49/   50] | d_loss: 0.0830 | g_loss: 0.7760
Epoch [   49/   50] | d_loss: 0.0985 | g_loss: 0.7116
Epoch [   49/   50] | d_loss: 0.1378 | g_loss: 0.6103
Epoch [   49/   50] | d_loss: 0.2318 | g_loss: 0.5345
Epoch [   49/   50] | d_loss: 0.0740 | g_loss: 0.6172
Epoch [   49/   50] | d_loss: 0.0692 | g_loss: 0.6381
Epoch [   49/   50] | d_loss: 0.1399 | g_loss: 0.7767
Epoch [   49/   50] | d_loss: 0.2494 | g_loss: 0.5893
Epoch [   49/   50] | d_loss: 0.1652 | g_loss: 0.9079
Epoch [   49/   50] | d_loss: 0.1111 | g_loss: 0.8732
Epoch [   49/   50] | d_loss: 0.1575 | g_loss: 0.8673
Epoch [   49/   50] | d_loss: 0.1047 | g_loss: 0.8617
Epoch [   49/   50] | d_loss: 0.0567 | g_loss: 0.5741
Epoch [   49/   50] | d_loss: 0.1322 | g_loss: 0.6106
Epoch [   49/   50] | d_loss: 0.0785 | g_loss: 0.5913
Epoch [   49/   50] | d_loss: 0.1385 | g_loss: 0.7222
Epoch [   49/   50] | d_loss: 0.1045 | g_loss: 0.9524
Epoch [   49/   50] | d_loss: 0.0720 | g_loss: 0.6692
Epoch [   49/   50] | d_loss: 0.0934 | g_loss: 0.9957
Epoch [   49/   50] | d_loss: 0.1322 | g_loss: 0.7895
Epoch [   49/   50] | d_loss: 0.0861 | g_loss: 0.9688
Epoch [   49/   50] | d_loss: 0.1260 | g_loss: 0.8470
Epoch [   49/   50] | d_loss: 0.3282 | g_loss: 0.7270
Epoch [   49/   50] | d_loss: 0.1281 | g_loss: 0.8400
Epoch [   49/   50] | d_loss: 0.0467 | g_loss: 1.0525
Epoch [   49/   50] | d_loss: 0.0982 | g_loss: 0.9895
Epoch [   50/   50] | d_loss: 0.1401 | g_loss: 0.5018
Epoch [   50/   50] | d_loss: 0.1118 | g_loss: 1.0712
Epoch [   50/   50] | d_loss: 0.1079 | g_loss: 0.6255
Epoch [   50/   50] | d_loss: 0.1726 | g_loss: 1.0594
Epoch [   50/   50] | d_loss: 0.0843 | g_loss: 0.9527
Epoch [   50/   50] | d_loss: 0.0914 | g_loss: 0.6765
Epoch [   50/   50] | d_loss: 0.0805 | g_loss: 0.7942
Epoch [   50/   50] | d_loss: 0.1350 | g_loss: 0.5723
Epoch [   50/   50] | d_loss: 0.1249 | g_loss: 0.9475
Epoch [   50/   50] | d_loss: 0.0637 | g_loss: 1.2183
Epoch [   50/   50] | d_loss: 0.0696 | g_loss: 0.6833
Epoch [   50/   50] | d_loss: 0.1645 | g_loss: 0.7164
Epoch [   50/   50] | d_loss: 0.0956 | g_loss: 1.2856
Epoch [   50/   50] | d_loss: 0.3283 | g_loss: 0.6904
Epoch [   50/   50] | d_loss: 0.0434 | g_loss: 0.5518
Epoch [   50/   50] | d_loss: 0.1009 | g_loss: 0.5225
Epoch [   50/   50] | d_loss: 0.0679 | g_loss: 0.9034
Epoch [   50/   50] | d_loss: 0.0786 | g_loss: 0.7456
Epoch [   50/   50] | d_loss: 0.0816 | g_loss: 0.8636
Epoch [   50/   50] | d_loss: 0.0465 | g_loss: 0.5964
Epoch [   50/   50] | d_loss: 0.0831 | g_loss: 1.2296
Epoch [   50/   50] | d_loss: 0.1212 | g_loss: 1.1688
Epoch [   50/   50] | d_loss: 0.1139 | g_loss: 0.4769
Epoch [   50/   50] | d_loss: 0.0648 | g_loss: 1.0010
Epoch [   50/   50] | d_loss: 0.0773 | g_loss: 0.7422
Epoch [   50/   50] | d_loss: 0.0955 | g_loss: 0.8399
Epoch [   50/   50] | d_loss: 0.0634 | g_loss: 0.8882
Epoch [   50/   50] | d_loss: 0.2817 | g_loss: 0.8395
Epoch [   50/   50] | d_loss: 0.0727 | g_loss: 0.7106
Epoch [   50/   50] | d_loss: 0.1269 | g_loss: 0.5298
Epoch [   50/   50] | d_loss: 0.0749 | g_loss: 1.1243
Epoch [   50/   50] | d_loss: 0.0996 | g_loss: 0.9504
Epoch [   50/   50] | d_loss: 0.0902 | g_loss: 1.1887
Epoch [   50/   50] | d_loss: 0.0955 | g_loss: 0.7242
Epoch [   50/   50] | d_loss: 0.0650 | g_loss: 1.2950
Epoch [   50/   50] | d_loss: 0.0756 | g_loss: 0.9113
Epoch [   50/   50] | d_loss: 0.1131 | g_loss: 0.6853
Epoch [   50/   50] | d_loss: 0.0399 | g_loss: 0.9434
Epoch [   50/   50] | d_loss: 0.1289 | g_loss: 0.6608
Epoch [   50/   50] | d_loss: 0.0475 | g_loss: 1.1754
Epoch [   50/   50] | d_loss: 0.0961 | g_loss: 0.9177
Epoch [   50/   50] | d_loss: 0.0834 | g_loss: 0.9897
Epoch [   50/   50] | d_loss: 0.1907 | g_loss: 0.9076
Epoch [   50/   50] | d_loss: 0.1246 | g_loss: 0.6444
Epoch [   50/   50] | d_loss: 0.1147 | g_loss: 1.2864
Epoch [   50/   50] | d_loss: 0.1422 | g_loss: 0.9766
Epoch [   50/   50] | d_loss: 0.0652 | g_loss: 0.7566
Epoch [   50/   50] | d_loss: 0.0537 | g_loss: 1.3228
Epoch [   50/   50] | d_loss: 0.0387 | g_loss: 0.8687
Epoch [   50/   50] | d_loss: 0.1364 | g_loss: 0.9976
Epoch [   50/   50] | d_loss: 0.0828 | g_loss: 1.0534
Epoch [   50/   50] | d_loss: 0.0500 | g_loss: 0.9451
Epoch [   50/   50] | d_loss: 0.1013 | g_loss: 0.9106
Epoch [   50/   50] | d_loss: 0.0525 | g_loss: 0.9230
Epoch [   50/   50] | d_loss: 0.0618 | g_loss: 0.8427
Epoch [   50/   50] | d_loss: 0.1989 | g_loss: 0.5289
Epoch [   50/   50] | d_loss: 0.0608 | g_loss: 0.5143
</pre>
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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="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[21]:</div>
<div class="inner_cell">
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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>
</pre></div>

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    <div class="prompt output_prompt">Out[21]:</div>


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<pre>&lt;matplotlib.legend.Legend at 0x7fa8f5fc1a20&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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iX/JXQZ74l+583+rS959cfzvxT45JRu+sC500jGOIpTlQdslbj9iO0R7MuYx3IezX7NZ8eUceQ4au1p1qWscSZxu/4QYlLm6SKNl/7uB3FtfdzI+89m0qfOcXyGODzNZOyZom8mmFPFwFzZdKuGW6F6927UYzByJ2Z7lPIn/l1jjrzcIjYA9d9grNgC763M/v7+9oe3w/G7Bymn8zNpS50/HDXXFUmVSqVSqVQqlUqlUh0k/SGpUqlUKpVKpVKpVKqDdDDaavs1fCYGtvg9OvplSgyCTkDACT6/EezqH/6DfzAc/+zfksSZE7oqBbIk3dIlCscRkv/anj/gqrsHHuJKWcadfCXv9BLusB9/EFxn81HQlc0ay8oRMR4Ua4kEqoHgQBEeqAMuC0PKUdLeo8pLAlsiGSFZMOK/dDPD8vwZkm5bOJ/WeO7HpSzDE/Mo4ALq6WaGxyQG53s3rcJLea6BubLdZQsp5wmwIOItxD9qOiMCJyC64veTX6PPec1R0toTJc52zpnFZNd2MvTHGRCxDPXy+kzwiHPgrHRnTej8xZcjikQUGTXW4Tp0vx1RPz0WAsrbOBSoBXJrgRrFTup9AjfC5hEOzbjTGo5nTOjtGuBmcNZsiMwBybPA2artiVyUvTdtz+4Wa2nT79ePOAfnA29aNkhujvqaZnLOi0vBVpKQjZqOhYhBuI7zrA/UaV+PxMIDbHFggmz3XIeBrefcCxpvgQ7FwLEb4FghXGzffZQYs90CRQaaFQP1LXNYDR9R3vsBS33cwoG0xDMh7s+As4Youy2eL0G8fXEh+H6EuEYiLwYSmTJ5OvE89PG2b1i+3Y/1tUCt6FBYx8C/0TjLmtsxpF4ixIMYWyjyXMqpLaVvuhEmh3YHzrCr2CmOpyBw5qx3k72YyVhyNQPOirLYwrGdeHdbIY6gHRMNnKBdEldm/UZImF5WQMAxrth+8HGIXXTKN3iuCnhgNxq/9m8rqekMyS8gHhBPfWYaYUb1iMt0p3JtNdaE/fiUr1fD5zXqZfQg4wF8OKTD7GisQhlF+Dxl/KLDJ+LXFq64dOh9cn0+h6vomzcyR35xJlswEsR4bp/hloHlo/QvhznyHeZldCbm/CecYEzgXAztKhhxv6dZl3LWmCzZPbuDIz+N97FDznz9zTfyfDjn9VzGwddXcnwBN+YJ3OGzBPWCLT9EtFu8M35WmK6fd9nRzBTtC/Gjw8+wriauK6fPEdcXOF5h3nIfyfEDymm5kgf78P79cPz1b78fjn/65RfD8V8ly4CuSKpUKpVKpVKpVCqV6iDpD0mVSqVSqVQqlUqlUh2kg9BWayUxKV26HPCAZkRHAI+Ae+f5RHCR//Df/9Ph+KuffjkcT2NBnRxcBOkGaJE8lA6bHq6SA31h9y/HW6BeSQbHSixZvwRm8pgJIrHpxOX1PpfPHRCdmkvVOA4DwQZcIkvVLL86PF2i3qjHi7IICA3KdkL3P7oL0qEXZUSUrQr3Jx6m+14A580GS/4ezlcjMzf71O7kGXM4MeYbOLGNEgvLM0bAPJncuwEiRYe6pmUbZ8NmVmImVt6P8IXhwQT5Jylw1syGBOhSLhdwHrvMpO6ugLyeZ8SJUV/oOx0QHWJU/PNTy3+MsJ/9CNDT8YieQPeOcM8QuGWANhW1SJYNzDUq5JwVHmuN47mR8+8rwSMvQolJdwbJf4FgV1en+bubN97U3a4tv33/7fD59qO4qn2B2Hh9fjkc/xztLAEzd3UGvBtInjXPMNps0uCOR0m34aZqza5u3Ohvkd3eY6KUDnEiQPx2FpgtHoYOrn/ylcSGbSfn393BlRddrcOgEKBdVTXvdTz5rjNVv5VhsxHckZh3BlQyQn2V5X4nybOpjIMToI9EZIMQeGQm1w9DooSMcRjn/J6tHyP0Ee6piLcOyOscOPoMsWe5QfxEvTD/+gTx6e7udjgu6YAIvDeGsylRwWMqDAJz1bsYzlJun8F4g6TgRCUTOGzGPA7oqg1HTqB0xLgdcMcIfYBzKjo5P2G0CIemSbBNo8T4PEpML+ePsVXEbzq+Yg5DBJ1u8yMsmeOjfGo6JzcO7WnGR+87U/fJ3iv2L+LHno6p8t0gADqO9wlxUoZ6mQMDncRyfkInYsSBin0J15/36OrlhSCsL1+I8/8U7r+O21BQdRWw2UnG/oLtHu9kbOnuZBzcoH5LuICnmKO2o21iUo+n8d7t0dZ+uxp/d7TYAvD7P/x+OH77Xly558BWr+DAfAmX+8UU/RR9cAJ0mPhxx/kqfkJ51O/wObcBoa2NYiycqztuEcN2QBfT/VieJcF2OfbTbiMNgrF0W0qZff2tzDXa5j8ejiPuyvpE6YqkSqVSqVQqlUqlUqkOkv6QVKlUKpVKpVKpVCrVQTqQKbBm+O2JtfTGkCuVw7IlWiS/WV+9uBmOf/aluLMSBSGCRbepjuidw1Ix1tuZGNU2u+MOCKuFE6GxcFbzKA4mIMW1z6/gaId33baCTmy24ox0t76Xk5w4cTH5MDGhJAY+tvkrrDF/gpy1Awo5zYitYskcODHxoyQiXoWyQCLkCMvwKTAeutBmwC+YLLyqxG2qpntm7xoaARGK8IyrlbiQ5Ru5RoB6pLOZqYmhAssB3kN0iNiqx3uHzwAd9kRJlkf3MMY8kRBTIBlENc4yebc5ULo0AT7YoZ3B4ZRJo4mXWzrbosO7URJnohjANfoydaTSeE8GEKBTCftLTU5OkPKY71fL+SUR2WSD86V+44elHE+A9BBLvkfcOKa8H5yst0vB+x7vJHlw++blcLxYiHtnYuTd5rG8/6trwV8RVo3lK9DBlXWX4Bywog4YvAl25dsyITVND4kzj4hX4O2M62iCMdpOK9Vlrq7Ph+PXj1LvX38v8bZu5EI0vXbgKcMT/f20894UvYsek7XTCZCYKx1RG+C2MZ41gesjXazpoBuPEEO6Z2IcRGwPOFz3p3O8pQOnQwzsuJWBaC3On+BZIrSBbisxuYLTYAL0jq6/FWI421gADNKPAvrx5KwxT+SbheNu0cBhFhaN4yTx8g4JYkcCrC6bAKWDGyS3XoQjjBwKyRWirfefkwgOA7bzZ7ZmMLk83Fkrjudog74hWguhbTq0gZGDK/F5ss6nMqf33jR9PYWIYy3QceK8wQgnxrg5lTaaIUn9HHOLC2wlWGArEB3DQzj0jsZNxISn/pChX0yBlcbA2FmGlnMS1Bev/fKFjAkh6ohxqP4o2wS22IrUomwMjhnPTmS+2090+kMMTutCBodf/OqX8njoBFdX4sx+dS51NFtg6xb6Y4q+zD4YhNgeMtrWR+wcsanv+360JQrjWo34VqMMLbFZbucBXo421WGQm6bYhmCl/TyuZGxpkdGiBJ7PrWm2O3zuqiuSKpVKpVKpVCqVSqU6SPpDUqVSqVQqlUqlUqlUB+lgu6wBTxjRrFgWxbJ3C1RigiXjn332ajhezLA0DDdG32C5t8Vjwu3Ikofib2Kgq09PBoLVeDoKjpZxgWc0wDYtnMqQk3zyQpa7/yR+MxwvK8HkbgvB07a14FhlJ8vsphFnvkUs+FYXnQrdcWYS7Za+U6C0xCaSGG5ySGz9HC4TAx1pmNAcAIyHA2YEDMAyEbDfj0m3vZtYA34iAVpStvK8NdApIkgtsJyqoXMbET85NMCE/AjdQdscISoiNqtRQvYjyhs/oBBxKndPgTiiek0EN7lw5H4MJ028GxEO1ssoyTQTN7v9iB1d3p5cl1vazKGNVOCfAqCqYyxKLtgA+SgS6XcPcF597NBO4MJYvkdS5khQkC4g8oFzZtI3j6mu7UzeJ4h+vL0bPi+Q6LyAK/TZXHAdorfnc+m/l+eC0QfAVpncPIQzIbFCOhbaEC6BTmJW21+HEbgj5trtR3RG3esZ1M0aulsD+wLWPptJfc3gZFg1dLKU60RACLv4NH8/9d4PDnmTiTzfBM6rhhgZcFY+H3F5vj/xeksnc2BqY5CKCDiHejpy9t+Ao+PYiRq3AepVGzw74jCPiXbmcFjNt4KIWgSoFI6zMRyYNzg/b+DYPPZ+PqK88b3DYl4DZ4XzJ5HFEQYJR/ow2Y9KxuyDxCmD/a6tZAa9Y9sAOts7y7dw3YzovIqyItZIB9017kPHY86X6DRMBJrnuFG8RyfHQNASS25PMz4a403X9ON/R8dM4KzoX+xrfM8EY+sUdZqhz6YjLPmZeIuCceEzWGo/PkYo2w7unS1tduEIy/fgNhRHB1lsMyKK+2IrDrH3KxlnipG7LZ4Bj2CBSrroRHHVWFP1GLfFGPTL3/7FcPzrb8SB9PpMxsfrhZw/XQBbTVF2iGsjx3KDrRxAWOnC39Hqlw6uff+l2z/rzo72j9BJHfU46kj4AcMtAxgrE24rwDMSNY8Qt+pOYhv3pTCjxKdKVyRVKpVKpVKpVCqVSnWQ9IekSqVSqVQqlUqlUqkO0kFoqzfe+B5fsrBdbIAxEdXI4fp0Hgne8/JmIdeE22lVw22KS8y0/UOCZCKnZuTEyiSgu2s2IXApIBZjHIs4HjAPYEFM8uuQDHQON683d4Luvv/hu+G4WsG5ruGyMtzXWiY6xvsdUdbZAQslPhgSr+KyOtEaoGNMij2CjJ4jjphMmcmk6e47snn74++OkDkkNZ4gEXYHhIAIa0U8A650Nd0mRzgBsTLel3jP/se1wMfq+lR2ZkKYe7wEsZiA2Pk46/y+Q2OJLo1QDCYXZ+ZmorDPoKh0dusRCgtXS2LAdAVs+WBEhJD8uQTKVRBhfQCOhetEDsjrG7nx8nskYgYu4oEJtdvTJM7O8635xc//L2OMMQ8fBW01jTzrR3y+AHq7OINzINwF4wSIDJ2LgaAHdPKEo6Dl+cBcPeogcE+udEgsjkTJHrG5YyhhnZb4nEmeEVe6kaWj3GuaEdOSsWW5lTIjbs9+TefnY8oboffm54KLEcktkUSbDuQj92e6swbov8/ga+RPRy6B5I7RN0ev3/frUR0948zJ0Byi7Tg4YM/oAo42VcKBuy3R19aCb8+wnaKbIz4XMm76iq7ap5H3fkhUT8SzQwxMUYhpgm0gcMzkLhCisCPXR56ENmrpzIg3dagoTyfi3po5gkVzAMdZYm9tKGVYVHIOt28EGO/wKKbAeMq5XvCMgyjH0PGuGLl+czK7T9liwVtHjIH4HyHG9ZSuyHjW0OwvlxbzGTre8s4tHKu5a4ltwPbbfwr2abikNwHKH9cL6fZv+bac38EtGH3z8kJi1dl7yTKwLuhui/HaY6sX7kTX4aOrL4ICTqM//4tfDMf5o8T9dApEPmKMlXJMiQVzLoTzPbsm5uicr1jirCz3px0DQKq51c+j7kzHGIPzO56P3yOYL4/c0flcmICljKtoV3Upx8s1ts5wK8YnSlckVSqVSqVSqVQqlUp1kPSHpEqlUqlUKpVKpVKpDtJBjI81Yu5TwXEuAAZQ0IkOlM1sIs56GRwCPS2gSKoCLfWj37t0A+TCOpeHgTn2KIAlYsCMzFynjrB8jJXnNuLpvDbxQLn+1ec3w/H1d5IE9r7+g3wXrrQ18LCyE4yn5HMeUYFzZjbduTpOgDjEcGsKyEUBQ3REzbj0j/M9HFnbTtoD2U//HM6Ce/Gcp3Y3cgMduQXLJRJiCzjunDSwTYNE0znwHmAsTFA8QpCgkSPryM70GXzpiPLWmrbHcYg58ZGYMJjYmwWqYYA6jcxUgSsRpwgDVuT+RMVEK1qcH/T9xKOveWLOIxQX2EgEV16gPi5FfyzkXSdA/zIktZ8DAVpVgsnAxM0ESBa8hvtrcXEaBKvMc/PrX/zcGGPM41LQojNggls4Rm6/kaTRxLRCxNUoRKJ39GvLBMoOcZguviPX6xHcKJ93//InxphRUnu/72NjiL+iTXWIwwwN1jD4Sr044EgLjC10oW48Yw/R2dPUo7N2QBuJiLOUiPB2aPctziEGGQNLimjBTI2IdaCEHbeHyDl8/yEkdIzxz8SMUSL7/c6yCcaTMyBmaziZFyW2dWCcv3oh42ZdSt+0aEAt8cvgmfL415T3xjRPeCLaTcT3hFNwiuMR8ou4FyGWRgGckO1+N11icKM+Zp+LlbuzmEQ9DKS/0IV0griaAwW3mENZzEm4tYjjP5qMiYD0chR040kCziFOeLr1jCdJz6lRAAAgAElEQVQcmf2Ow/EIqefYP3JhRb0jxhIP5VyBk5EO4yCd0tmviO8/bcsJiCLDPZXYalfjekSnOSSPXIHhxg1n90Um9z+Hy+m7u8fhuGEM4/Mwnp2oGr2RYeP7D98Pn3//vWwbC0LUHX54xCPHUmwNoEk15grcbmBG297QHzqgojiH2zlMH3uJkjIhAQe5boS84ncKsxagPxLF7fDsZcPxFNgz8W28X1UKzvqXv/l/huMXlxfmUOmKpEqlUqlUKpVKpVKpDpL+kFSpVCqVSqVSqVQq1UE6zLXVi3vlyBlqhLTBHQ/kSeqI9xCPxPI8HJOIBhGzoGurxXK2hRMrE866/hVp6hriGh3x0VGCbDwvVqzpSEYqFrSd8TM4Yn3xQo5LYexuP8DFDu5YDqhPUI/BsWPJOmvStHd5g0NjiOS1dEcd5UYG/hKxDTxT/l1DFMbvPeb5LR3tmOy2/5vHyFOVS/ZMqAqMaOREh89zONqt8grnwzWUidSBqBBVpTEi3VxHRsMj5PW4enrcuiViwWcFMkeX3RF6N7IXxOd0jHwGcQNeYw0xYuB5wT7UB45wdKIDGkaXyraV+qpYR3A2bWM4AZI0Ajq1qQSrCxM68KEPzuD4WgPTXp7G7bNpa/Px/v3uGPimTwSBa+GWmMNp0fn9zqQBE14DO7PcVuDYZ4kzPtM29mBgo1Ph9kknOn5OTIunjFBYxl4wSHZEMqK+MJ7EQH2CmnjY/uc5pqyzJkt3Zb18XA+fZ3TNg5M50dYYbpuzmSTUnkzEQW9swkr8FXgz3Y2Ja4NvaulO3tepDYg5o6/DJZXOlHaEHyMBO2LsGRKD32+kPJYPMg4yhjtcP0GZZYngdg9Av8OTuUR60/ZbHyI6PSKh+wLYbkLHTMNxCM6fOIfjxyjpOFG2UT+R8WnkzIh+8pT4nGN1nCCWbpC4HPEgwnwDu0CMB/bunpkXEZtkTbR0Zx89L87nkGNP0x+996bu42YDN8wRHIzJHbFs4tqsuyyDM/ZoWwEQSpwfGW43oFOoPAPH6Ccn3BFaiz7FMg9CBkS6GTMZPcYExn4EX77H+VziDWNpXXEuZPYft6ec5+zKaJnL+M05d4joeJ7JO0ymEkdCzHlG2yfYIPCPEI131EQ516KDP13r+848QrhRzx7zZbLInnb3fv943mLeYov9mHFLt2m08QjPW20Ebf3Nr341HP+dP/0PzKHSFUmVSqVSqVQqlUqlUh0k/SGpUqlUKpVKpVKpVKqDdDCr9YTjcPk8oOMfjhMst8/hbDZyk4vgYGb24zXEnloDh82crp370Tsb75Z1Hdwlw5RL1vIeTQj8qZTzGzgg1TgOgDB4uLw6YAmTC1lmv/lsMRyv1++G4+VacLYOTo0uIcx0PFkjzoB03A3Bjjliw/h7A1GVduTiB6wQ7lEVsNUCOGkzcpji8j/qafTMu+vQBYsITdXRcY4OiHKNCI5VxE+CgC6zdNCS77aeyIN8HjznyPqMi+ox5b04gbEPxuC4I7gCjpxagU0wSfyoLxPBInpY7keBmWibGI/FcdwjU23H+6Au4PpMvKh6xhG2BnZuGsHnOnTIHJ8nCbDnd7iXlzjUOcGXGifftVNBQY4qb8wTYU+XvxZuwtutID0pUMYKbStLiPHQgZlxFe/PHkYse+RwSpds6KlqRi6/zzi14uOAewycvB+3JphRYnRcBjEjDeVdE2A/jrGEsYrEUHCqDmlM06N0eSFtq1jBpRRDzAz1GAFTS2Jpi7RCdIhTxFkZv4jvO8sYtN+NcOBfPREtYIAkL0foFl0i5aQG2FeGsexqLpjr248fh+PNVjDXDk7ai4Wcv0bbf9zcyTO407iaGyNth9hfiuMsxXyGsYlbCYBKuoi4IcYb3NO33OIi12nQlmq64vP6/TOw7xLnJppHFDygKy1RPsxhLAZRxpsR5sr34PYful1iTKTb5Yl6o/HemLaPByOT+NEzycfc2jPJ4GYPzJp4KNFS9gE7GvsZ/NA2nsFcw34sZJsa1XPwzDHmt4x7nee7EqFFLMF7sF3PZ4Jvb+7EdZkTI85/7IniqvfGdP02ryLf77gdob2OsHOLbQWYc3i8P9u34xyJbX2/Ke/ouw0dWvsvj7MZcEsf4zrmvNy6N9opIrGBrq0OMdA941zbAI3n1rGikN8djx8krm4bTqo+TboiqVKpVCqVSqVSqVSqg6Q/JFUqlUqlUqlUKpVKdZAORFv94LhqR4l0sbwNbCLEkvzF4mw4bsAZVGTTCsHIthtgGcTnPDESJB0PBIUJYiwhb3fXb5EAvVrKcU4n1VtxhKvgrkcMIZ7Is6Sz2XAcYTl9kkrZzKeCCry6uhqOVwvBddYg5kIyGO1pXCK9ESfRkIlsgSURg/BwdqPhYdtK0uiikPraAmHNgUGWcN5sa9wLy+1MxMuEzk+Yo0e90EWQxznsvIhaVWQSgIWEcOMrNuICyoTpxHtrT2SIzw5cQe5kTmjaKoQq7kH3rrHTI/7BpkXHTjIcfG6gI94ILtLmRJTo2goMLBFU1PSfWyDqrhHEIgDOXcF1swR+0zrpU5viQc630gdzK/06d7h+JP2u/JnUY/474K+BtNkUSb2JTx9dgwuqlPMKsanZAkGfSMLgGI6058AHiTLWI+J0P35EBIvbBNiOSf08PS+xaFTpGKUkXw5W1TK+PeMUSrfFkbEr+tp8JtsHIrr+FhKfyCkFoxc5njrfmbzctZflVtoNY13MLRat9KMtHKpToshwWJ1l0r5Dum0TseNYTFwZz0nnx64/piu1h/tix35PPhjtjkm06bbu8OzpRPosk7o/bKScShzfXFwOx+dnUr8f7qXM/OEE1ifJWmPi3sI0ASKWsG35/WNlhLYe0KURKFuJMb7NR2y+XJ8OjLWUS5GjvICmpX3bYB+pCoyVDVE3bA/imIhxwGF+141QVZwON/3QyRjKMZftl3GFburdidw+vfem6LFg9oUIdZGgHc/gDpzFMmal2DLA7SHNyHUax0wygLmTR4ONuIVk5Gbbj4/od53ZPz63o60JcjhyjGdcJfZM/hXbIOhKewHX5Q8PMkmtEQfcM1j9MWWNMVE/9saNjM0x+lpn5fPZBM662AJAtNrju6M+wPgVEmPmFqH9/ZrM69MWlRaOqfztYqTIx3MrOlHXMn45Xof7RjBHYuxJ8SxhJGWz3mCbBcbrHG729VrmwJ8qXZFUqVQqlUqlUqlUKtVB0h+SKpVKpVKpVCqVSqU6SAeuRVsT9L89mfCSzkR0irucng/HE2A5j3eCo21x/hZYYZnLUvp6KdhZhPN/+vkXw/GrF6+H4ywRd9SwxxKqB8FA1mu5/++/ezscv7v9MBzTKfbyQt4jSuGudyZL6GevX+GegvGMEkRj6X8+l/KIE1nCbkssjwenQemctSbq0Z2OuCkROCApHRCdGj5zt6iXD+9RXzmQwUrebVMCMQQekAE/mAIznU6An/YoBh1T6dpKXKgo5XjbwPmTDrJ4Dya2pYgBjlO0E2GQz4MReyeH9lR/r7F+QAJbYEYsW4N6HCW+ZVJy4DdBR/xIrrl5lL6Zl1KnHfDEEE64cyN90ISCZaQ9whfAYSzGMfG2dSXXvkVXeFhJu6sKQTLugWTQ3C3u5LmugL9u4OY6n9DNFK7DncSH9vyNOYWCwJlFj6U+LCU2OSBEK5TLeSZ9NoQb5BrY29sP4ow5ywRtSYAYToEATVK4oBLTAnI5wt2fcEa2F8SSAn29KKVeSAcz9hSI98VGkN4WDnLEhFomfge+lcZExuReFhwakbxjyntxEd4SwUfS6A5YZ4HYlFby3EWFOFnK+Q22gSzX0na59WIKJG82l7qeTGQbBlFF1+OMdMMO0P45DjL5dgEXwRJI5hY4VlfK5+sSSBUJWaBcW2xtaQppM/OFvNMU7u9b4MPHlLPWJP1zZTHw3IBjOfvFM67m6BvFhniqlG9NR0Uew52ViCx3HrQjR/RdWafJ/vIhbkpn6DiQfj+fSltjG9nQNRb9KMR1XMQXl0Ped5SQHeP1CC08orquG+IQ2/EUc7gY2wEYDyPE1RL97mEtsWmDOm1LeWlus5mkMoc5n8PB/0r64xm3RfWPEHtst+mwDepR4uQD6mUF5/+QCCfeu0Zc4Tzq9Y1suUqItmLem72/HY7zBk7LRC7dieY5vjFNvdt2Zo3EhRQO7BXKy4ech8kztWhnxI+ZfSChszDGJ27zSeHoa8ERd+S+e5ScBDG3SnGuzft74NV0WK/Qvgq4uVcYywiIJ+iPdO5NiPSmePZc6rSsDnen1xVJlUqlUqlUKpVKpVIdJP0hqVKpVCqVSqVSqVSqg3QQ2mqtMa5HPizduLB8Syel6USWaQvgcC2Wg+sfZNn1w60kxXzcCOK1yWVpvwMmtHxAEs0vxXH188+/Go7TdIfYlXB7eryXa3dAcTKiGnBYmiFBdAgHrwBJsZstUCaglURUvKFTmeAVNZbEQyv3ss3pkMinRKZEmipiZ3TyhCvf2ztBHH73Vsp/U8LpD+dv4TjXAhVIgWMlfj+KYYCIBE8uq1imb8CkbtEuVnB9rHFODrSgQHtocM+QrmgjrA7YAp1u6VTK6xB/Nc+ws/+assYY16MNNkTbilGGQLOIPlRE00d1jXoEnsesyWsgMu8/SN8M0fevzgRTu1lM8PmuPyYztG1cr3iUxMfv7+T+7+FOVmdIoIznenEhuFCLoLQCOt1tfjccnwvVZBo6wIHNehHKs92tBP84pqI4Nm++3KH59bfS5irggxO4DNN9OGzwniuJZdOFYEkOuHjZyDnr94/D8RkQr/MLcdhO0GocEy77XZkyxzJxxAfE5g/vP+J7cn4CFNowuTytoYkHAt31o5gp52Tom0SNPF1b6T56RHWdN2XvWN0gHtLpESSY8Rh9GWNjjCsWOCWGJ+PgtFg12BKykX5X5FK+lxfXcn1sMzE9AmVR/h5xr4Oj8nIlyNMP797J51v5nEm0FzNpU3ynGO1xizpdo3/dY/vL7EZcis/OBJnvTuTaaow3YT9HSZGMPgWWnACrGyWAB7tdbYCqYpygy24IFLlp5Zwtxq0C20NCxDuirU9OpMVW7slhp6OTKqZ901RwyyliYM566Yjc0kmdLw4EGmOLf6Zbc3vIqWzNrbWDkzrnXgxaCeZzfL77JVy/t9K/Nohxoy/QebOWl767x/GDxNuHRyn3n7wWtPTzm9116Cu9fJAx8YdbmX99y/nXSuprfi3jYBLLfWrMfzwcf+/fy3U+fy1uyVlGNF6u8xH9lKhz4+mSfTx1TWPK+922swX6XQyH99hKXPAIrFs4ZjeYZyzXyCYAbDTmdirMqebAnm9eyPh4OZdxtkXfb+t+ywD6ToPxfLWVsnoPR9w1nZYtY4k8u23Y7+EqzS11E2nXM+DKXS1tCSS7efkSYz5+g3yqdEVSpVKpVCqVSqVSqVQHSX9IqlQqlUqlUqlUKpXqIB2EtnqP5LHMNz1aXkVCXitLue/uxBF1gWTKCfCyCk5/xLroSje/nOAcue9DIcvtr2VV15jZ7hXLR1kmfoALVwleqMI71aUsSRe3gmYRS5meC0JwjSTpwYTJS+U4srJsTtyxQeLsGK5N5mSurc5Mo93zlihzIrbEIB7g2PkWy/AtMNAAuGGM94wDLKsDb85zuaZHHQS4JrHRwO2u3z3jCLcFnrCFWyBRICaRTkI2fSRrxg0iWJsxEbD1xFZFDjhlQ+bvRK501hiT9gh2hGcKO7Q/IOg0VQNxbSoiL8Cb2lrqcY362nqpU5ilmRb9ii5ysSVKtLt+inZOV8u7ezhAroEi04luArQ4AOYKBP0cOO1jKNdZGWA/a8E81k6QknUtCNJPcU0fCBZ7TGVpZv7m3/h3jTHG3N9J/1pt5Ngj7kToLxHw0OsrwZJevP5sOLZoDzmcMVPgLwVwxsc72SYwvxBkaDJBsO5RrraScsvRqFZw4A5Rhkki5V8D+wnR14i8hkCnFnNBHO8fBPFq4SI4zbD1gPQ0xpMoOA1q7r03Rd8H2pEtIFzzIimLKMU2BsSX20c46N3JcQWscDaT715fSR1FkXTIAM6TDuhdjRgX9nHKt4yBUqcbuFQul9Jfam7ZYNJzoLgG4/YMSNrrly+G4wLXKYFs3W3kXmYqdZrNJPYUFYLYEeWsNXEfVzjeJyjbBJgrcV40MxNiHJxOBQ2M4N5usfWArsQZrr8FWrnFNp8I/OOTm63H/pQWjqEcj7zBOxF7Q1zZ4J7c4sGtMHxvoqOWLpR2/+fcPjBK1H5U+WHCQAfyqmF7lbMrzH84P6FD7/W1oIwL1CltUD++lfjJ/lOiHO+XEtsvLuAW2zuChkDXPeYnHmOsw/Uu0S++/EIyCJxziwPmtyvM4z4AI2efOofTc4Z20qL8Irhkc/54TFlvTFDt7jnDHGsWAfNGeTn02Qrj0xaoOedkZ5eCdRq4neZAeH94C9dabB8ob+CmvpA47PpYwS19dC+/v8Xcgwg8fi+UXuo3GW0NkGfP0O4c4i23cU2AvLYptglu5fgK9Ts/U7RVpVKpVCqVSqVSqVQn1uFmOwH+0avBX0YM/irR4q9OK+R+e7GQnI83l2ICUBby3fIefyHFr/qrS/lrJnMOtniVIJLViMVit5G5ruUvMGkqKw7bj/jrAjbpTrGp/+NSVlMnVs5vvfw1IsMKyOVc3i80WL3J5P2m+OvmBGYgMc0l8BeMo8r7wanA4i8XBjksmQMrRa6p1MrxA1Z07pEDC6nqTDzBX8KxCR0LDabAymIZY4Nzhr+o939O8/jrZUuDDvyVN0xhUDFa1JVnafAsNGapGxjM4C+BMVZH65GLAS+P8mNyyhPlrXPGmuTJTIB5P/GezP3XwWQEi7bmfil/mXqo5XgD06JltT9n2wyrRx5//WMOxPWKf+Xsj8/lr7lNIH2nmWBlGX8xW25hsgXDg+8qaYMTkA6fv5TVs8tQVrJmLYyjkG+qgkHRFTwDJujj0wf5y+0xFcaxefnFV8YYY17//vvh89W/+Ivh2NP4KJZ3uJ5LnLqYSTnevRUzFBdJDPoBdMUERiKvX9/IvbzUFymRFCuLaW/SUVmYCSB+JzARcaiXeCr18v133wzHm3uJsTO4AFxN5a/ii7n85XgKcyCae1ws5POzicT8GiZOi6k82zHljTdNn1ORxIlnLj2szC1myLUKI7KHNUxt7qUPeK72LeWaZ0sYzV1LGTkDAyWu+iA0uX6VqMPyUrnd/1d0hExjQynD97fSL5bILRxYycF6DeO9l69kzP/8hayefPvh/XC8xkrOFPWeYBU9wyrEcWXlr/tYuQiwBMi4GozIGYxZWFVsMc4umYsQpm/MMxcw0R/zwGG15fxSYujF2a6M6hyrLki+yzx0CKXmEf2C4+MaK1ZlyVyXiJkVVkwQG2i+6Lg6iTuxDTbtacbHwDlzPk2fHmRQEku7uUS8CIAwZKjfFPGLb7G6l7L7iPlthBXfs4W03VEcILEE0sD242aG3JIZ+toMRMM15qg3VzLn5GpqU4uRTowVvGuYxySh9McRbeXw3mjLAWiuGm2AJoRHVWeMKXZlVN9JmdNoz2COynzttZe4Ood5UIA2UMPo7BZ9k3ntk0iuH5FSQtulOWE0rA7K/y+x2hvh/lkjz9s4uc/jo8RVxg/8pDApYtIE+VEbJNK+mEmc6EDvzFKZF33+06/kXsXhK8u6IqlSqVQqlUqlUqlUqoOkPyRVKpVKpVKpVCqVSnWQDjPb6bxpeiaOKEyMJeYaiM4WS+yLTJZyr2/EFGIykeXYFy+RLwq5oyx+715eSc6dCBtTK6B3Y8OB3XejTJaAL6/l2vNQ0KnVVvCExaXc500h2IBB3skKyOIM+E2Kjc9tAWwSS8YZ8hcRcyVyuD0REtn5bsgFGGI5nvnDglDQihCI6atA3rMGnrH9ABMeLudjIzGvHwLTosENN2y7RMou7JfwPfCJFu0rR77KxgLJQBvpsLPeAQkgNtgRBUUOom6UQ08u74CzuhHnut9k4Jiy1pjkCYHCBngPPKUlqQuU01n2WamX9SNMcs4EiZgDVWVupDk2pzsgPdcL+W7imBtpVy41yjyAiUoEzKN4L89CjOcKf/5awRQixLMU2BAfL4Bk4jrL76UvJyly92Uw9smlb27cicw9XGCy6a5fvX7z+fD5+lEQ/Hd3gqoSb5udw6gBRmfrOymXDAjly2uJvTR4yWCsEJEHJ+YKdDno20ALpD2EccgkkPrPK+R5nMN0BGYwJbCv9gFGXMAsmf9tBix3iro7P0POUuDT98iBmM0ONxP4FGHHwCiHZQeO39FUCHF1AtQJFJVZY0DILtBPgJfBe8EsgPw2ndRvg7gWA41+igmOJg8Y42j4UDP3LEyNDGK5hYFQjGs+bgTRnaPPLohdvZd2Qqy9BMpOU6ZJetD05ZNlrR3yCTOvMJnNMCTmihyRAXFW+eojcghWGEBaxF6a2tCAbjalWZV8fob2/ZS/O0G/47yJeSxrmDl1GJuqlsZHMNEq9xsr8bhCrksa042MYnCvdjSInsZsJ3DWLHrjww5z1AuYomTAVi3G7HiGOQTmJ3e3qEdgvimMjeZziaUXyKWKYdA4i20giLFFb4Z2eSlx+gymjk0h/aJB3/zZv/GFPC5ddRCz74GO393Je8SIyRNg/x4YLZO2jnKZEu1sD0ciP0XeeFP1sZ/bWizbUID81xiHwhrGdGhnBdp3hz4VISZbbIOxMCFcbzFHiuS+AUzcXPzHcz6OCSHz0KKOEuSxPGtlLKuxxcA5bh+EaSf6e4TyCDFH5aaOq3MxYrp59UbeY6FmOyqVSqVSqVQqlUqlOrH0h6RKpVKpVCqVSqVSqQ7Sga6t1oT9Ei9MPU0IRIl5/nLgDl8ir9nLV+LUFpPjgfNkAwwxSWRBNk5lGZ7YSXYhzlOzCRwhezSIqcMmxLiQ83FWCraZYyl5coF8Qa2gCikwy+kcLlhwSeRq/8YKUlIgz14CzDUycq9VQS7xeOq8H/KqOSd1ZL0saTs8eIpl/RhlG14jtx0wiDUwU+b0cbWUSwkkwgL9Iq6UIf9N0qNsITAAIhZVIff0yGsas20C+eD7OfCfMyBjLTAqIhzEIsi5NjgOR2TDaZCPwBoz7/PDBciVSETHo4tHKBfitp+dSX94hTxZCZxMg1jOCRrpGx3QkYBuZsirWj2Ic5zt88a1ubS72QvkW4O74JsbaWtTIEjEAzsr96ErrYmkjiYlXNzWcBeeAnNFvMmBBJZwu6weR76Dx5O1xvZt+eUbQZSIGJpvpK1PMjgKBsB2AynHV1eCQXadXGeBnHwx6tQhloXA4CrkpSOa0/a4W4e+EwVsa8xHCnQLOcsWwFOTV+LGTZfC6QS5E7E9wuC5tnDmm6G/v4Aj47tQ8oAl1any1kmcYLxge60LOPfhMc6xHeLqTJ77Cm6MBbZvxBgTU2yNYK7YKcrRwYHR0lbzCc+j4zSQriSGG3ko/SXEmJVl8nkDPLLm+FVLzGAeNMahGZxdC5TTCs6iNVyE55PTuO9aIxMjGlEy/yPzHXOMyVI+k3w5g9tsCySxZEJfoJLM+2kMcgfSfRROmq5fE6Dz6Oxc2pFlTlHWNZDFzqL84TB5zziEPIbWcXvE/jjBmMzwzLmePdX4GDhz2aPuHdrcOfoOnb5DII4BtodYlNEZ2mg4ytOK/OLIkWyB9obYtpRXsvWiKbA95slBFFs/skyud3EjW65qbE/58E6w1cklEHj0+whbSM5n6L/cZjXhlgTEdiDYzKtJF1/vTzU+Ciqawnn2BVD/xzUc+bd0W6VbtbxPSrwf86IEiPJNKmNSi21mKeo0CqXtcvxzT2g6c5Y23KoFFBrO9wHqdH6D/tsiJyziagfXdGZgYFVwS1nCcYA7wOCm3rWKtqpUKpVKpVKpVCqV6sTSH5IqlUqlUqlUKpVKpTpIB9ueuR6LIZJhgWpMgRMtgHnMkFg6A1I2ASqQpHJOjmVofp4Adxw5ZsJBjC55T4vJCTCfphCsoEMJzOEUO8nlGctOnLLoVhvGRDuRyNXCzRSITr6W66xWgrnGJR3wgMgm4jR4XDnTmt1SeQcUyQPhcAkdseQwBJ8SApG6JB5BlAsOrnQZC+FKZ4EfLJA0lsmnn5LMxkCKmJw39uRmgAh5uqPBuc7Ks8R4RiZQdrgOE4wTW6WbqxlhPDz/NH+vCYwzl3bXXog8xXS+NcQdkDgbdeojJJIPkPwa72nBrAd0v4X7WJIJFrNewcWPxof9c1bo3x7PmM7leq/fCGput0Q45LsjSg/PbsHeNw79Di5rbS3tp0QozIGmV36Kc8Tp7tiyfZnS/ZlJpi2SVmfAmF0tcYR4agRkK/B0lgPKFcNdEd29pdUvmncAF07Xlxe6kfEObQTY1wTl2QFdJlrrrZwDwzkTA/Gzlu7OcO9zxHLl+ALo42c3Es9deRoXZe+9qevdMzJZexay3yFBO2JHgA4cA/m9QjwMHXFlIFNuv4NoGgoO1cIZki7NQ9hE+6JXOJ0/p4jHMdAs4oFsO3TDNl7Kn8++hSPoxVyw+sdHGfuWGDeJCkbuhH8H78vRIS5YjDFu1EaxxWYqZfHUFowZmR+bsNk/hxidj/eMgayH2IrDPuaeUFEgo3RFngOXjoDEPtzKtoMNYqzDGBJh/Go45yI1iPGBuGOH5/FoS4RZ3eHT0E9SHEXmize7bVQ55jm24byRaKKUc4rk7rGVdu/wPg5xJwj2Y88edd1hLuIx/rEc0yd8n3MMzMsCxIPP/uTL4ThfyvU2cDyOF/Kurz4TJ9hZJi6dGM5Nkct320eUDSsM9Xu6TQIiG4QmWuxiQzCRdnw9kTpNUymwNcqLwWzi9s9dQ8tYKucHmAA5tAFu52CxeDr+9xXYEvOmmS62m6RwwG6xHdAUiAc4Hz81Rtv7tsDRG9zXttKWM7Sf2Mn4G4eCTFsRTqwAACAASURBVHfAtD9VuiKpUqlUKpVKpVKpVKqDpD8kVSqVSqVSqVQqlUp1kA5mCmzPdKRAMuZwOroCqjrLZOm0gxMcebQI343gGDUDgkWHuhC4zuP9h+E4vxcUhphQ9uJs9NzGGFOtwKVUSHbcyBJzOoc7VyPLxxb4XMCFbWBKxHXKe3Gcu72V510jQfNkcjMc08ErauBSeERZa4dy9DkQU2AYBs6kboTcSL1MsMbeoinFYBnXSCBbtXDqhHskndBSJGSlK5rtuQC6as2QWHwSy7kVMBvmhe1GuCnwV+BmTQMkAO2oQlVb8A8tXOzoWUb8syZqcURZY0zYv9PcSbkkTBxOPBV9kA56AdCsBsyrg4MunYUboHEZ+mYBJ1Ym0KW7rumfgeXjOyS1R//eoJwjPIuriJHDiY0Oq6Ugn3xvgy7l4Aq7fZAKfggEw6tiQWlaexoXZeO9JCZHI5otJH6mkbi5FlvB0ap7bAEAqhrh2OHvhQFcsp0Hkog2WqIPtJ18l/2x6c+3QIXJBTEhcwB7OCayTz3Qy45YHXuSuHR2BZObI5E7+CVuPcAQZa4WaKePp6lH7/1QLmTT6houyjg2FWIvypnjV5hKjLMAyeKAyKVc0qH/+prJ7oG7E2sfAgTxZ3muHA6BIRySicNFRPzoPIpxvsPnFcaZBv2UiGgDV+0KW1V4Y9udBon0ZudsLv96uh8KmugYzqm3EgM7tktPxI3tGEjzyEEU8x84iXNfBbeiPG0PoAsu3XkDtJ0FEpHzubb5x+F4kko/nc8QGx4wz7J8lv0O5x7tuhmNg3B89acaH62J+hiXYz7DsSdAeXnMc+jwboACx3TKZbzDcYf3r+CY2cBZviqlnUyBsg/9l8wkytnSGR4OnBcvZcw6bwRhTTK50MTRplMOW/Q1j3ZdYx5BV940pks0XPFHcfuIcoHpprs2G2YyV765kuc4R919c/tuOGacIkaejbZ+2L3nEFHmklsN5LSi+79nH9iVBcuETYpjMkuNW4VYth4Tthj3pCN6NNrnI4cN6pHjhltIpgt3KWhrjt9BnypdkVSpVCqVSqVSqVQq1UHSH5IqlUqlUqlUKpVKpTpIB7EhgTVm1i+9T88Ec3l1KUvpC7iz1nCC28KldLMS3JPJlGOiWZM9jmTGmAquUvmtuCg2lVw/mQq6UfVIQ0fHSjgBdlgSz5dyPWIsMRy8PBCJBugQXcuqUj5fPcpzbQvBGV68+Gw4breCdt7lSPgLNPi48kMSYDp5NkD36PRIVIXutE0sS+wZkvBGwGimM3GhfHmFZX46aBG1GiVfpiva7iQmbM2A30xmcv9yKa68dN6K6TFG51H51FS4f4k6pcMiXenotEdElG61HW0Qj6jOeLPpMYdzlEWIOqpKOK8B60zgwNnC8SzKxBnSAIOk012IzModUL22kfadI4l1Gvyx+5sHPeGAPzmgyyAvTROwDcr1SL1VuWAeHVC+DihuECFZNMoguRQ0aNNKGSzv5SGW4ak86uxg6WaBrk0YS+FsbBvBhltgiA52p45uq8CFLdB0A+TYk/Ykyg/GyhIr7FEb1yE2wlV1lIibuZ/Rfz0RM14HfcoBs6noMM2KB/bDOL/dwNWPibkvDk+4/Kl6SspN11wPN8wSTqYeiFIAui/y0i5DHFt2YCCsRAxDRLNmZF5IJ0lsW+grh86vCdrLuoWLI+slIW6H9wA6RbyKyGtVCuJnWY90WyfWDvSvIz5myf8dT9YIlms7vifaLs26EWs4hqLqjAMSGWIbwghbJIaH2MSy6FB35RbYd7NrY1EsCHcKt+SRoyTKOcQWIm7xCIHYBUj2TrfekXOwodgf/d7j1rOuT+OibLw3TV9eAcYvG+KZKowr6AMt5nBji3PE2HD/OkyFeWGDTlij77NdTWcS55+cY4MQY+9ox5HUebkSzJj1mGC+avHlCvM4uls3cCGtcVxgPOUYwu1FCcYlbvM5prxzpuvnwpuFzCcTbAFIgXjGa/l9UXuZhzA0scU5vINDv+NWDk7i2pF7OdBWtJ+nWOEQBALOcyLuOeKz4Hwg7TWdkLHdgNsdmpbO3Ki7SLbwhAmyZ7x6JefAcZ9bXj5VuiKpUqlUKpVKpVKpVKqDpD8kVSqVSqVSqVQqlUp1kA5CW+MoMj9988IYY8z1tSwxf3b9Yjhua1lq/e7d2+GYS62rx9vhOOPSeAIkAAmd+ZQlXNGKtSCMng5TnSz5r3qsbZaJ+6LHtS2dPIE/lUtxRnSVICJMWFrAnZWI3wMQv20naNDaC+Z6Hf9kOP7ho3xugHJNnCDDx5T3xtQ9uhEAh/NATFogNBHKiMlxmajYZ3A2A2ZBzNUhGTmT1MOcarSEH2Jp/4ksYJLrAPjcAmjneiXtIgY3EKCt0RXNIdF2JOSIqbdkw+A02O5HXkE4GQ8EqzsR2+qNMXWProTngptULZFrID1kzVCPYYP6YvJ0YJYJ+wldPeE+l28EuayJ7eH46fpE9nzFBN3EQ9im5Hy6k7WIKyP7M7wfT6FbbRcK4hgFQD6soK33G5bZaVyUjTHmiZojGuiBTgUofzqfOnQk18CB0NEVjhgZMN9Q4hqhJDr3jbBYOnL2fYkxkI6CFngsk7c7YGV+5PJH91f5bscEzTWdpHG6ITYpz7PdSly9nCMheywY8zHlvTdtj7JFHfoRt0bAQZhOiB6xlAA1HVxNsB+zTJiYno7SGIcMY3hD7H53HOHacyStzldA2ksp52gK9JRthPcBgl43EpPtqNrhzoqOSgySbqYJcULgbMeW7WO2HQ0ZbKN27+ftKBE46osoG+LqyFF55AYudVRuZT5TVDJAFVsp0yfHyOlcHrgs5XtNB/fdgNbV3KZBp9z9LrNsR3T7JGTcoQwMxp+WPDAR5RO5mhvjjeufMcI8h0R0Q6yQMZbIsWF9oa9hfOro3ok+TifPGvOfLJXxJomxLajHkYORKzOuDepwjYwAnZH6PV+IA6cjYg+c1uNdR/htgaT2xEVj4rKYFwDZrqrTzHM6Y8zTMDw5k98abE4JttJkE4lfLscz1Vw3Y/3Sghrj1sgVGvMo4Ot0VrU4J+rt4TEMmNrC0Rn9y/vRxGXPExoTdTLm1/xuJb8v6ATMMbHB9RvM62+uxQHXMv4Hh2/90BVJlUqlUqlUKpVKpVIdJP0hqVKpVCqVSqVSqVSqg3QQ2jqbTs1/8nf/I2OMMdML4otwgATaWiEB6w/vBXNd3smS/ATuVCaTJdgylO+2wHiwOm8eHwXdeH8r19/AcSu62CFNlzGS8CIh8hwuRm++fC3nAONZPgJzTek6KGVQVHBzRaLpEmhcUiChdynXKe7ku/NMsCs7Ow2603Wdyftl8BnQFmKaIbANosCeS//AEEMs8Y+W+/Gniq6ig5kc002VGBPxUNMnLR4hz0BoSIAxsTWX+2NiPEzqCtSLSWjplteMXFuJtsp9+Y8RDnQieWNM5XbPUsGmk1ihwbOy/G0Nhz4gIiHQwBB4hAMSWa3uh+Ptg+Csd0tBCXPUzfwcTms9ImNbIO3oL474HIpwg75eAoGLYrRfIL2o3pE7a9MIzhk2ghRVbKghHM8MnBFjiVvHlR94v5GrKeCWEAhR0D2HwiBhPDDUEdoaAkVCMngiqi36HZ3onJFY7Xu8Jmrleg1RSsineBYge94BO0JS8s48g+sS6QF2Va3g5A3cb72Vtnl9IdsE4onU+zHlDXBV4miIh2EMdBnl3FRosDUdy4EZs01j5C5W8p6bDRFSOGAjHlkEyyeELnLApdHvE7SpGnGyXgN7w1zAjcBc9mXgrwGxUHmp9VbqcYu2lMVwuJzApTDHPoQjyns/1AcpTbJmHuU5wtRQthb1O3J/xfzDAPWu6MgJxJBbeN6+/yDnF1K/We9avsV2jC3qf4PtHpdIPj6dCfL97vZhOH7APUcIK/ovi2Y02uEfLCcLzLUbn2ROIztYdfqc7p1s/zib213C/VssDOai3CnCzAIe2Qro6Ms4sMV80S2l/wbJ7jlBIJoC7aJtgO6j/S/vJeOAv5EHm13INo0Yzvp0jB6hrXQ+xzN6bM9hfJhNgcGfyrXVG/M0tE/mMl+vkKmhXWKrDoJjTowfHbjl/MxhXoQtN3nJbTOIfZhn0Pk8ADKe9tsDPGlfuoszHgTcyiH1UraMbxjnuZ1qRJ1zaxowdWC/i5fXw/H0TH5rNBtsZTujhfunSVckVSqVSqVSqVQqlUp1kPSHpEqlUqlUKpVKpVKpDtJBaGsYhubq1W5p1CEZPe0qUyABnYdT61LQtO/Ai6SpLI23XFEFBhAgMXqA5fkHXPObbwVt3QLvKePdb+XPrl7KtWGl9GYumMfNjeBPEVyzKizxb+GU5SZMEC3PWALVM/fA00q8aykYT5zSEUq+Gm5O4xLpjTd1j71UaAFRSCcrVAZQSaI7AR3MyLmABGHC37wUhKMA9jybCl7DpMglUKcnEoPuuCXaXQE8o6QLHG06Ky79yzlFKe8ax1JHTCxcAwttn0myPPqrDDCDzp8Gc+06bzb57rlWK3mHDK6eTFJbFnD1IqKM+grhikynP1dIPTZbOa46YpBy3wRtel0Qm9z9dxajzeP/ezgbtxXwIqLIeBbiSB2QIuY5p1tdDUTEw+m3RJL0sCQmLJ83a8HKji3X4yruGSzMA6miKy+T1Ncj/AW4NuJtt5U6rdDuS+BlHrEvZkJlIIYDwhkCeWqJrgEFIqcFf9h2C3Qo4LPDaRmILvvdFvFjA3y9QLyxcI/+/qPg2JfuNGjr7p59Insm/CY65pk0Gs6cSJxdeomTMZyCLbeQoA+sgJrXqNMIfdyGRKbZrnbHrC8QiKaGQ2EJnDiCW3BYYGtCTJtC1DXetYF7YomxZYnYzu0sKVC1BCgZx6Jjyntvyv7Za6KcMd4B48rI4ROsWcDOzD6IbQIGKH+B8a7B2BYmMiZlE9km0BqZ5zw1hxwujgUcjzePPBfbbTYyn8mBUG42wMURe6vRMdy2A25JoTPz/sTrLerakYY+ppwzXbLbysCxnG6kCearztKZlLEM7sPEGjlZQ72z7hiPCuChd/cyd+1uBUv9to9TMySIp4u2B/I9m0scS4DML4HKEn2cncM9Hf2oQb0XmKOViCV0nk84d8B8LYpPU5HWehOGu2ehs/H5lWCaObZ7FBhv6FJaY0z02M8T1hJj21reP0LD9JyXkndHW8euEdP2z0nYt3WMe9h2gN8XLMMWn3N+EgCF5dhSsm9iLmQn0pbcQn7j1HCCnWAcN+j7nypdkVSpVCqVSqVSqVQq1UHSH5IqlUqlUqlUKpVKpTpIB6Gtnfcm712dZkCq6GS1QhLcTQ5UAxTAA7Ckd3eC5VQZsSR5tMWZ4KdXN7KcP0WS6QyY6cNGbpb32NwjklNfTMXJKl2Im2sNJ0tSKWGCxN3EZuEKaPDsJZC8Enha4mWJecPkrYm8x7qEi+3sNG5m1joTx7v7OHqvIYF0B7ysBIpjgRzXHZz7GrQHvHMDVCJvpG0EkdRjMJXyHTtryX3XfVJmurDmFZztiFHRkAtIAhMi18C7SiAPayR/LolNjpIyE1sFkueABxJ/sKf5e03TdebjeteXLpaCQVxOpD1VwOQsGrUltgoUx0o3MRbIhwOK1IAnDWbSpqfncOtF/y0fBN15cjeOYMnaoU5HbmZgpKcTcRgrG3EXXC/l2gHQ7Ab11XZy/XUlKNemFTfmaCIx4X71cThebhGfgN0eU9bYARO3xKA9MRc4ogJbKYHi2FE+aOkPAZJxN8CIOyZMBz5X060O/SSviQLvzqmB44XoR0x6HgIPpHNhg3cigh2AEbLoRy3wciZMb9FmYiSjPsd1vv5esOTv8z+Yk8h74/t+EsJdlHzTtpH6qhwwYyBYnns8+Hkp71kAU29xnIBTJ6ZPLJX463L73hgzbncdMKptLuNdBMS0K6V+Cy/n+AbbUBAPi5p1Le3hARjV7aMcp5hfJMDgDWJ+moElO6K8FwyuY/tD12SbIz7o6JKNdo+PjY2AqaOsW+CDWSbulFEsZfoKjsMVtiFsi13ZtRjPW8TVzVSusUL9v/1wK+8BzJbJ5bc4f4TqjXZsYJzluOn3H+/Dq48u54yb7cpxGku5tTncxTEvDCfYqmM4F0TlEV0GHtp5YurEWaXElhuOQ9KOLRDKp765LWScSuFkP8G2FYPE8Rb93qZw0mb8xlysAR5ZYp6+oXPydr8LNx1tywIxzJymHp2xZmp392wwn1kjNi6B/JaY81Ue2OgoxmE+A6yTjq9dCLdtbK9zTmITf+Pk6O+P/RaS7VraV0NHfNR5gLl2iDGf7tpbxIbVRuaoFnOxkkgv5pxJLM97dvFiOJ7MZN6Nqb8xZn+9/6ukK5IqlUqlUqlUKpVKpTpI+kNSpVKpVCqVSqVSqVQH6SC0tS4r893vfm+MMebm5mb4vPSCZ/z6X/xmOP7L78RJlVhhYGR5Or2DC+gFHDCBxqVwp7p9L9hZCUfFOBUU5PwCyUl71MjFstx/i2S74R9+L/cErnJ1ITgtXcUyYKj1Vu5f58BYQIYlFggQkCWfy1J55ZkkHQ5LpyHpjDXGhD2ObC1eDn9WIKrBpL2jesRyeMtktHA2I0qXwC0xBpYcZYJoNMTjWuCyqx5tINq6FqyAmDETDkdAPmo6E4KzaYEH0GSOroBhBEyM6Fm7Hwcm5UHHs2Oq7bxZ9wjFCphgh75jU+A6wAoN3CPpMueAR9iGfRb9FKiEgYPbKNl6JGWRttK+22L33YBueQmTlcMlFMeO6GOGcwrpRzYBIpLLe7TA0+5X0mfvJui/CT5HG/sIR0wDtPOossbYvjzo9kmnR5LVZSfP+uH9++H45aU4sjkkI2/QHx22IcSorw59oGilL21XwITgBlj3X03h5BoBwQpgm+tCYm9oX3ipugB2Hso5aL6mBbubwnG3hc2ooxv2DO30ToLpqjwNEmmMN23ffwK4kU7mgmVXG7ofSjwkLsYE8ERSXUc8Veo0xneTFNsEELd5r5ihukf7qkrGRLbBFHhwhi0eHsiYB47VgHds8B41zi/wTg9Avx4LaV8zoJgB2gmNWuPwoOnLJ6vznan6eMqtCxwbOqBjTG7O8WvkYswxkQ6icF1MMqKKcL/HuBVHiO3ob227Ky8mae/ovI7xrt4Aq0QjceDbGk+kdz+y6DjecS6AezHxfTvaqYNzzGnGR+Oc8cmuXNI5ksW3so3h4ztB3kNst2DS95FrKuMR3ZiB3Xt819ANG++fYCvBdCL9Kp3u5n+BkXpZnMlcaZ5KPx5jqHLM+cbVpbj80kG3qBDjgfduchlb6H7PrUOrDdx9ubXBnSqudsb0vzEyfFqhnTm67IbyeTQnjo/6wnYXh3eL4NzLaqTjqgHSbIHCEtHO8z5+jFBozmeA2eKcAnGScYVbvhhj24Yu4Gi/mFORWp1Mpd1NcE4QwNG2Pjyu6oqkSqVSqVQqlUqlUqkOkv6QVKlUKpVKpVKpVCrVQToMbe28+dBjEfeVON99+Ch41dt3gp4WWO4P4byWhHT4ZOJmOp/Kkm31wzt5Biv3SgLBX+bAh64uBbt9Ijc6oHxLOMUxMe7DRtwgOyAfKZKEWqJhTpaJ6QwZAhOrAymDDq6whZFn9wWcUJkseHYi5MPYAQUIHBMJE2UUOSBldFsrDfBIPmoLLAcOWkEk93JoA0zo7BpgqaiDJ+epAo5ZAdy24oSYgzxK44kB7E8OTNc9IpdpCJzVyru2azgm4vqOCBAx4W7E9BxN1tih/mjqOSKRWmJhSLCLrt8Q4aC9IHDDEEjENBPAxHZSpxGTdwMj6bZynTTu0W1EHucESycq7D1xcaC7QPMSuBu28BS0ERwNgYusWkGDPryXe8XATu4fkeAdSYHr4DSudMbYgZ/3aJce6A4pvgis+cNaHNwurwRjSoD6EF8DiWOWW4l3CRwAL6aCUjE+E+NxPULpwLcHDnUH18cKaBjR2g7bDdoEDZhtEDj2KH4QgUcMTzsklwZaGDDeOAJSx5O1zqQ9SjdBEugIlVcjEbYDIuXgClh1fDe6KwN/JUWPDh+gDyZ8f6DmAbYzzM53WzjKGs7kuGe1kva1tagjxOkKHTJFrEuJpyJOeCBbS7gO0wXUAwXtgJvFmdTjCCE8orw3puzj1wgjwzHfoaXrNcY7+wyySXfWkNsEEJ8b9v1Czr/NxaV6/UCcsa+PSrDDhxWwYWDpD1vg5dgGUo+crvFYjCV8D/6DSxIjk1OMs8T5DOcap0pkbwe3TeKDLpH+ODsX1/44wBhPR2ni5SiBCv1kC/fdCn2wRCxL4Sj98oXE6jmQ5vlsF3sTYIch8PIW89gYbe0d6p14arSR7y4CbJtC3XGrDl1mc7zfCu/HeX0CB+ITmdMb642JnsYQbI2gu7QxcjzHVjgbcpsAMi4UcJqm07EDKgrUO0G8i8E3hxjPiHpnT9tGUsZVKbcttsVxDtnUHDcRhzGedojf3ALoMf9xiCsbtN/bWxnzK/zGuoDTfzdVtFWlUqlUKpVKpVKpVCeW/pBUqVQqlUqlUqlUKtVBOmgNszPebHo3wB++EUfWdw+Cs8Is0ITAIOdIqvvFa3FErbZYdgWmlWMp93Ep98qBeVwuxH1rPhVEIYy49Lt7hhDYRgFug4mYCzz8HZIsp0CQbq7k2aep4LStI5cD1AfL4MsayCeOGyReD4lmbU+D7hhrhj8hMEkwnSH9CFuBUxwdzOBO21i4ljFRL16hJkLhBctZwgWsQYLbDkms8z6Z75bYCFBVoisV6oLvV+N8vmsCxM7CgdAH8nlXEoemQ5tcx4MXYSJrJuA+ppxzZtK7VzIROd2EU9RLiIf1cP+LgKPlwCA8ULrtSurl9lGSWJco4FlE/APtBARKG+z+4SbEFAXBssCf4kSuNwPGE6dwikX/ioCROyQ0vwOivKmkHms4KttGEJT8FlhdQ076RP1xdydjzBjn5TE7ZDIXnDdHW7zfSMx6fQUHVzJotZTvdoOEzkCjjJVYTddOGte1PertEQQq9FcPTK/AONAUQO8eBbOJEbOvzyWuO7QB6+HQCzzQRUCasX3AtdI2KqDWZ2eCeB1TzlmT9pja5cX18DlR0UckQ7+9lX50taCjtdTvDI6NHvX4ww/iNrlaSr0HcFY9BzIXIzZFHYb9vg80ETA9JCJ/KASlfOxkfO62QPaAWv3kzWt59gzjPLFkoJVbuHpbIGYx2l2EZ6cxZHOiBOjG+8GttSyAOCIZfFuhMyDu0bmbruYg4Eaoosff8ldLSUJfokxrjG05sEJe1Pbx3MbSjkqMawXmGPfbH+R7iP3ctsL4aVrps3T+JFJtGapG2xM4JmL8wbMTGT6qvIzzAV12ESOiRMorwHhvMedpwOoSZzU1MVA4nMIxfI35TIQ+MAFGW2PrQWH78QmOsI/oIzVcOgs4rFeIq2ts3Yrgqj3F9hQauGNIGNXFFjGT/ZQOoqy6NDiNi7I1ssPCce0LMSir5EG+PJetbQ+h9NkH/NboNsBZR/uy0JfR1zb43cEtdRH6aYC5yIDUYqJZob64jY+O1hxD227/PJNblFq0a4+tDLikcSnaNbZQrB7lXvOp/JZJg8O3fuiKpEqlUqlUKpVKpVKpDpL+kFSpVCqVSqVSqVQq1UE6zLW1ac3bDzv84t2DIC9FzmV6Of8GiVYvzgXXOV8IWvTdRlAfXtMzwSgwpnQOhC+SzzdGruOAhz4lk20quDfRvRMYYAiscRHK835+I4gOHZ4MEtgyiXRXYOmfeCqWpLcxEMIOjn1MTJ2dxu3TeG9MX6ZMfFxj/TwJgP3B+SrwUv6NQVJX4qxMCIsypZvY5lbwiwmcN1m8rJtNvnOdK4B2tEBOOjpGAqFhUl0D5DUBXlTjfCItBXFZYLm0rmsNMVpiiWgP3WnQHeesydIdrjKb05GNlrFwGIPraABO0SMZbWIEf4mAhTRAlN4uBXFzsdz34uWL4TgFTtHAxTd4cgRFe4kT4Dcx2kIpdV2+F5TPFdLXF3SGhHPvx1ugf2iDOdA/S0yMCXkjeaeqFGywaaTNHlu2L1+L9tTCMTVkeaHMo6kcP9xLvVydCdqa0ekYMWV2IwjpeillvaET3UTqI2B76JtVGAl6GVo40eHZzQbJr4Fhxuhr5zO5Dx24A8SnEu2BjGOXwNkUOGtJNAtbJX5yIfH8uLIm6B11GY8yJBEP4Tr64UHcgS8epBxvrrBNI5PvpgndCOU6D+iPnZfPF+fiDJnNpV83NF/tq6lrgTnhOLWCic3Wd8Px7beCRyboRgvUYwSUbvVRrnl7L+N8BySQzrJ0pMxmQHRTju1oD8eU3Tl+GmNMA/fYEmha1BH5RltHP+X4FEZ0G8cWF+KeeOf1CsgvEHxHFBNjWNqXe4gtC3O4x0/nUuYNxtDHlcS0BljddIrxkU6SdNMdJYTnHhmij8AG8X4e2z3sCdcznP/jOVTNeQt5/QTbWogsNlKm20pi2XQmZXQ2lTpqPkh/nMGxOYYTa3Yu9fjknGyMMWk/Z24LKec4lbiboDwTzGOnmIPPp1KndH8dYcYYk00N5956f3vntp3Wcm7Duj6Vi3Jr6nxXptulxEyXy/1ev5Ay3G6kLO7eyfYJB94z4byX81VsBwhiuG03nFMhA0WM+QTQ1qDHyrnFKcQ2oxJbScqVHHeYrzrH/oW6wzyu3sr5jQMuixj+4uonw/Gbz78cjosttmthbHXTw/ujrkiqVCqVSqVSqVQqleog6Q9JlUqlUqlUKpVKpVIdpIPQ1qZpzMe73dJyW2J5G6yfA77AhMRnQHRiuFdVcIks4HCVIaHzZ29eDsevLgWfiyeybF+UQGGmstz8RFAQpXgsZdm3XMOtEMv0V0DGzuDWlgIzCZGgOwR+mwMFWcMdqmrlvbOGaCESnxIFbU+FCnSmqnd4YG2kzOn4Z+CiaIC5jpbq4VbnuQyPJMek7SSLNAAAIABJREFULCcG12Gi50JQkBDI2hRoga923w2xxL/dAisFckP3Xbr1MvlyAHzP45r/H3tv0ivZluV5rX36Y91t3f25vzaIAEQlIgWZgpoyYoYY14wZn4CPAGICjKvqq4CEaoBAkSoosovI6N57/t7z5vbWnX4zsHPP+h3CvOQWXFNKrvVPKWM/c7vHztnN2tts//Z/hYG29TUwvArMNhMoE6OtgSWEGBNEXp9S3qsrbQ785exSkbbqFtgfcDsHDDQGYhjN4Q6awiUz1GfOT7Udb28U9XEbdR0MQ72HBRDKsHcHC5DsN0Bi4cghaTCwq7jDezAs5rGidHQR7hBXqk5Ryfu774by2QtFCFtgTclcMcPtVh2pXQPLyCeUcyJhHzfpzIkqF4d4EcCJ7+uffzOUf/c3fzOU7260LSKgkh6xKUYsy4BxO4yfcKF9Y3Gi16l6HKhzWs8NYmkIfC6GC+g5kOf8TOeEFEikq4D3437pzOwwvtjuFeLK+xLIJxC2y8/UWfQp5YBE3t8qfp1NcN+O6LzW8w8/aT/7xddfDWVP9A5uxXmg9QV6TpY/6TMX334/lEMcz8jPFXsO0117BMBK3ZXGjHaFpPfvEA+JGSNOJxNt0y3cWd+jP75+/W4oN4irGVwz0xQOk0DGEsxFxRaM7hPKe5G6n89L4O8lsL+cyCTuj1M250eJiL9yvsF6CUnEn59oPRZLjbEN6j0nnjfUL+ajO+0L/kH748ka/ZEmpHDdTBAblnD0dXCu7mgliTmxw/mUAP3dO2J+mEOP5b4rMhxDoXO142ejLWr0xRZrsgJruxJHXHKs57imPUMsuzxR9+Z8hqMBaPfpQuOqPGLPOVypgYxGWGcVlfb/DmPBi67jOlqyYs2zQYaEFi6zdLTtuv04J9eAgjVV0xxnnbNdL+Vv/s//RUREbuEY/3McpZnXX+h9hEC0YRnfYDFaJ5hcMZeEaMcM6/65w9EAxLUW44GXfMTdAwTnAkctYoYDLCu4/q87rM2xGKi3ep0K47cq6bqs9/XzL//9ofzimfa11Y96PMEji0L9J7Sj7UiaTCaTyWQymUwmk+kg2RdJk8lkMplMJpPJZDIdpMPQ1raR69sdlhIDQaSTWoAkuAIkYAN31iXQrAZbw0FI3AE3CSwkz/WWA7xOV6kEbqpp75LngSzmnWKr5RTuaNixh7mepCmRSNw7XKCYPJQE3BbOsqtEt9wdMJI20euvSkWcNhmw2yeVk67HcWrsjWd0qcIWu/NsFyYIR6JxIGUe7lRC9zO07wTYc7UERgJnV8eEy71TlYP7YsgGQzkBTiMJ7gs/mzCxcoh7v4eT1f1G+2zjiLYCi4UzH5M7B2jTBAnen1Kd97Kqd/XVeu1bMyA0RaNIRg0UhtnlmdxdgOo2RFiAv8ZgMRZwzJSVIljrO9Tj92+Hcnq2G3tzII4ZOQ+MKbrlObiQsR+R/2Ti6BoDuHmhmNj6R62bEk7Ol3h//JnWwTxWrP7+zWs5hrx4aXvGrAGn2QH57YDjS6L3fXqi2O480rYuC2DBoaLFHMstk8EjfnV0ghN16vTA3STb9Z8YY71ZPqCsn884EWVIBp4pghUjPpfCpNtwygWeFtAMEtjvcq395wbOl9OF1kEjiE9PKOcCSZJdG6wftO3u18SG9mNq3VLv6cc3in5OgIqmOG4RAoOkg20O/HV7C/fCv/6Hoezxt8mry/5zcBxkrY7Hm3dvhjJdcCXSa0eZ1u0W/bcBmvXDrT7Tjzc6x9Ucs0DvN4hVywLHHeBwPuqPTyonQX9tHlMJ4T6cwyV7EmgbEZOjY2gLbJIu9MQsk0ivM11oe+SYb+7heCsrHW9BX49b9LvtveKLD1scGaALKeJqzETrcPtMSm1HxszRsQ7PMYU6wLrMYblJh22u+55STkTCHgt1dJHmnIG5f5TrHrFje4vxS5dhYJMzOPHmcP5MEvYBfb9HXXSI1WkfE3lsKoZDc1sjqT2OzHAt2rScz7UYwh29wwq7KJl1QZ+vxfw/+iysGRtUaxQdaTx2XoJy9/mfneu4+/KlIvoLuFLXcF2XXMfsw281rqV4vwe22wgxdbqmYp2DmOXRHxoiyP0xDB4bSvDvXFulqLfG84gJxw7W0Zg3CKEy48As16MMyUTXCE3DYytaB8w+8B7u3B8r25E0mUwmk8lkMplMJtNBsi+SJpPJZDKZTCaTyWQ6SM77j3fM+su//Ev/y1/+8oi3Y/qQnHN/5b3/y6e4lrXjP56sHT8NWTt+GrJ2/DRk7fhpyNrx05C146ehj21H25E0mUwmk8lkMplMJtNBOmhH0jn3XkS+Pd7tmP4t+tp7/+wpLmTt+I8qa8dPQ9aOn4asHT8NWTt+GrJ2/DRk7fhp6KPa8aAvkiaTyWQymUwmk8lkMhnaajKZTCaTyWQymUymg2RfJE0mk8lkMplMJpPJdJDsi6TJZDKZTCaTyWQymQ6SfZE0mUwmk8lkMplMJtNBsi+SJpPJZDKZTCaTyWQ6SPZF0mQymUwmk8lkMplMB8m+SJpMJpPJZDKZTCaT6SDZF0mTyWQymUwmk8lkMh0k+yJpMplMJpPJZDKZTKaDZF8kTSaTyWQymUwmk8l0kOyLpMlkMplMJpPJZDKZDpJ9kTSZTCaTyWQymUwm00GyL5Imk8lkMplMJpPJZDpI9kXSZDKZTCaTyWQymUwHyb5Imkwmk8lkMplMJpPpINkXSZPJZDKZTCaTyWQyHST7ImkymUwmk8lkMplMpoMUHfLmy8tL/8033xzpVkz/Nv3VX/3Vlff+2VNcy9rxH0/Wjp+GrB0/DVk7fhqydvw0ZO34acja8dPQx7bjQV8kv/nmG/nlL3/5p9+V6U+Wc+7bp7qWteM/nqwdPw1ZO34asnb8NGTt+GnI2vHTkLXjp6GPbceDvki++emN/Pf/3f+w+wDphtc329VQ/t1v/2Eor5fLoXxzczuUy7IcykVZD2XvnZYDLSdJMpS71uOO9B58hPfH4VCeTCYiIhJ6pXg3G/38tm31Gp2+xzn9nLpqhnJT699GgX5OKPp+j79tRa8fZfocJ4vFUD5fzPR+E31/Fh+HPP79b34j/+y//K9ERCTstA5PMm2L0zwdyqumGsrbRt+fTfX5f/6zL4eyQxu9//H9UL48nw/lLz57PpTzRTaUA6ftiOoV3+zawIfF8FqHeylqbaPX3+pnfv+T9rvbe227DlR3Vep1Wraj4F70VqRlH/RaDrzecOB0aJVbvben1PurK/nn//JfiIjI//av/tXw+u9/+/uh7HDnXavt26FfBq0+f4NbjT8w7qJEny0KtVyV2jZ1p9efpvqely+e9/eln/lwr3Gi6vRzOtQnhpQUhbZjVW30HwJ91mwyHcphqn2Zbdris7IkHsqfvfx8KP/Zn/8nQ/nF5ZP8wPpH+oe//zv5L/7pfyYiIk2t9RIEaAzca4sxGyFOzjJ9/gXG72Kq4ysO9DnbTq9foI8Wldavw+cmsZbDaNc2i7nGrjDQdm7QkZoKcZrxttV7r0X7Zhhpv5tPJ/ocKeJEpM+6xr3/cHMzlG/utG8UGKdb9L2n1Otvfy//7X/zz0RExGEuC0O916DT1zkGpEO71PqeOD3V9+QaPwu0Y4frd+jfPkRZdCxxfnocM4y1jHYBniPAHNoglojHczgtO9S5QwB1jvel/aHD9UczH67jUZbuOO149fat/Iv/8X8WEZG21f7XIY6xj758eTKUzxO0RfXjUC7vdE66Q7x7uNW1U1mu9fqpXv/s/GwopxgDszMde3Gwe72stX42a22jP/ygc8J2da/3fqnXniJmpvN8KLcl1z/aXiG6QIz1l+g0IF2I+RHNFYR6nYpLuifU2z/8Sv6n//o/393SUuu5jbQdbwvt+Cv09Q36VofXERqlxnsazHcxnrnDe6oSz4x2Yix7nHM57rZcf3rET4yXMMAYwZwQR1gLYx6sK228baXru45r6tE6R4tcI6XR/jn6KbVcF/K//u9/LyIiAWJd22LdgO8Uf/vrfzOUf/N//+1QfvdWvxM5rUZJMq0XrpeiUOtikmgcqFutL85zIS46zXdjM8FaqUI9M+45BF9MieJZt3jdjb536D9c3+t89/6tDsIg0HH9F3/xZ0P5Z7/4ZijPz3Q9XmKO/ljZGUmTyWQymUwmk8lkMh0k+yJpMplMJpPJZDKZTKaDdBDa6sVL53db4h5buj++fj2U3799O5STkNveRJr0byNgAE1L9kGLHV4nMth0+18nchr3eGgmiopExAYKvZfWa5kon0+A9WErOdZLSgSsqwV+UwEFaTrFCVaFbkPj1mUS6xZ6OlO85EnlApFwd/PObYeXY/BHyUTLeaX3sQVCsF7qVv2bK0V3XkwVlyEOFxS63X7//oeh3NaK+U6Av0aV3oN3u7ZpgUKP6vNO72W51Geqia16onyi7xnhlMSx0Ak/gHmQ6hLgJfxcImFPqc57WVe7/nX9oAgvsQmiIB590bFdMB5AVEkM5GaEHmIsdzVeR8VkuaJ3L84Uz/v683MREYlavfb9VPtXVem1VyXakRhzouXlSq+zBYLVAdsUPF8UAtdFe7V4vrc3Gs8u3mhfPrsAZviE6ryXqtl9fhgRK9T3eK1OSTsNGAFfj/U/0gkwPKBOLgUWWyKWKTEnMeoozzSWToGEzc93uMzzi0u9F8SxaktsE7gjYjnnkNsHRc9WQICIzp5faJxgO1bo7+GPWn9Fo2jhakUc6ThIZBg4mU92mFQI5FvQFUmQ1jX7qP4L56Su1HkCxKuEodaLBMRotQ+AwpPRhIrxG/Q8Woc5LsS9EKkO0MCObBiq03NOJlYP7m2EwBHP8+S38Ln4KI8YQ9zrKdV1Xjab3VzV4OGcU0RsBnScR3KI/HrEstu19u+b24ehvNwqzurRHxYIxI4DC+ul7Ubn03ayuweHdU460Xs8xzi9ApoXANF1iB8enY3NEqBfo6uJA7ItE+LHiLGYLEtgnu2REGXfeWn6Otpgzbna6Gc/tPoQK8xJFbDGBhgq66Ib9XusadHvs0zrK0fdeawtCqCrj0dOSBc2HrEL9dwJ1y3AI4mn4rhWMFp3cw7Zf/TA4fo8zuOBr5cVkd7jrHPaTuShn09cq+NFah1T7x70e8frXyvGfX31Tt+PIxYNJtcQx0naSMeva4g962eN5g/0kxyTdNkfBytwLKzuNCLnIXBaxlK0Y93gKBbWNozDARYAISbxL77So2Ztrt8pfnzQ+vv1/6HHodpO//bzL38hh8p2JE0mk8lkMplMJpPJdJDsi6TJZDKZTCaTyWQymQ7SYWir76Tqkcy7teIZ373+figv73UL+PmpokgZ3IuCBlvAwGwS2HrVeN0RU8O2fQLOoIP1lwN+Gpa7z0rhuNZFxGzwOXTgxBZ/hM88AdIymSsW4hveO5zTcF9LYAA1tqpL2JxlL86H8uXZcVwinROJe2fbx/oREXEREAtgpS1JJ6ASywIuVD9qf7j4ueKpZzNFsOZAYbqtbtsv4bSYwKkqzhR5jB75XwcHM6B5NRzqyo2+h6gGUcEELnPEcirgdh1QHLpXitvvVFbDcqvB30ZHQlurqpbvX+8Q4e2dOph9yOHT4T74PBEqJgLenJGoAh7BNmpgY5cAfZzNFQO7PNXyi8muTZMYTq4X6nrYAfO5W6q7YAskcgXX1jfv9H5/vNb3F0CwSyBjSU40jjEBiOxGccLf/erXQ3k60bH5pPLqoOo6rdvOjfgmLQNvCwFLBnR5Rp/2eD+vg0ceHUOYLTSuvbjQZ57lir9MT3ZjfH4CxBJyiBMdOS1gdWzry5XGjCv05ZrxHmN2BldJHnGIv9T7rYDBV68VvZeN3sNTKo4j+ezZRX9TdHoE3oRnppN4hT4aYn5qgEMRkffA7Yg0tURIgTkSR4OZq7qW7ydVJQKCR2wziBBX2L+EF8c9jjDU/a/7bv8czsAdMPbyjMETqhORTc/N5kA/ySXHaIsI7G35oH13c6fHDX744c1QXq+1rTdbjTXPRu7tus4QIIktkPHWYSz1/SeIidzqDedwpX5xoQ6NERuAy6wPzJtB7Pa+7niEgqaPIe4dxz34Uf5IjHLXdfLQzxV3WG9tOm3Th0o/u0DHb3FPdPavMa/EQMpPTnR+fHaq7Xh5rmvgGRx3icVe3dwN5Z/e7/oMiFvxcIRd81gJYgyPU+HtUuDIhsccOsW9kBHvRg7JeAvdoGktSpf79jjjUaSTzu/qfYLnuV7r8ajf/PW/HspvfvqD/imdcrFGZ5sGqY4jYv21R3YHuNwiBMl0ou0eYEA0xe79Xbi/TogrZzimITh+FeMeeexvlEWC+DGyQiT4nhJnyBDxQo/nXL/R11//pN/h/p//S11vP1a2I2kymUwmk8lkMplMpoNkXyRNJpPJZDKZTCaTyXSQDkJbm6aRm+trERF5e381vH73oFvzDghNUQN7w9Z4igTZUzpmAqppGiZvVfSzAQJUgQ9InLIAeap7zyeT3WeF2Kdfb7DdP7LdJPamr58CsXx2omUmWveANTbYBo9rJCS9V8ekLbfWmakX18mwVf2U8t4PmFKAj6jxu8KWyCY4lA1QxiWwnDDQrfQI9TgFwpgDBQlm2kZVrdv8WyRr7uoRI4P/37+30HrebIGP4e+ISyVgEmiqKDVduIBy1WQ78Hq3H/8gaeVGGbiPg3w0TS3v3+3cyogP0jnRkT8CxsR6zJFEewor4nmufX2S6ZjNc628BNfMEzgG5lqmm1vWo1GTjOiWFonWpEC9WiDHCyCW/CWsRMy43aJvwFm2A/fTeQJWGj9iML0Pa8Vlf/vDb+UY8uKlecTXEHeIFXajOOX2voeOlqyLBPUfBvrMc7jlXp4pWvocLrun51qOgfk9uj2mSHLtyMoKUTccH0D8aOBKRxQnQr9br5DdfPSzJxwIUQczoH3PzxU7ensPV+0/IeHyx8g5J3Ef7zo6J5NQZsyCdfQG/bXGcYiG6CH+VoBDhcD9WwT0GnGVAY+I22PQqvCZTHJNl/JuZJ+q5ZO59hGHyEI3cj+i4Xi0BEcMgNiNQiZiqcc/dEdyifTipHW7fsRE8zmqLYngmAqX8PL+eigvb3WNdH2lcaTBM+dwVp0BiQxxfKBrgNXh9YDumavde2KgpJJzvOrLGRydeZxn5L47csrX14OWSPMH4hNeR9cUhwvRGLgpjzM/1p2Xd329VED9N3ieDZ6nxLxeE2VEX59ibvgM7fXq5cVQPsPri4nGsiyCKy4e+fxEj35cLHblh6UeEXsPp813wPIf1nBzReznsqlBrCurD8wtdKLdb04vjsg8zZVHy5wjjUcv0vU3eXejR6jugYvfv/9pKIeY+wocDfCY7z0c/6cRj1DJ3vecYC4ZHdPD2onr/scOziM8NRB1Ou5OM3x3SeC+Cyx2ucG6e6XlCkdVWmDX97XWzQW+X4SF3u/nmPOj5Ouh/G/+9jdyqGxH0mQymUwmk8lkMplMB8m+SJpMJpPJZDKZTCaT6SAdhrbWtVy92aF0V8A2Wuylh3SAwrb6Cdyrptg/HiGGwAy43V5hy7YG4kb8dQXshw6tp3m/VYx7WW/1GrUjKqvlCfCMzy+AgE3VYXKUyBX3Ese4FzAc/NwCzpAxnTITxRym+KynlHMijwabKXA1H+h9t8BvSuA9xHIS1HMcog/A7er0RLGnFGhLUygaxO351S0SN18p5tr2rpIBUCuah7Fumw/gGXSoC0cOq0iO7PcjHGheaYl70SF1lCBY33+sX2uaupabqz4RLxzJYnBMDRka4EczYE/PZ4pXPYP73DmcWqdzxUmZ+D6MgVoBY+pS4JTk1PrLRHSRBObkgc+lOdq6QF8DopNcKlJE5OSnK3VMfH2r/aikGx9ahuis64DuAt1cXynC/6RyIkGPhY6SXPMtiBERKizAmGKCe++ROBwNEyLgXp6eDeXPX70YyidzRV5iOsExYXkfQEKg0A7oFl3+HJ8Jxx3CUmNAFCmm5ZCsOcI1i5aIH/A5BgL0xzmQsdM5ENwRZ/l06tpWNusdhhbCfbcC8kQH4S1cZYlFlQUQZfKGQPNbUZwyyVAvcPv0jANsj1H86v+daDxcAelEyKMnrMIEy4iTuc5ZxN4YV0enAT7QFP4DtKPnvR8JpRPnxYV9P8XYoWN5c6fz1JaY60bbZbnSeNFinZFEGkufwUE1B95N680OQSHEwigEBtf08YvYdoiajjB2eVSIsSTDezrEQ6LpbDzOfUSOebSCuP3InZUOxKMjBk+nzjvZNLvPqdFHS+DX65LHHnCsAmvUeabz4KvnGhu/uND4Oce8OQWaH8Mtl5g4UdEI5fhyt04+yYE70oETzuQwd5Z7rH+7dv8cxyHVfABR9lx4j9rLf6D8Ac71CeWcZnRY3+q8fv/926E8KbS+low1XPPhqNIUa4UvTuBS/gxtiiM8MxzHS3F07n6l451u8uvH7yzoUw2OzzR0tAaunqd6L9lEP/N0omPkOtHYc7fG8Qgg4gFcf9NW3796o+g9woB0+K7x1eXh7vS2I2kymUwmk8lkMplMpoNkXyRNJpPJZDKZTCaTyXSQDnZtfd8jrUWFhJrYgq0KIjd0d9TrzJA4m1hbjK1kieiwqdiABwJEHOfuAc6u+H581iN8CRAs4hbVVLf4c2BsL88Uxb1A0m1iWmWln7kBIiFwRkqBZl3AhXKN+ivxHMtK68/DKfOp9Yi9MMEwk5tv4TC1hHNisdbyBO34bKb1ewGcg4mVM7htNnDt7EIgccCxCrjVPfT3cAfXR7qsbeG8WgHbiNHBiJB0QLaqGngg8CUiHEQO+fsLySw64IXu+C6RXdfKarlDPTztxhqiRVrOwj8eFyIir4Bt8PWTM2AWaK8M/dIBt6ND7ug3Kv5c1d9nAJflgJgHca2Ry5rWYQScMyKiI4odhbgvmu9ewWmYKEoIBKZutY8nDFDuOAiWEydh/zkt3T7RpERSibO2eH7+rWeyezRACkT57EIRlukUOGsKNCuiQx3avU927xzQ1gCOnXRuHJkfwyaa/E2g+HEGRIpIZHkHHB4xKUJs55wQY0yczvWZuvZIaKv3UvbzE8z8pIZrcN3AoRqutQXeg7ztIyS1xdGLDmjvenkzlGOM3xGRSJdGcvr96+xHDmcDPOYBhGmpgMm3GC+z/BdD+eT82VBePei4G0XD/aahY1tpfFY3OnpwpATo3kvbO94SIe4wZ2+QUDxIgaFiDJSi75lMdO7PE13PnM60LE77AOfiQIiw7se7g36+8Zh3iCk2JY5+lDzCAqQ8hatnAodVxJIO12S871rGaiCcdGplGWPQH2k/wzuRoo9DLZa620rbi8cBUrTd5Vyx+M+w/nt+pgjgAo67U8RGrjUjzHMjt1M6q/NoR+/UGWZAuDHtRJdcrxFRV2dXupFzrHWYZ0doK1FQNgWP59BRGWunkemvO1JcbTpZ3+3ix/W7b/X1zfuhnMTo35gnGFefEVFeaNv9ky8VLz9/puv+DO0bIn7GCISLuY6fCgG36NcWDWM54tVyrfGwxpuSkGirfv7kORyCUefvb3CE562WH5Z6/QZrZpxGk7LFe+4VmV7MNG5/rGxH0mQymUwmk8lkMplMB8m+SJpMJpPJZDKZTCaT6SAdhLZ24qXo0ZwSiM5nl7oVegOH1RgOeilxVjq+0dUK70mBQc6wHxuBA6BJ1OWpbmEvkSA47xPCJnBpevbiUv8dzpQJsT5grg0Q1gKuousQKAicPx2SkBZgC04XilddrYF2oj4yuqiOWJ+nlB+S7G62WleTqX72ZqOowENBeyd9oFMkoP/6mSIfJ6fYngeuHMMVLgbyGqMNNnCzrVF2j5zFCmhzUe0tE7/J0L8CoAVbINJ+5CjIhNdAg+AUyvYigUXMk+USTpVPKd95qfvnpqunZ8cBcpQjOfKzUy1fLHSsLeDgmuZ6zQQurDEdWdEHRtgZasYD3XA9ukrkiRgXnZ451j2wLweMWbZoL9zXswZY+CuNT+GVYoB00GyIU6KBW7rhHgulExnc3QI8c8Dk48TC0EdbIlK4PWKuDnE1wzUztF2A5x9hqTGcJIGiuh7BcSGRJ6B2QHEdcPEg3t9Po44Yv7Z1gnKEIwM3bxTFiTPF0BwASboxny8U3Q06MJpPKSciPfblRlnc96Ng7FslsEn2P8f5EeiYB9ZIx+EK8ZzoYQusjeN0mIz57w2dP1Udxh3n3jVi70+Yv07g2D1BX9vCrbf7AEs3cnYFwheM3n4clM53XvyjC6bTe82A8Do8Q4z+5OEOHMOB/Xym/S8NiYfig0e4I2LfKJ7T+XzEHu7+h5gk1h6bQufSAlidR99ZcR0C59EQ2CbjDRePwYhRxjyINeCo3zGeHacZpfN6zKXCuqXA3M/+dALs/+WlttfLU/RjrEvpppqijmK0L5FQPjPbN8AYcD2a3MD5fqZUoySIn6y2EWJ6o2vUBjGm5h8QS+a4Y+wZndvh63w+YM/tkebHrpN2tUN3XaX45sUlHNWvFe2tao1H0ui95nONQecz7d+zFOtFtFGGYx2OR0vQBhMcA0mxnnh0QOYpggZHLRLEyRJxNYNra8vjA6XG9Rnw6vnJ50P58kId7H//2++G8hpr+QooO13uQ6zHaxxP/FjZjqTJZDKZTCaTyWQymQ6SfZE0mUwmk8lkMplMJtNBOghtdeKGBLZ0OYxgUXd+qRhAC/eoCCiOh/VpjNdjYlfYVo4DOGKlTJCtfzs7VbzkJZwEw/46GdGDqW5Hj7ECUQE1CrDFXGMbPMRWPm5XYuAndBANwZ1EeNYNXJXoXFseKVFvFIZydrbDbq7vFBVY3SOxMnA0ELwyTbWef/GZuj5+fqoYTwo0Lgj2/1bhR4yXlumSGWC7/axPkp6B3+PWPP/uodS2iyLUPxN64/k8bBJb9M2qgssZk2vXRLCA8RDBSpiI9liMsh+SPztB4t5jAAAgAElEQVS47xIwidDnJnCTmwEznsMdLAZGTle4EChMjMS6vsM4heMYk5cTf3xs9ugDOHHU6TVq2tWNGDDagHZ7Xw4iZUrmGJynSBz97l4RoBKDnwgfnQYPi5aH6ZEoojvcCLPmI4/cgfV1InAe/TtBO84wpiKPIwNwUw1kfzn0cGjtf4Nk9QfEXDvGVcQDfKaHo3XX6n15xMwo1NfzU0V6JluNmcuVYk2cZ7oamDTiw2Kqseop5cRJ1LtOj5wYu/F7hjJiCpNS8z0J3JJ5DITQ6QYugpuCrtaKKBHrJ4LcPiLV4X5slTitazlXa7mBU+gaCOXr19/j/XqPs5muEYIPIPl0mxwReaP6Ow4T6ZyXsI8fbqsxqMYRl6SD8ynCVNDiOE+kTOJ0qm6QDvNTBxQ5CPBZHFeyP7bDFHaYpDscOaKDcYF73+IzRzePi2/WQOBmwOSJNSI+0x2drs8B0EIaorcjJ145irxXtLDGfMTPjrGGPJ3pGvFirvNEjjkxS7EW5fEczH2R34+f8giH7JkTRUSCvuc7DvXRMRAtn3MfCAOmRNwrEUvXcKtllTeyfy52wjU7/mLk1MqXj9OQbVfLQ/lGRESiRJ8hw3qmvMF6CxhzgvaazLQdc2aIYGz8gI003eRHyzlizFhrpY/rZGLUOPIWnup9ZWg7ZnmgO+7m/mEoF/fqcD471bhyfgoG+puXQ/Gnn94N5YclMNeKa2OsHdLD46rtSJpMJpPJZDKZTCaT6SDZF0mTyWQymUwmk8lkMh2kg2CtMAhkke+2T2tskzdwTjxZKH5UFboF22DrNIJ7URBhW5mJVLE9PUqqChKjxm7zxAMByhStiHoUNoYLK1Eroh3dCK1RFKSBc2AjuMcRegr8A85mHTPZYxu8hJ0TkY8NHD5vbtRh8imVxKF8+WLXTotct9Lf3FwPZbqUZkA4ToFAXSKpa06Hq4AunEQPsX1OsrWj4ypcF9HYjzRWCsxkQQQrAKaHftEEwDmAYOXATBbavFIhcXZHN0Rgrh4oWcDfYhwSHZfEDI+FtrqBQyOhJKgLksUT4Bw588ILEQeP1+HeiOt0aJcwAJLIMcCE2sT8enSkwzgKWmKlGBfEn9BHPOwI2UYdXh858aL+6R6ZAtMukKSbiI7HzbNfP6V8/38i/x+8D8yXdyOGSMUk1yRSAqKM+vIkI/YPTHBU5keRy9zTj4kXklEL+F4mJQeuC3dHD3dHaYFOR9ousdO+dg7nbQGmXSF+whhv5ORXV8c5MuCcSNgj5p4OhsSGiFGhYdJk//xEp+XJVHE7BwRqAtdUunOuV3qd1Vrx3wZJusM+nieowxCDPWesGxlQ6nveXCsiXqz0XiaJ3juR27OFzq3PXn6Bi2rxQ3DV2OP1OChdIF5yv6ujiE6qJfqN0+d0jfbd7VafLZrrWohJ4r2nqyRdwvVlz4UO4g6x71HZ9e+n+zRqscQEsYaVvIfT5KjOEUsrURSW6L1Dv+aRl060rQP0U65/WsTn9lhsK6/Nowv4vBRO3zNMiikWERGPcdGxHHFytKblGAfyO3LDxhw9evoeH2d/CWu+A/eO2DyfKip5iqNbD1sd63Wj7bLFPMvjG6O1Me+MxCfnJb//PU+puq3k3e1rERF5gfqvOIxKHGnAPS3QLmc4CpWSbOWxO3zuaJ3R7Z8HfbW/7h7b3fFoFUYYUf80gzM33bgRp2c4khNV+v6mxXyHOLFYKOa6QUwq8L0jxhxV0V7WHT4/2o6kyWQymUwmk8lkMpkOkn2RNJlMJpPJZDKZTCbTQToMbQ0DmfXOVsstHA8LxWbCM91in07gZIXt/jAEAgfkIoBD3ThBMpFX/e4b+Riv6xYvkVfXfy5RkYgZcLHF39KJLYSDWkt7tP1b+Uz6TbS1xfUD3C8dvDYb3PuDlv2r46B0znuJe3T4Zy/VQW/q9flXS6C9qLvZFA6bCbFdbuvrNrwnvkY6D+iywOWs+xBq/OhOCkfeBJ8ZTbBlH+vfbdDvPLp7A6usZMQEAq3Fdv8aCAH7oGT7nUpRlUd0bdUuyM+OgVhM4PR4MYFzIlDCBEhuTHta1Fc7SkYOLKYk5oq6AB7RAQV5dPNklRN/bkuOdaJb+v6W6ClRXMYYYB4x8LEIaGUO9hFDUMoOCdOBvGYnx0lk78QNzozs82PICInI4cYYYAxkwGJkhPNqO86AQH04uft+B9Fxoz3+OxxZo/3tNcaf6LpJx06g0AGc60TnhBCXiSLtJycLjWEVcCAPB8vtRpHPEgnZn1JenHh5RNP2o9h0BcxSHscAeoe57wzZyGdwre1gC9ud6PuXcPcjxp2niMNA0x4dDul6nQKBT8HvhWi7AvXcEemKNPY/e3k2lN+9uxvKP77/g/4t1gKXL9Vp0Af7cbPRnHuk38Gd95L0iLQHkusd21Troibzm+jzx3N1VGyxRuoaODnybzGWGyCndFllbI+BHTd93PSYS2tUTwPUvCq55kFs1rdLhzkhQmwknsq+4XBUIUP/5Zh1YOYrPF9bHAc19+KH56uwhuP6LAfCOpoTsfRKMHZCxMAoJM7LXgr8lXGtQ1xDO3YBsdjd/8aYS3nUwNF9FsfL0ggO90DgHzB/rVHnBeYQxgOEXnHo42OCdb/La+COt14N++8ATazPc3WrR7+WcEIOgQ1zvEzR1hOgpRHnGx5lGR2BG1m1ogxEG2uLx6kwwdGf0VE41CezCSRoxxKOrzyulIHLZdvVaFMZHVeCC3iM42gToK0PegyxLDVWfaxsR9JkMplMJpPJZDKZTAfJvkiaTCaTyWQymUwmk+kgHYS2Bk5k0m/zz+aKSK1vFRWqsR17AuwvARaSIbFxQkQHKGEzhZtYACwB2ESAbV2+LiUtpnbXjIAtEMUR4AFtg+1r4JGjXOjIwBoCHXIFt/iZ8JYIITAHbHNPJ/r6q68U71nAAfcp1bSN3N5diYjIF1/8O8Prk/hiKC9vlGe5vtaEpie41wnRZSTkHTEyEdEdYDRwIiPuxSTTYcTuuWszujhGREyBJSeBupblSEzfghFp8BtKnut2/2wKlA6YQ32j2/13QH08EKAR+Yf+ExzJlc6LF9/3NdZhBPfUBbDGU/A6SUz8CKjkBgjcBK6p8f5kxi3GnaPL6ggp5k3vKikkBtwQJ96fMJcuoC0QMB+j0kHGB0reSwKXwgRYE1HQkckozUeB9yyArT2t/IB/BkSnkMC4a/djownaNOQ4Qp87yTRWnwGPZEJtJlOmEzHdBd0oKXX/HibW5u+SfsQg4s84SOgAibeDHAowfpmgOUb8CIFiRhh3bQsnVPxtuSUC9HTyvpOi3GHRbIsE88TIqRZzQIe6S4HMzSZMjA4sOUbSd7TdyVwxXx45uX9QR24PtPLRMJljkEnaM7puoklXS53ziwJjCrgu3SZzjk1gsZsHRV7b83P921yfdWxNjOsczezTDw7ydN0MG6CMXENgvnGx3jfnxA4BiW7JnsddML7oDt9hTVUQsQPe/Yj4dy0GD1F3dPl2FG+ZdB7xG07aVaKfE4ecz3GPcCkXHHnBqkBCBFnGueBIRz+89wNCyyUhj7LkWJ/N0UlzOrhiDk3gsEmn6w85lnouUbD+4ZGEYBQ2+/+gCyyPDwCrjHBsJca8+Xj8TETkOebnO5zfWFfsO/h8rqkDfi7egzKPPnTdseKql7ra3fvbe41jd0vFMbfo02HMeY1YJ9Z8mNeIfgZsUyw/A/Z7upCzgXGdoU559OYD7+VYGL0FzyFe244oLN19EQ7G32XQYmWNI2voMw6Yekuk9iNlO5Imk8lkMplMJpPJZDpI9kXSZDKZTCaTyWQymUwH6TC0NQhk2iMn80q3Wjdw+FyX6jB2eQEUDNvqLRBW0K9SYiu99XAmAs7R0VkVjlg+0u18btUPBEq339GoaYiM4Q8bIntIpEt0pMF2MBKmj1kFLcKgbuQcNou1Ep7DcasNj8PuhEEo8+nOUW6K5LXZRB0CTzJFcZJA2zQBarZAEuIETlItXLC6DmgcXKUcnQyBwRH5CAXJsB+xGKA4IdkmYkchkpijAWqgrQXaqKz1+SbA5F5cwA0Rfba+h6snHczoakhclLjEE8qJE9ejLgHq4gRY2CUQ9DT8AO5Q7Xc0dhhfHD4NxiOTMtPpz9HxtSPS1P87MW/giEwyHNIV0BNr1L9t6GIIGq5Gnw2AZqX4gJzoMnE/uuFNgW8DBX1Kea9Jnic50Th9D5G2GMh3AAzSt3TQJcZMxBOOdhkcJjPgeUKclf0BmGPw6L6LeyGKDtfBGp0HXUQ6oG50bfWIpXS3ruHMHeI3UI/rpLiHhomVU72f6exYiLI6/eU52oXJv4Hzdph7skxj73QyHcpRRNQe8RCvR4iZLd2SEStjYNJEmh6JLU8HbPSdDE7qAe69pNMmkMgI+NgEqOC7a8RM3KMHwllUiqrNcronov+OuLrjxFXxTqSftzwx1C5FWd9eIWZ2dFQGT/qhIwAOyFyMPsqx3KGtKySVbzcas1x/5CiMeMRG2+JhqZgzTehxykhqOHPzNEaA4zycZ0K8qcUYb4C8RoxJDZ2ZcX13nP0M70XqHq+OgKeewbn6swt11p0AW00R68OQqCTWpXQ1RV/sELO88DgTkUQe0frjpPbj+VGfiTUVpkBPEUsc5nM6c84nGuPvt9oJGsx3ftQujP34YNxFOzoTcpzx2HadrFa7/ru90RhRb+lqqmMnGK2btZyin6WIUzwa0NEiF2tXH3IM8GP1c+PRemX3v3QR57Et5zA/YKJvPesWjwHHXzrVJ8Dn6xioaoXjDhd6nOUa7qy3KLP/Epn+WNmOpMlkMplMJpPJZDKZDpJ9kTSZTCaTyWQymUwm00E6CG114iTut+qZiz4m2rHSrdy6wvY9MMWHQrfVE91tlyzV/+B2b4DMut7TnQnOS7FePwMiMri1AontuBsPdIU4B3FaOjN1+OO6Bh7ApOpwmwxaIH7AWyqgkl+/uhzK85k+9w0SET+lojiSy8/6zyQdkOr9TeBaNr/Qdtlc63b4CnhTBDyi6NTRz8P1b4I2YgJs3kTniaz9cZnQQgNnObqwlmACKjTqCu6d91vi2FqOnN7LBKzk89P5UE7R725WcEKrgbZ2dMQ6kr2g2+HmIiJ5os98AVz55VwxPmLkPtiPIhd0EcTrns51xClBQXQYm6P0zEA+Hp3LaLhLZCyY6ntrYOR0xw3IfABpdnCWjWZM3M3k4fqe6RyI7A9v9DkwxkEjye2NOkw+qZwibsTbWmC4o3gEFGeJWFoDV7oA0nwHrO13v/t2KDdIVn3x8rOhPJ1rXw94xED+GGMi8tPyaAL4uZH7LhMlA5Mj4kictgEORIfLBuPUwwl1yAS9+2N9Gcj6RI6DKIdhKIvFDlFNgIpWDgnlcWQihNvn5bk6ZkcjC1sgqZhnG8w925WW1xs9klABBV5v9XUPhHHAUoHahcS48N6I7rN6BQEVLjUSgydApMKEuDSQyBqoZqVx2AGVDDhV0DFRjqTAiRueFUcjEBc6zCs+gns7KmYL9DRAX6w3wNRzLefAeT3d3oFJt2RqcXyi6eurRWAtNjoPb+60bmsmXU95bf1bHvlxI+dJHkPBc4xcLbmQYitx7GN9lx1vP+NxyMxxfOib57reenmpsW4GvN9jfVJj3VYiZvLZHNorJO4PbLUFdswlQYK17iOyP3JS5XGT0XEMjZmrUvvC9Urbuqj3H7kKR2W0NY8MyIcEZ31PBP1I7vR038U6k+uNCNhqFY4W8kOxRptugYpucU0ewUs8jligi06ARuf4CpVP4ajb97eaeDsdu1HP/J5Ucu3MIyGjZ9LnLnFejv0owphaxOrk/epLfaYlnLfv1ji+5kYB96NkO5Imk8lkMplMJpPJZDpI9kXSZDKZTCaTyWQymUwH6SC0NQydnC96LKhTl7nrm4ehvCp0W/27734cyi9fKLpDZPHqSlHJC2yp5qFuH9eF4gRTIJfhVLd7M7pnYhv4EVPzzR+/JiJSAHnd4nO2QB+Z3JP5tD3wxaLR9z9uw4uI3MNZqm2Ai8INL4Ob1upB69LHTOf7dOq8SNHv4N+vsJWOZPQR6S/QND/c3Q/ld1f4W7hmidNyAqxxAozmdKHOhC/OgI3CeZMJXKt2V49EG65X2neu7nVr/ic4e22BfORwr0yI9DREkBRtOF1gux8ItnpgibREz7baxzZwayu3x0m47MRJ1CedjoDWLOjEC9fRkGZcqOgVcSlipnAEy7y+v4Rh4wxJrycJHY3xWUBzgh4JY2JlOgHqKBJxkT5HF/Lztb1K4HBlof2xATo+B/ZCHD4DCpsBBdng+tVGsdCb+jjtGLhA0h6lC8HQFHQ8xOsJ0TTUbQIs+Itn2kvnSBJ/D8z1b3/966H8aqlx56tvvtG/naurYQRnbOnvt9roGHlAsujNnbYF3erO4CA3PdE5pAGGWW71Hh0SLo+SYgcj+9ehGBK7CojhAWOOjoNgBU4k68chnTljOuLByTRGkJ1N4EyKbrbZaj12NcYJ5qr7W62vBnNPg3mw3Gr9uhGCvKvHzsNdEM9UAc+s8UxrXG+1VmyW4zEHVp+MrgpkCy7gfoQro91jOmOj7Y7k9umck6A/B8DjLuEI8YTTIxwgW8TDEEj3BBhzVWub3mx03G3xzCHG9clU58pTrouAtZXl7rPWlY7HZkN3fG2LCO6kBZqlK3FsB/0hBZZLp1y6agcBMUMgzSjHfB0x4ViurYFzkvdz0udwZ/38uaJ+U4y7JNc6YsziUYIKdUTUvFjpGNwW2gY8qpNg3J3NNfYtiEomu3IcM8YDZUSy+Busxd7i2MX9RsfgKIPByFl2vyNryLUzj6wJhD8AuTtq96eU9yJNjxevcJRjiqM3DQZqCIS0DHRMfXur9XWFRUyOONyIzmEdMP0U2OhnlzqHvfpC7zOIdZw+1l1TwTl5revSJVDSJWJpi+eg83eO7wgJjgnEOJpX4wgF55kwxBGK0+dDefWl9pPNH74bykVx+DrHdiRNJpPJZDKZTCaTyXSQ7IukyWQymUwmk8lkMpkO0kFoa+BF0t5J6BTYyumJbvU2QNayWLddV3AgZYLmLbZ4724V+ciRNLpjwnQgB89vdCv5KzifXp4pxvDoaunhcFXBDen2QbGcd+916/v+Hpgcvm5ncK4LgdPS1SkCPsekzK7Rez9H4vGy0s8qat2GjpLjIFhlVctvv/1BRETO0EYvX+g9XZwqepHOFD199fXXep3gaijfwe1KgBXSgPAO+NzNnaIYm9XZUP78pbbjNIezVc/iroABrIHlskz04mymfWQBJ8tTtGOA93sgyl6QAD0BeqefJBs8qwfaSlfSxh8HiRQnEvbIBZPh5rMZ3gSnYDh/vnl7M5RvgSHS/Y0uw5NzHY8vZjq+ngF9vDzVvjvLtK5jZlTu44MD3lbjHrcY6/dAwK4ftPzmTvGTNz/pc7Rb/dtTtPXLsxle3+9cx3G6AkJIF9UCWNFTyjmRqEcvQbdJB1dih7aLkIiaLtYpk8rjb33Hv92fmP71ax3LGfpS/PXnQznPtF6a3lF2i9i1unuvnwnylC6Gyxu4zK71b+sIrq36p5Ky63CcAoMMkDCc444Jtek4G+aHu9J9rKLeyRJUrcQ4xpDguEICV+gQ91rATa9A0mjirBgmktMBEChdidiUATurgW43PdpaA4kNAiJtOAaC5NoNMO8c3D/daj3tXHGdCMhcDDbO49iCA8IaAd8i+sX7fFI5Jy58jKtwhSaeird3AY++4L5R50xc3uGIxXJ5O5SLt4pHZjjCQ/Tx/IXGsuoB65j73ay0xBoqm+n9uk7L74E+ruBYvkGnitGBu5w4MZBtOnkD/Yvh1oul0DA2du9nAvnj7GeEoZOz+a6uPweOeLbQOoxxHCMG8st+z2MYdFEOME94PEOJ9W0DvJmruRL1voYjedy73Ec45kU8lf0/BJJ5eqJrqDjTdtwWWLe02r9G7scjh1g40WJ+DLAWIBYbChHc46xX266V+x4LLTC+6PzfASFt6SyMgXqLI3ijdkm1jpBYYOReXaMel3cak6sz7VcV7ifojxRx7m0qvUZdorzGMTp8N0kCoNZEsNFnSYXDAFtKXH/Ux9FnLj57NpRvcSRxec9+8nGyHUmTyWQymUwmk8lkMh0k+yJpMplMJpPJZDKZTKaDdBDaWjeNvL3aYWUhXYQmunU6jxV1y4FROSZyjXW799m5bsk3QAKWpSKvAbbYuSX845ViIRUwmhYQwfnJ7n4qJOu+Blb55r2W393qljWdKaOFbgdPkcgUNIdMsMXP7f4GuM5ZpFvVl0zIXiMRM/CD2ZFcsJq2lasenyrQFi0xNbiQffmFJiufLdTx7OQEfxvq354miuJssJ1fgaHYwO3x+xv9rBjI7xdOsdShupgoGXjVPNO6zZAY1jEpORAOJvROMn3/DNhLGgMtEL3HNmNfgwMfEgHfEUdpjoO2OpGhpxHDSOAiKHA8vH3QZ1ihg3t05AxIzZZ5eoEUvysUJ717p5jp1UTb7tUzxSZeXKpj86xvpxLjceu13q7eKX7y/Vsdm2vcbwmnx4SJy091nKaIT8SPm2v9LLr0CVydiWUS9anq46Ct3ou0PbpaC/qWJ+4JrJSIJ15fok/7e8X0/a2+Z36qz5znWkfTTFGzWzjjpbfa1i9e4m9PdmPTwRmyFY0NJcbmw1rb9O4H4u2/H8rE4RYXOgZfnCo6tADeQ6zOAwsNiPQCwQp49MAdNO19tAIRSXoEzHE8plrPMdwziQPWLeJkp320Qx9IgESy3euWien1/TVQxRbu5B6Wyo9EXgSmawPnz82W8zDw+Uxjc0YksKWjsj5TROR1dO84tgKnyhhMc4i+ATpytC54WrnBsdp5uJEDpWXy9SDQZ+6AhG5beFC3cO+cYI5Z67GRh1Lj8/pBY98Z3Bs/u9D1Uj7Vurvr2+nkRNdfBfrFW2CzV3daXoKH26Cc4VjNDONlMscYBPabYXJNRw7TwJixbxFjfITBccZjHIby6nRXH2en2l/DgHgujirBcdeBkWc/vgfWeAOM+BaO4VtggimOeJxkOLbkNA6kKd1HH9sUtvngF3HCQQpgk8sN1rdw5t4AoWV/AKk6ipMs8mgAjwzQRVbogN0dZzz6TqQqdp/T1lgTIzsAj3iEwOUj4rYV1nyR9oezi3N9f6J/22A8pBEwdawDrm51/cM1R96j2yHqP8cxGZwkkADu9HUB120gzxscHWPcm081lgSRjqMacbgtgCuHQF7RXhenWgeJHP69w3YkTSaTyWQymUwmk8l0kA76KciLSN3naOG38gAHgFMYa0xgLBDgp44Ovwzwq/kcv9jU2MUJ8YvnyQt9T7nUX4dq/Iq6Wmr54tEMhLnk8OvCy0v9Jn56pkYvDX7t+B6/Ds4m+FUevyQF+EUqwS6f47d7/Nozn+uvr2dO6yPDL8fVA4/0P6GcE0l3N7PFrxU3oX5e3ugvNt+9fjOUQ/wS/f4tjIpWWl7hF/j32Nms8Vk5jAA8cjcW2IlOMv11NesPCdPwpLnCjih+XbmumHsNOyOF3kuOX0HPzvWZLrH7fDrVBsvxC22Ow8snM23fKX7tCfG5oRznELo43WlpRzvyOEiO3bgUv1jNE62vFfruGofQFxiPV8gRyFyPGccDft2NYOIUOuYC3NVRhF8y65XWG81bRoYeqf7ytlnprlbc6jOdxLp7lfInMuzqxKiDDL8Wh4hVzGVawpiiocPJk8pL1/+i2rAdYYbjEL8a7MJ23I3C2HhADizm3uUvlZ9PNN4luY61BjtDJXJyBdgZyh6pA+brDGBY9NPbofzDFeLxpX7mBjTI9kF3UBvMSgl2yrhrGaagDvB7KLxmBBsPo9xio1/gn1DOuSE/LXfdmKetwq/cziNPaovn7GC0cspdSH1/iASA9+/2kzlVx91tff98rrtg08VuzDT4lfvdex3r281Peo+o3AY7v+sHjhEdvzHm2Wymg4r134IEoLlXBKolgZkSN5PD8HimSW1/j6PIjR2gDmZhfB7m1WPu6gLUBddCJ1hzvP+9Gl4VS+TmfP5yKEde42Dndf2T9H2pCXSs1RvsTmOddflcCaPPz3Xc/+aHPwzl9a3O+SvshpxgdzRCzIxBvsSYW2ikw1yuDuSLO1I7hqGTeb+D6tlepFi4Y4V1bIOtv9VaY+kb5Gu8hkndLdaiLf72BQwMA5hrdejIzLX5mJ/UsU9hXBSI68xFeAVTLppJfvmltvUtiKkr9C+HOSQY5QnFDi12/BhLuDnpj7TOcc6pOdNcqZcN5keaAcWhvv4cc/wMREdHahC3/d0bjX0CivLP/oN/dyifZDBGe9DY24IqcT1p0ZG4QJv7Rtvufqntwrznr9/qGOR3ipcv9DvLM5hIzSca1x3GYIe41WIdwRy5Cb5vTf6E8Wg7kiaTyWQymUwmk8lkOkj2RdJkMplMJpPJZDKZTAfpsFPOQSgy3W2fToD0laKoWbHGAX/8aQJcKgc7trzXLXnmoHyOLdvzZ7qVO4sV73n7g+JTd7eKhTSjnJG77WZPJAbbvguYT5x43fqukRsrWipOMoXhgzBHD7bTp8CuapjtRMB+Uxziv1zo/dy3WpfdFGYgT60eoWiRvy0D9jd7oZ/9cK/3NM30PRcvkT9Ud+ElCGBeA+eSledhZ+Bo2M6fz/X6bJtHk6UsB8p4qv3oGvjNBB3v8pmaE2xLfX+O3G4B3VU6RQ6IKbUhc0ZpO14gx9erc63LH5c0UDqO2U7g3ID+BajbtoVBBtDAFFjSBjkaG4+D3PhtaYa8kFWDvK5AkU9OFadogDneY1ynEXIdJjvjnQAHz6trvfcNUVKYVSzOYFKyUeTkbqO48gKoYAtzi4yxBwYzE5QvThRvhn+MLIGLuvZYaKuTsEdOaKrDHFEBzCkIEOUwnZlPYT6AdoyAtoYwogljygYAACAASURBVCIW883Lnw3ldqvYUwMkr0PuxjbalStg+RU4JwcDq7McuVwXitKlE733soY5DfFj4HMTHHHIkQ/L0ciFhighxwTGYHUclM6FkWTznclUCNOVNNV6DlPtxwFQ4A3QuId7mCYBI0tC5GnFiYkSOflugVcliNUpTBmeX6gR1vR0h4p5HMFIgKg3QOnWOHYwwfVyoON1qddZ8bgDzJ9ixP4Ic2WEvswckSHGL7HQo6Gtzg0GMA2OTPgQeS5b4tHAxRADE2B1CbDsLY97bHHcokNeRswr+QRHLJADtakwHvrx1iFxY+3132c4qjJBzE4Q9x4qnStljliy1fl/McXRJcSVkwmONZDlxhESxzmfeSrbIyGRogZ7rqNRmj4D8/x5x3lKn+f0DHg5jjxNTnAk6VqRyBpj5uuvFEuOA/wtjsfMFsi53Pdpj67NfKTETXk859mJtmmB+f+CRpHAxd+9ezeUf9pqjOcYpBxzthIHHp0SOM6RgTAIZT7ZzRubBvNNh1yMocbPKW7vxTPt3zMct6lF62KCPOkNZtcAzOskwbof9zDFES06IT3mhm8ckWDmV9fy+Qymjhf6OVWBvJfM/bvW577a6sLbP9N2nMKkLoho3KTFjkcfsNaapoebX9mOpMlkMplMJpPJZDKZDpJ9kTSZTCaTyWQymUwm00E6aA8zihM5e/mViIgskDtpMdFt1JtffT+UK+CGabofScmBtrRAZzLkpFsQXUKutFPgifG5ujmFfg+CAiwtBmKaARdqgMNtHvRecmwHR4W+3sDNawZMKYRrZguXRO+1nIZwiQS6MwPutemOk1/Ji0jVIx0hGIpgpvd0dqK5/+oKeFWnz3l5pnV3Cly5WAM33MLdD5hlCJfAKZy4vgRux3yITe/4miBf1fMLxeSiQDGAFHWel4pUXc6RO+hM/7YF3hIgv1AbA3MNtRzD2SyCse6Xnyka9GapffBuTZ/Tp1PgnEx79G+L/hqiH0fAaXLgkfk52hFoxy2c4AT40fmXz4cy84HRqW0CJ74IlpkZ0EbNz6bXmMAJ+Kvn2kdOp3ovEa79/IvPh7KD+2uLcd04YqiojxC4HxCdl6fadm/h+kwEhXj+k+sRB0PfihHfGtxrB7u1DMjR12fajhczHVMtr3miGM0zOKgSx/eIOzAZlRaYftnH9hYonweiNkc+2PCZXo/Ot1+9UkfB4JX2r+lMsckL5H9bENGFA16AfIzMl9g1yAkGsrWW47hhuyCUsB9LcQQ0cXGJ92gfpTN54PT9mdJwkvA4BI5+FA8a7wS5Hqu11lcHjHl+rsdDXnz+xVA+udxhriVjJpxiJ+iD33/3HT4Sztic4zA/09W8wzmBDkhagnmQOdEy9J9RDkpaDR7nxIA4UddM9jPPZ8CHdzhKw3tN4TY7wrUX+gw/vldX3Iu5PvPlhY6NVxfaIcKRgy3yvfZHPlyg47sFYjpDTLt6o+1Y3+EIAJC2i0ugy7HGkjRB7mY48c5GDp/73U879PcOzqJVdSR3+s6LL3Z9002BsOKe6DoaB8Ss4aKMNVLyTNc8J4hll1h/Em9mPHd4/WSKIxZwR/f9WPI8RsG1Eo5W+Vrr+QxzdRQgJyCOcTU8loS+dnsPB12g9N0oX7GKeXsJs9Id9EnlO5H+KA4zHNK5WxDrZ8h1erGAQzUTKCB3Y9NoLJvjCM0c6yKizh5u8l1LrB1x8LH5EDMc6m2C63E+97iXL860jcqC7sdA4PFMC8TqCJ/FNZrHGGz5uehj52dA3D9StiNpMplMJpPJZDKZTKaDZF8kTSaTyWQymUwmk8l0kA5iJ4MglEnv2hrU6pI0m+s28Tlwwx/e/jiUTxJsGU/w/oWWmfSUbkgwuBIafHVIDLplYlRQAVHv9FevgTwBqyiBm+ZIyj1D1Wxruujp3+bYKyfuWBXEA/dvSbtA8Z4MrmA3K014G0THcTPz4qXt6zpHQvkk1PIsBx6B+7t/p065ETDTE9xrPgE6DMRxC/zCARdJgQktgMu4Dglne+wkhkNhiIZ+dqooziTT1xtgpSkcJpNKkdcUnyl4pjbXtqvpEgmcIQTWNQU6enGudTB/r/f8lHLiJO4d3UomCUby6RDoIxOEw/xPuoSOmcABge7QTTTC+2PAJhmc+1ok86WbmfRYuxtl7tZiChzuGRx6faHjrq7o3KZlDC+JMZYd3BMDBJO4Ai7KhNJwhYXpmqQ53M+eVH7Aq3O4RYdI8i3ASj14UxpXXk71/n72hY7fhhgorp8CqYrQCBvWKSogdOw0fcL2Uv8uZB9B8u1kgbZG3zwDSu+AIid0GoYzYYy+GRINwuVdoW1XE6tr9Zma4jgIlnNOwp41ikJ9fg/L8EjQhxAv2onW3cVLjWUcOzXcFbsSOCXQ8RdfKRLpgDplQFvPX74aytP5bpxsN3B6pqPmVvtaV2l5dafjZYv+UqJ9OXstHzTebuBqWQHhS4Gz5igTwaJjpAuOMz+KOHH9mAlon4m53GOtwtugg7AjUg8LZjqPJ7HWxf1G6+jly2+G8otnikYz8XqNsZE8rqmAI84zzF+YKzdXcDiHi/DFC3XznSHWBYwBmM9zzI9BRXdKuDfD2b7h3ALEjoj3U6rzIkWPf9LFW9x+d+AAxy1CQJt8D7HdCHNVgrm1BWbJfpKgb0wQq3nk6bGb4ETF6BoiGC8ZYi8Q8ZxxHZPiBg7kp2jfHHNiUesHN+0H0FYOR/S345GtnTS903EMZ3L2sxniRRbjGBuc6jNg2UTNU8xVIdzkU7jcEttt4OBfIDbR8T7p/7YoebRN6zZOuYfHeIv5Ed+N2jmdV4nPa9uFOPoh6GstG4w4K9ZUCfp+lhy+XrUdSZPJZDKZTCaTyWQyHST7ImkymUwmk8lkMplMpoN0mC2ok4Glqjdw4kPi24szdft8/d23Q/k2UGTzZKLbsS8X6hA0jYhqIMEtnOs2W90q3m4VC6k36naUZ0CmHrds/chfSktAhyq8HgBbSLEN7VsgmcRs4NwWI5mwbJlMWe89hPNTc6sup0QIXIDrPKHCMJTp2Q6fqjd6f++B9G2QcHl+ru5VHnhViCSmTGKdIBn5ZIYEq0g2XxXgj2v9XOSuHyFDj05hIbb1wxSueHAky6ZwnXqpF0nAwDHnaoL+W8WK4vhA0YIiUQygoyEosBDieSnQlFSOhGB5PySVTSO6bsKVboTFaDkK9ZmZhDgFdk7Ek65exEIClFu0YwKsLgSqGPQhh7gQk8hzPIa4rwTYRtygr7XoA8BSOrhmRpn23wBOwB0wyO5OXTD5HPSlo4PmU8p7kUfanm62ArTYAx1jkzZACU/hBHh2pnhkh7Z2QFsdMPEOz7kkOgtkH5cZXAUdsSz0BZCdEgriMdBOz/gGt9VEiMuqHNHahE50+jKGneAEw8hddoOE8E+prmul2uz6kQ8xdoBvtnCFHrHYmIpjYGqOyJwnsoUjCThOMgu1r5PmS+bqhp3nwKf760fAwYIN3LVR5xP0rxj3PgOyRVfVAkdCthutc6JWIX7LDmMmfidOuN8l0o3m9CfW4+TDj0AYDxhWgZh6zE9E3QI+A66zSLTtTl5q2z17+WIop0ik7jdwpMd1HscYXT2TQK93dqGuyF/Abd4DlYzhSh1UZCtRhlMsHSB9qNfh/Ijl0sjtks6pPGr0pHJOgh41b4Fpdq1+XovP5viKOsRM0s20ycQAyzAePGIT8eY4Yd+A23hH5+tdvXSjNQNcqdkW6IQR+kiMtUCK/tBEGGsoJzGPcQE5Ht2XfiyHHWOv98dZ53jfSeN3c1Jcog9hgZjArXjE3nZwrkbcS+EMHsX4roEYFwBRroGobgv2dayTT/SaYd8feDylBSLecK2EGM8YGMPd2rf7nY0jtgXWaE3Is354DgzOCut3jv3W42jeR8p2JE0mk8lkMplMJpPJdJDsi6TJZDKZTCaTyWQymQ7SQWirEzckbS2Bez5g33tyojhFggTDD8vrobw51a3T2xtgLkjqGlVA5iLdBg6I0QDraju6WQFl6+83FLpuAovCNjhxC7qv0XENu8cSAmelk1LZcPuYW9L7E4PKVl/fwh00hmvT08pJ0G/5b2rd3qar119/q467/+QzxZXn54pIuQe6g8LhM9Bn6ICsEWBxaMcEiCqT14fYno+KHbpMR0GPKxLhjOCGGAG5DYB/gJwaObrF4Pcaurw64K/43Ab300RIsgw317A9DvLRdV7K9W4sRXRPI64NpG+SoG+hIz86G4uIJAFc0UL92xB4RAIumMhgDRyaOB8TPT8iXkQ4UtzXtlEELqA7War9wsGNsEX9B0CA6BraobEDYDxVgzErKiIiMbCXzh8HwfLeS1XuxmFF10fcBz/bAVFm0nGYgw4J1UVEQvRdOuuFpGjXwFngapqdKjIUEYVtHtFW3CNicEgrVTjLBQ7YZrwfoQzRf6NmP8rku/1J6tsRSwfEDPfjj+QSKd5LU+xiRhMAmePneT2CEcHJPIq1HJJXght2CrdJAcYdeI3JbQdnV+BTdBKnk2Alu/vx+0koqTAn0MXZo3/FaLsWeD/R02iEb8E9eqFrhOlUMU8hMtfQ7Vtfl/A4cVUc64BYJ+IIl06sPMYaxC8Ph2jX6N9OIn3+c7imZqk6tSbo62V7pddBfHD9GPOI3zRf9KiryYTHMThGcLSHTpJA4DqMqSbA2gbutmwWrqnIR9INOzzScAyCQCa9g2mGYwkBl21AE5nUXjB+O8adFs77njEIMWvULnSC1TqqUad1Q2fk3XhjzHasIK5bML5CzrF8DLzOeaPjmNK3S8e5BQhwN7LGlr3lzh9nX8qJE9fH9Uq03gJ8+ApodQJsV7Dm60Ki1fpsERxfhW1KQhb1JbXWaYY4nE00fgV9u/N7R4h1qSPyiiMAMdDpuNVrj5YeNdc8cP6mezSfA+ucrtnfB3ikqah4duvjZDuSJpPJZDKZTCaTyWQ6SPZF0mQymUwmk8lkMplMB+lg19bHBOeRAyqKbe98rohOOlck8u5GkYz1UrdOp9iSLpaK3xCHS+H6R3SkxTY8t4QjuIZGvZNgFCMZKRPmtkQ7tBwHRKqYZBhJdbHdXCNZ83qrz7FGomfmaA+AYL1eYat6rtvsZ9PDmucQPVIRIVCzBu3497/6Tl+/1eTT/+mf/3tDOSQ2AJwX1IC0THBLFzDURQi3xxrb6i3RmR4VZVJsolNEqoiixMA22XYd3DtDYFeN0G0RyBZQF8/EyvisGphQAaQ3Ojy/60fJix/wkwBjoWHSWeCmRG4iJnp3IxZG3wOXOeJKCcZmB2zR09GPSc3RN4LeHYyYcQqHZu/R5hnwOSCpcaQxpgNW54kvOY5xvWRHDBLY0RYuhTXZPrS1Ow7ZKlEYyPlj3GR9glEiFtUhkTAtBWHsKiUS1rsWLqw50E9SXU2B1/X60znc7SKOt911ihKO0zXxcqA1iAEC92MHd1JJiE5pW3c4GuCR+D4g0tsRwdL3NEJ3So3DQX4klk78wOyN0k0DN40cMCpg3GWj91eHmpg+jXi8AW7JnLfQXi1Q1LbUdo+8jpnidqWf1Y/HHGNwvdZ4/7BSN+PVEtg57ipEzCAyVjc4kgI8M0VAPD85H8o84kA+j9hgh7m7aw9HsD5G3vuhT0VEBjFP+A8g3cThItRSDSdk7zCHETmFw3mAuThATGg5TNfASWe7ftUg1jZc58ABO4DzZRj+MR67e9N+N2bGcl+ijTz/dv/xE9q5utFRFDmKAhdI1vepNKdzuMpznSc8KkUUGTEZeGKHOSagcy+wYOLNLdYEm42OsbLQ8fjokp0CaSf27z6A6xNlHK1zsJ7q8HwV4yTGbINFLV93I55VxaMSbXec8dh2rayKXX1F+L4whUtqudIxtcFcWeL5S4ydAGtLt9a4FtYam0I4v9dEWwPirPvdX13v+B/AedW1HNM4bqJXlo54OfoOHcvZB3m8zwPdHaPIfF37YJzwWfV+ypX2x4+V7UiaTCaTyWQymUwmk+kg2RdJk8lkMplMJpPJZDIdpIPZycctbgck4PpOUZwvn6nz6n/45//RUL7/6YehfHene8xnCzg2Yhs4reAwCQSK1FkNZIpujBEQt6xHXjs4UFbYpmbS84hJg4FLEb2o4ITXYVuZDkhVodvmK6CtDyUQpJgIm36fz0VRpkWG5NVPKOedxPWuXhJklCVC9LDW7e2//8ProXwKnODrM63nlHjqWq+z3io2UCAReAqMaVNpff3hO3WLFWA098Xubz3QrTlcgWdwuE0mQFVxLyFQAbKafNnBLZjocgenrFGiXuQ2bx6A6wCLnSXA2Z5QURjK6fnOKczDfTeI9mO7ji6CdCgugDgk+9FHuvu1TFoMdKQpdPwyGTpdWV0/mEKg8VFNB0rgY7iBuzeKxqcTxfTyxdlQTqb6OlE6iYGVATGTrZabUvsgXTYTICgjHvsJNZ1M5J/+xX+8+2yMwWsgJr//4aehvAYjT4JleaeOoPWlPk8KB0AZYc/EBPHMjEcT7bux0/p1vfVhAqvYBmhTu2FsBq5D5zo8q+s4FREfI+Ko7/Bb2nriOsRoR7QuXWGP5y4Y94iZI97WEoEDMtjR0RvPWejflgLsSohO6fjxDfFrYL6Yq1bN7VAubrS8+OwzERGJgCPeXL0Zyvc37/TeibHx+ALmTboLt2iXFogysfos1xie4JhFxT7OpPHt/r7/1Hp8jqYmHqz9pkLfXa4UUwyF8432OV7nNNPnD9EHqkLb2gEBz+EG2W20vra8t2H9o39XYn2SAG+jW7MrdX3i4MZN906i4yHWOXT19DzagonTIa6Ojr9gjdT447CtbdfKbY9jB1hXnc+QvJ5sJuqLc6Ub2Xdi7ciQxUcYOZliDOBYTrnR4OSBqUePLqBci7r9x1Do7sxYPspEgOdrcdwBpx2kGWHyeA4+N+KZ48O6bv/bn1Cd72Rb7ua2PIfrOtaNHuvpGv3v5kbH1EkAZ2w6iaMyHHDWAOO3HnUT/Y87oP+bNdYZye5zC8SGCmvtBBhsjHvP2TdHWQn2u5GzzluMzQ5/W9FNXbAWAOpcIt6+e3crh8p2JE0mk8lkMplMJpPJdJDsi6TJZDKZTCaTyWQymQ7Sn2AL2qOtxOSAL/zq7/9uKD//XBPsTha6ZdxeAcEi4gAkr4h0qzWB+14BVGCD7VhuN3cLuD0mO6QhBNpKt9fgA9iChOQa+RagAviHEtjCCL9p9mALIvL8+XwoV8ASTl+pi93FCRI0P6GciEQ9YhaM+EXd6s5PtL2WS0WX//Xf/W4o11/rvf7ipZY7OGuVQJroSAXKRW7WigfcPyhqQ0yo6LfnmWt2BdfDczTXBXBOiYDYbZlQGq57DogEEkc3aMcGDoElEgg3QOwqoD5JCAc+OL4+peI0llc/eyUiIr4AKlPr+GKO4BKJb/n8zPKdtsQKgTFhfBEpoSsaebcIbrkBXRcfPxaOsFGu2JHjOMJ43DCBM+o8AuoSboF/AKcN2NlaxB5g0jcP2gcb4JFZBmfRI7F0cRLLi6++EJGxA2b809uh/JvffT+Ut4i3KVDgN/eK0XwDpD6OdSw7z9hE90a9H7r70Qo2XACd6VFIuu8yTtbAKh3w4NDh9ZrHB4BIob0cYykw+QC4XY2Eyw3x4xhIHlDnQI6D0u3cPh9dWxFrIqJIwJVGmcDxH5hjmIianojBiNIH4gYELZroGCBKF6Ku681uPPz2hz8Mr333m18N5Rkc0NNM+xEpNsaGGn2zRMzcrjEPEG1NdeyPpmIhnsf64ycfiW313eAOTrfKYqt9a4u+e3d7PZR53IaNFCNOxuf6zDMgbjdv3g/li+efDeUORziaFGjlFJ2gd7ltcFTHYy7lMZwSzHeKdVwHx/IQEy3dOBvgkWVFbFWvSedgOut2QCh5vKhojnNkoKpbef32RkRE6lLXUjkQXo7NljEQKOFoLDN0ANMflSGi3uOYiHiEz9KhTCd1YK7RfndcztUdnM9bjJd7rOMesF4o6v1tFDJDAq1FR4agOEbj+Kank/cizWNcpXspjzqgCy2Bi79b6nriHPEwTYDaZzhmw2MIeP4EcapBfT3gOEmUwKW67WMlrhGF/HyUEVc7nB9gW7APcjwS+2/Q17gWbUdrPa2/DbJLLN9pDPvbf/gHOVS2I2kymUwmk8lkMplMpoNkXyRNJpPJZDKZTCaTyXSQDkdb+6+eaa7bsbNzxQa+/+2vh/JypU6LRLZyJN5lQs0W27QBtv4bOGw2hW79pthu3mwVTbt6ULfDyWK3tb1BcuaCGCz2xJkANIN7k8P2dAdUoIBrWgUcq2r2oHwiEgJLODt5rq/P9PrTM8VYgvg4NlheRLoeYWxR/xmcV0E4Sgx8bg0U5C0SVJ/O1W1qiveUSPTu8LvF6emJfhZcee8LbY8CKGbYO5/SnXWWExWA2ycQgoYYBu8FGEAI/rMBn0eEoQXG0+A6BTCekkgPE7LDjfAp1XVe1qvdfTkP3Hal2IqjQ/GJtpFHAzs6rzG5O8bDZqUYRFujUzMxN1zJatRddAY31d5tkgm9PcY3e/z2WlHNoNOx02Hcbe7gMDYHltICV8F9hUCtN3faf1sgeXGMROJo9w8lZf7/q6Zt5fZ+hx0RA14uNaa1iC90vSyAsNwhxl3d3w3lbIoEynDro0NuBEyIiaiXN4rbbYGvPbqpOlobY0zFmbZ5CnSIfGbb4XqofyJVHmicQ7u36O9djWkso0Mwkz4Dr62PE1fFuQEdovuhQ3vR8dDzOYmptcTU4EiL9wj6tIOba5DQ2VU/az7TeBvAIXe12c2VxJwncD8m2l3TpRMhoHFAqhBLGozmgo6FuR7roHs3XXzpQtmOXideLUdR27ayfETdmSAcy6Xbe40db+EoPT3Rer5/0NjkMHZid6GvL9Tl/vpGMbkQOHjwCyCho9iHpPY9zhie6rolncPtHvGYcbUYuY1qhSYp/mHkWI61WMX5EYgd8N5R8nT0h4YYaXmc8dj6Th76LPSTRutlhViTJftd2mPEKWLkfFPAeYtO7qM/wBEOjKUJPrcGZtr0cZ5odwU3dB4rCRFXmFy+xnjcFNqn3rzVWL7c4ohHzSNicPGncyyeyDEM0c31SGE1cE7SOO3L+nrIfTCcZmrhen2LozJvMJ/Oppd6HRw9cqjrCMgp2y7zmk2BqDHR6DTZvSdGNom64pzFIw6oZ7Rpx6NVDY+k0MWaztj7x2mHOafA950Njju8fqfu3L/64UYOle1Imkwmk8lkMplMJpPpINkXSZPJZDKZTCaTyWQyHaSD0VbfJ7ps6GQFzHVxpgnCt3RCHCE6er2K7mz4h1HSdyQOL7ElzAS3WabIYwAs4fp6t03bAYltuGUPrKJBUveyIYqj166Itpa4JraSK9Je2FZeYSv5BmjMi3Ots2AKl7zgOO6CXdfJqk/wSmy3xLb3dguEYqbb+vlEt/UrOMgtgdVJzftW5iDOgAjjHflc+8/Pss/1foB81P0WfoZkzkxuniBp7wgtQbEG1hhU+re1sE9pHVQtE9kD3QFyUMNBiyhuAezHB8dxMyvLSn737WsREQnhuBuhXlbAXM8wTiPQtusl8C0kjy/gTrve6nUmsY6HHBgcsRuYg0kGrGt1t0M4I8SP9YNimMv3GjNu7xSxOFso8j090z7o0e6bld5virb2IZJ+d3Bq3Sp6VsFNNAMmXaM/uCMlzm7qWt79uEsCXxTaz97fKjJXYnylwGzolFeisy8L7Q+bpaJsNDQOGyCRiL2TE30TsecNcNlgsutL8xNtlzRF/8LRADKITalIFa32GiDtdEWmc7NDG9G9GYTuCOcMc9RTjjEeIVY9oZwTCR6xaFqyIh45DAw6LSIEjTPGj9wSUReyz+lRxHm0aaoVk6TAsTDnZn1cPQP2Pok0Bm8wJ9ZrIPN0mix1LkOOdCk7bWu6LV6cKto5nSnmyjUCkd6O+Bbn/yOZtjZNLe/e/ygiIjlR6Vbb5RpOrQ8P+8dXizng4VYdM2c4QlLBtf4Ec2uNeLv88Sd9HSEo9hqHT6c7RLZL0WHowBnqZ5ZT/UzG3hoxpoab7MgdFIhsQ04dRyjYMB3xbfR3jlMXs/M/nbz3UvX9hfNg0ezHCslvEjX3o36JeR1zzHgpjbUIqiWKcfwGYzBE7C16tLLEmrdmbMSaiPz8fKZxmG7J90tt35sH7YMbXJ+uyGw7HjWL6JaKe+cafMTzP6WciOvXUDEcd+Mcg+Fhv3v+w1Jj0A9wJb68UAQ9wmTiG7RdSYdYHOHhETwE35D9vl8LNQ0cdNEudOql0/WoD47cWfX1UYYKXHMUP/Xt41gKjDnHsYIg1Pl6Wx6+zrEdSZPJZDKZTCaTyWQyHST7ImkymUwmk8lkMplMpoN0INrqh21+T8wLW9p0Jn0AUtUBm+BWvgdy0QIBarE5u0LizKaiZRQS/sLlbJoo8pH0jxjMgJMAOVku1eGV97Vd62cy2Ttd8ZgwnXQA0aQAmb63JRC/SuvmolO8J4Q7ZQxX0qeVG9CoWuBGikTvrqXjn257Z3BjbGtFmu5QRxlwx2DkeKv1ckdnXWyxhw7tCISy/n/bO5feNq4zDJ+58yrKtJTYLtIgMIqgaNFF63/aH9JVN/0N6a7rAIlTJ7EupCSSw7meLjji9wwqNyIqIoDwPquBw5Azc66jec57OnWzhgbg4VExuJGpom0MJbVXSNA5qcL2NA/+88P6B9NEfQUtBH4zUxifEu8C13TtkJvUJ1RpoapWvAa0uwXSS7eF6S93SDDz0D+m0LfOSmsP5zNLbw5hRlE5/XizU8Za6ky4PwXq1MmZ6SeTM9PheO7Ux8IU9x+bbofwBnNoTd9f229t0K8wiW2EBNeyOo6C1TStu+0SkC+uTO29W1sf8SltaDI2HW42sfYSo03FSLd20PO26JO53IBq72Bgx7NTS5iM7n8L9yocM3EOSZso9IeMlgAAD5xJREFUoyCCngnlxscoR14rNPKImb7Qt1leEfretoZS2tPwcPyU+MASoKGaf0qJDntmK+4FFVZeJyKo+7o8Po/+bhgxLRmnWdnno07VC5HMGaIP5JkPEqtTOZRMtruql4Zt9eEEKd2v3rzeH8dIrywKbmTPFFssIYG21obHUenqqnbXFzt19eyl1fkayz0WH027r1C/i7WNaw10zwI63NXG+p2Cm7ijX30dQU+EOhywC6K63N3HFr8ZMiwZCckZlgE1VJcr0wC3pfVDIZRMpkT6lm2Z+jbqLNs4Tp2aK/vzpyVwYbf0gUui8hXmPKd2Hg00xaZXtdBe0FEGrIv9XNP9UYi+icsEOM/gsoK0W3PSoH1X6AMLzNEapsYGVu84nl5jnkXlk86t751Wz6V/8JBzIaY3B/5Iaqt3+7RvjyVJQ9T/7RBtzabuboklZG1tDeJqaXVgnpkWXFIVhRbLep9hXlpzDMWwkma7ex311pIwSRXzz4CqO+arnkuFMK4xCZkp6FSO8Y6wxm4Cca9M8fyEdNkYS/kei95ICiGEEEIIIYQ4CD1ICiGEEEIIIYQ4iMNTW+9fa3Pzaag7pyfz/fHyxPSPyx9N0wrwWnccUpvA69jePtfQI/HvTJLK8Ho4hVITd6+WqZjWsb0CHuE1btEwYQlKBl7ZM4WJr/iZ3un9w7qEvWB2LkY6awoV1EMr8/44ykcQBS4Z7657W0KhgP4yGUFTxGvvGptZN9j0fYPvuY3NLRjiXiyxIeztLT6TQZdF2mQITSfqNNYG7/JherlhZi5lirTGyejh1NgBdJoQAZOul9Bm9ZHKLXWzEtpAhKQ7D2V408C1eEKiKHLj6U6LjjdWn4qttbUcKkyBdhRC/x6Poe7AZdtsuXmtNbzrrekizQTtZAvViom6CTY475RLT20ZMctMCHY4x5u13c+Lm+/3xyOU43xsn5+dQz+BzpFD2b5GeGeONLMosnPIxqhXR9rI3ju/7z82W6sr6IJcBE+N6W/U6yv0qzlONUTyZDiCnnhr6s71e0uIHeLPi2cTKMIv0fme7voHP4Lqv4bmimTZmueL6ys2pl15pOkOh6hHUKpDJD0mKKMWfX9PTYcHWGFj7nyD5NgnxbvmPokW4wE13BB9ClNbaZRREaMy14bU4fCzPY0WCac1rnlh6Y2LC0scXS13ZbAq7L8XTGeNoO9hk/oWyaNM343RHlMo1RxnR0wnpR6IOQWTMvt9MsfW4/wdvG1bt77b1dMhxmMM/a72D6v5DZKrK/QXG2i7zQIaYs85tut8vbHjSWKFneD+bmvrh+vL3WeiF9Dbsb4gxnfUUFI9tNUQ6jJ19BobtjPJk8RU71iXe0tb2H8yzfXBr/y/iaPQzU92da1CSulqDYW3sGVFA6iSDco3pCbYUmdlsqv9rkfHjVvnWg6uqDNUIqtObaTaXWyQEg+9uoVSXjAdl+eFc/c157T4fd5/nC+XCbCQeguEeqsEjpNOHwSBi7olFsEIaadYHjPGUo4cuukKc8gl7t0llmJ9icvsLY2Airq8wDKTFrrwnY1nCZeldf1GhrHXYfzyUNfTAeeomF+jPwgHGAc/UWAs694wiD6G/SqXr+Vo7+OhtYnHojeSQgghhBBCCCEOQg+SQgghhBBCCCEO4jB30pt9EeCVKl+dToamsb16/WZ/fHtjaZAF0ghX0D+GeE2eQZfN8J0ZvCu+5s8G5ieOpqZlBp2uwVSxxpkSkuMV9wapqilSYAMokQUS/7Yt0r8q6mamIiyhtGyhAGVMNuUm2kwFiw7fGPQx+Na5utidb1naNbdIB2sqbIDuoA1AAcygna1L+/wUisMACm+QMlEOam9GFRkqAJJP63j3nXWB1/dIYlwhtTTqqX9WL2Yx6lEK1ZlGBjXqlvoNkgOhDXBL4grSRw394OYOmwg/IWEYucl0lypI/brIrX1t4Z5crUzJOP/M2sh4ZioDDF73mxO7d7BZ3fsfFvvjwavP7Tu/sI3MPbTCFgrp/YbtLOcqtS+P8O+RNwXuw4cPdo6VfWYSW7m8mNn5RtDqyhzadQG9GsmTown0apTvZGiJbmdf2bU+Jb71vTS+ewZQygu4Kk3NumjHTOW7Wplyc4n692Zi6ZnZxNpSnGBjcqhfdYak6UtTItv1rj8PqcGk6GPxJ0qEp/ayDQdQ0IczKHn4TAD9M4TOysS5APeGCXXFBn0720R5HAXLe+/KrmzCgBoZxkoscOgpR61/8Jh1McBSjhbjL8ee9RpJxLd2/TcLq/erC6SAJrvv9FBYC+jH10sr8xBpn5NzSzNNM6R0IoU1wZIFjndUdOtPLSFpebw/7C03aHqS3dPReufKbpznEoyIGbbQqQModiXGfi69oKJ8A7U6iPm3fGtLd0iRXqwwLm9suVBZYBlIp6yHP1vfyGTsEeZNDeYhTIodMP0YanqMxuwZNUw1mw2b3jUUSm7eTv31SOG7Lo4id3a6679v0E/mqN8LtIsplkYw/bam78mkZSbP4tpKrMta39hYGaCuD0fWHpjUf/8RLgfwcF9ZdgyfjqFERlwOgHnOFufFFNuGZUS1E/1TVfPzTOKFKhkc6b1UELqoWwrDtNOaS00yO843Nq5vcW0s0+9+tHL/7Qsb1+eh3ccXU2tLKZZOXXx/sT9Gt+lCTJ7um1K9Rj+RWYFVGAcaHCcpxmcsG6nxrMGlfq5lu/7EkgiMy57KNDciiGzu9PUf/rg//sff/+Yeg95ICiGEEEIIIYQ4CD1ICiGEEEIIIYQ4iIPUVu+9q7rosgjqCfUMJqm+RILr+dxeH19Ca1jlUCVDO+ar5GxmilsG3TOC5sJNcwep/b9F5yIEUDJSbBI6DqBUjZGYinSuVWkqSkmVDJuHcvPhuzu7jo8LU3r9FPrtyF5hM02U78oTqF9PifetK8ud3uFrO9ccrkSEZNstNIsS+oujBlGaLnLb2v2aJ6ZQTpDUeTY/3x8nSI9yUFcjvIbPO0W4gh6QQ/kYoF7EQ6vWVFijMd7lQwtpmWIH/YQKocP9KLGhdwvNoIArsELy4bZiXu/TEUahG53sdKgc2moYM2nQvNLljX1meWuqXwq1NYGaBivZTZDSWFzbtfXuO3Sd6ZtX++PR5MX+uAp296JBvYtiKOrQQ5b/hkpZmY43n9pnPofOOh1aXdvCK1vhty6W0Dah7hQNVX27pvH85f7491//yR2DtvUu77T6DKmMFTdHhr7IZExudt/AU1tCh/vhJ9NWhyMr69nUvmeIPnZ4au30ZGZ9+ADLB8JOQfYjbm6O82WQas2xwtrOaGLacMiRCP1qGHMDZWjyULm4VKIssGxiafV9663+VPVxlEjvnGvu6xHTKiO75sQ93NcwDLOBStegD4wwxnGphocbmGVWRsm5fT6DJj1EmqfvFMrNFv0H0pLboY1TGyhSPSOzlxKJf+0lrCIdvGLSIHRdpp/29F58Bt8TH2nph3PetZ2anCPtl2o1zNPeZt41U8Wx7qHAMg2WXbGGBon5z/tvTWGdv7V+NW2sTGG1Od+ph+MZEoIzJkba4SCy76swhha19esZpoZpjHbP+suE4MDOvUUCJPVtni/bx7HeZoSBc6Os+/aZXfN6aXOyy2s7Pp1aHxhF6JvQvriky6Ed0c5NMP+czVBnsGSAS2U85lFB900RNMUAvzOf2Ri3wf0fjK2MOG9pkcS/QvJ6xf6mZ/p/IvmTqa0cK6nkH2eTAeed36vm0RbzrQS/jaU3mw3mjWioTGP/6cr6u399935//JfJ2/3xGJ3yyRTLfz5D2SGQP0OdDrqlWxnqQooxNsZuAgH6Vc67a4+E3qJ58Jhack3tHANqjcIuUdg5+t7T89f747e/+/P++K/uceiNpBBCCCGEEEKIg9CDpBBCCCGEEEKIgzjoZXTbtm7bpcKNR/bqP4baQY2PGgqVG99LxmQKE9KWNvbqd5tRn7PXwyk2+2QK2F3Jzad35xNCugmgOyYJ1EdsSMs01yiy34nx7B3Dj8xre8d9t7bjNTZ1P52f7Y8zqBNBY/cmhtrnwyOltnrvmu5eB9BAJyn0DyRaYn97F74w1YmJVVRFG2oTtV3DgIl+SIULsPnytjYVs+bmu91HPDaJzaDujKhCQyeIkRTbcONuuCirEkmHN0g0RJ0acKPYlimJdo6LhdWZ2zu7jtYdpxyDIHBJp90k0N64+fcKyustjq+uTOmZon6/mFodGKF3yOZINYW6fYuNzvMfPu6P4xU2cT5Bn3CvaxfWRuPU7mF+Zff/52tLah0hNW54YnpPBP27QbteQ7W+urWEtp8vLUWvRsIiVeQEKtEQ9aptmdH7hASmgAUhtKSS6iP6SVTMNRIgQ2guMRSsCb5zCdV+CL0+nKB/Zp+MDdkd2pXr7jVT/sKB6T8pdMSipTJvXxFChY7hvbUj3OeQKadQz6DZUivbXlv9WW2QpAi9h4l5T0nbtm6d7+pRArUoQ9usYzuPGCmH3Ey6/9ddaFRQtJue14i+CZ+nkhePkYa8QpptN1bCmHQVdLUQSmAKvX2IZSDcdZ2pgBz/A/ffyZTOORcjHZ0b3zumPjMZEvc1iY/Xr8bd2BtFdg8rnHi5xUVATRvj/O4wF9psbTyIcD3cAH2xsrZZ1d/uj89n9vk55lFMLb9vJumdtZ0qR3kN0NamNj6MkYi/ba2OBBj/mazLW85yjNBXhWjvLdV71KsW5RsEx4ltbZrWLTq9nb/B8XGTW7l8vLCxLEntXsxC6PshtXP7f3sp9JjDZUiCZT9QFJgbJ1DNuzbroUJXBcuCm8vvDxli7W4RsX69sMkbr7XlfDjgEjH0w/XD5ZVgiQGX/Dhc31PivXdVN1+N8cji0advciytWts8p/Sc62Ncw24FH360+vr2i8/2x7CF3Rjj4PgUc/cx7h3627DrK7Oh/X8plnJwlwmmWLeoU3WF70PfX6Ff4bjWoHEmaFI15jZLaOchUvmnY5sLUId+LHojKYQQQgghhBDiIPQgKYQQQgghhBDiIAImq/0S79698998880RT0d8iiAI/um9f/cU36Vy/PVQOT4PVI7PA5Xj80Dl+DxQOT4PVI7Pg8eWo95ICiGEEEIIIYQ4iIPeSAZBcOGc++54pyP+B196789/+WO/jMrxV0Xl+DxQOT4PVI7PA5Xj80Dl+DxQOT4PHlWOBz1ICiGEEEIIIYQQUluFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhyEHiSFEEIIIYQQQhzEfwDpieMKh2wu4gAAAABJRU5ErkJggg==
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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>
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<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> (Write your answer in this cell)</p>
<p>The generated face pictures look like all have low low quality, like they were taken by a camara from 30 years ago.</p>
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<li><p>Unfortuanately, the generated face does not have darker skin faces, so it is less real in a way bacause the data is biased.</p>
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<li><p>Model size matters, in general deeper nets have better ability to dicsover more feautures. If I add one more layer to the model, the generator may be could generate more clear faces, like more details of eyes, etc</p>
</li>
<li><p>Optimizer, we could test out different beta2 and learning rate to find relatively the best combo for this model. As for number of epochs, when I ran 10 epoch and get a little bit blurry faces comapred to epoch=50. So increase number of epoch may help, but before that I think frist should try tune the hyperparameters then move to model size adjustment for this case</p>
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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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