kaggle_letters.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="Multi-Label-Classification-of-Handwritten-Letters">Multi-Label Classification of Handwritten Letters<a class="anchor-link" href="#Multi-Label-Classification-of-Handwritten-Letters">&#182;</a></h1><h2 id="Step-0.-Style-and-Libraries">Step 0. Style and Libraries<a class="anchor-link" href="#Step-0.-Style-and-Libraries">&#182;</a></h2><p>Let's choose a style of the Jupyter notebook and import the software libraries. The command hide_code will hide the code cells.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">html</span>
&lt;style&gt;        
@import url(&#39;https://fonts.googleapis.com/css?family=Orbitron|Roboto&#39;);
body {background-color: gainsboro;} 
a {color: #818286; font-family: &#39;Roboto&#39;;} 
h1 {color: #37c9e1; font-family: &#39;Orbitron&#39;; text-shadow: 4px 4px 4px #ccc;} 
h2, h3 {color: slategray; font-family: &#39;Orbitron&#39;; text-shadow: 4px 4px 4px #ccc;}
h4 {color: #818286; font-family: &#39;Roboto&#39;;}
span {text-shadow: 4px 4px 4px #ccc;}
div.output_prompt, div.output_area pre {color: slategray;}
div.input_prompt, div.output_subarea {color: #37c9e1;}      
div.output_stderr pre {background-color: gainsboro;}  
div.output_stderr {background-color: slategrey;} 
&lt;/style&gt;
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<style>        
@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');
body {background-color: gainsboro;} 
a {color: #818286; font-family: 'Roboto';} 
h1 {color: #37c9e1; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #ccc;} 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> 
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>

<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">h5py</span>
<span class="kn">import</span> <span class="nn">cv2</span>

<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">cm</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">GradientBoostingClassifier</span><span class="p">,</span> <span class="n">RandomForestClassifier</span>

<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">to_categorical</span>
<span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span> <span class="k">as</span> <span class="n">keras_image</span>
<span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>
<span class="kn">from</span> <span class="nn">keras.preprocessing.image</span> <span class="k">import</span> <span class="n">ImageDataGenerator</span>

<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">load_model</span><span class="p">,</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Input</span><span class="p">,</span> <span class="n">BatchNormalization</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">LSTM</span><span class="p">,</span> <span class="n">GlobalAveragePooling1D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">BatchNormalization</span>
<span class="kn">from</span> <span class="nn">keras.layers.advanced_activations</span> <span class="k">import</span> <span class="n">PReLU</span><span class="p">,</span> <span class="n">LeakyReLU</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalMaxPooling2D</span>

<span class="kn">from</span> <span class="nn">keras.applications.inception_v3</span> <span class="k">import</span> <span class="n">InceptionV3</span><span class="p">,</span> <span class="n">preprocess_input</span>
<span class="kn">import</span> <span class="nn">scipy</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">misc</span>
</pre></div>

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<pre>Using TensorFlow backend.
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot the neural network fitting history</span>
<span class="k">def</span> <span class="nf">history_plot</span><span class="p">(</span><span class="n">fit_history</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
    
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</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">fit_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">:],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;slategray&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;train&#39;</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">fit_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">:],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#4876ff&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;valid&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</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="s1">&#39;Loss Function&#39;</span><span class="p">);</span>  
    
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</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">fit_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;acc&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">:],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;slategray&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;train&#39;</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">fit_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_acc&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">:],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#4876ff&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;valid&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Accuracy&quot;</span><span class="p">)</span>    
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</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="s1">&#39;Accuracy&#39;</span><span class="p">);</span>
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<h2 id="Step-1.-Load-and-Explore-the-Data">Step 1. Load and Explore the Data<a class="anchor-link" href="#Step-1.-Load-and-Explore-the-Data">&#182;</a></h2><p>For this project, I have created the dataset of 5940 (180x33) color images (32x32x3) with 33 handwritten letters.
Run the following cell to download the dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Function for processing an image</span>
<span class="k">def</span> <span class="nf">image_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">folder_path</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">keras_image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">folder_path</span> <span class="o">+</span> <span class="n">img_path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">keras_image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Function for creating the data tensor</span>
<span class="k">def</span> <span class="nf">data_to_tensor</span><span class="p">(</span><span class="n">img_paths</span><span class="p">,</span> <span class="n">folder_path</span><span class="p">):</span>
    <span class="n">list_of_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">image_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">folder_path</span><span class="p">)</span> <span class="k">for</span> <span class="n">img_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">img_paths</span><span class="p">)]</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">list_of_tensors</span><span class="p">)</span>
<span class="n">ImageFile</span><span class="o">.</span><span class="n">LOAD_TRUNCATED_IMAGES</span> <span class="o">=</span> <span class="kc">True</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the dataset #1</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;/Users/olgabelitskaya/deep_learning_projects/DL_PP2/data/letters.csv&quot;</span><span class="p">)</span>
<span class="n">files</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
<span class="n">letters</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">]</span>
<span class="n">backgrounds</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;background&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">tensors</span> <span class="o">=</span> <span class="n">data_to_tensor</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="s2">&quot;/Users/olgabelitskaya/deep_learning_projects/DL_PP2/data/&quot;</span><span class="p">);</span>
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<pre>100%|██████████| 1650/1650 [01:04&lt;00:00, 25.53it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the dataset #2</span>
<span class="n">data2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;/Users/olgabelitskaya/deep_learning_projects/DL_PP2/data2/letters2.csv&quot;</span><span class="p">)</span>
<span class="n">files2</span> <span class="o">=</span> <span class="n">data2</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
<span class="n">letters2</span> <span class="o">=</span> <span class="n">data2</span><span class="p">[</span><span class="s1">&#39;letter&#39;</span><span class="p">]</span>
<span class="n">backgrounds2</span> <span class="o">=</span> <span class="n">data2</span><span class="p">[</span><span class="s1">&#39;background&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">targets2</span> <span class="o">=</span> <span class="n">data2</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">tensors2</span> <span class="o">=</span> <span class="n">data_to_tensor</span><span class="p">(</span><span class="n">files2</span><span class="p">,</span> <span class="s2">&quot;/Users/olgabelitskaya/deep_learning_projects/DL_PP2/data2/&quot;</span><span class="p">);</span>
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<pre>100%|██████████| 5940/5940 [06:48&lt;00:00, 14.55it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the shape </span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Tensor shape:&#39;</span><span class="p">,</span> <span class="n">tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Target shape&#39;</span><span class="p">,</span> <span class="n">targets</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Tensor #2 shape:&#39;</span><span class="p">,</span> <span class="n">tensors2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Target #2 shape&#39;</span><span class="p">,</span> <span class="n">targets2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Tensor shape: (1650, 32, 32, 3)
Target shape (1650,)
Tensor #2 shape: (5940, 32, 32, 3)
Target #2 shape (5940,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Concatenate arrays</span>
<span class="n">tensors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tensors</span><span class="p">,</span> <span class="n">tensors2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">targets</span><span class="p">,</span> <span class="n">targets2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">backgrounds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">backgrounds</span><span class="p">,</span> <span class="n">backgrounds2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Tensor shape:&#39;</span><span class="p">,</span> <span class="n">tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Target shape&#39;</span><span class="p">,</span> <span class="n">targets</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Background shape:&#39;</span><span class="p">,</span> <span class="n">backgrounds</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Tensor shape: (7590, 32, 32, 3)
Target shape (7590,)
Background shape: (7590,)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Concatenate series</span>
<span class="n">letters</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">letters</span><span class="p">,</span> <span class="n">letters2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">len</span><span class="p">(</span><span class="n">letters</span><span class="p">)</span>
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<pre>7590</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Read from files and display images using OpenCV</span>
<span class="k">def</span> <span class="nf">display_images</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">&quot;/Users/olgabelitskaya/deep_learning_projects/DL_PP2/data/&quot;</span> <span class="o">+</span> <span class="n">img_path</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">))</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">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</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="mi">6</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">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">title</span><span class="o">=</span><span class="n">letters</span><span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span>
    <span class="n">display_images</span><span class="p">(</span><span class="n">files</span><span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="mi">100</span><span class="p">],</span> <span class="n">ax</span><span class="p">)</span>
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<h2 id="Step-2.-Save-and-Load-the-Data">Step 2. Save and Load the Data<a class="anchor-link" href="#Step-2.-Save-and-Load-the-Data">&#182;</a></h2><p>The data tensors can be saved in the appropriate format of files .h5.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create the tensor file</span>
<span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;LetterColorImages.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;images&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tensors</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;labels&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">targets</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="s1">&#39;backgrounds&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">backgrounds</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
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<span class="n">f</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="s1">&#39;LetterColorImages.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>

<span class="c1"># List all groups</span>
<span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">keys</span>
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<pre>[&#39;backgrounds&#39;, &#39;images&#39;, &#39;labels&#39;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create tensors and targets</span>
<span class="n">tensors</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">f</span><span class="p">[</span><span class="n">keys</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
<span class="n">targets</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">f</span><span class="p">[</span><span class="n">keys</span><span class="p">[</span><span class="mi">2</span><span class="p">]])</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Tensor shape:&#39;</span><span class="p">,</span> <span class="n">tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Target shape&#39;</span><span class="p">,</span> <span class="n">targets</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Tensor shape: (7590, 32, 32, 3)
Target shape (7590,)
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<h2 id="Step-3.-Implement-Preprocess-Functions">Step 3. Implement Preprocess Functions<a class="anchor-link" href="#Step-3.-Implement-Preprocess-Functions">&#182;</a></h2><h3 id="Normalize-and-Gray-Scale">Normalize and Gray Scale<a class="anchor-link" href="#Normalize-and-Gray-Scale">&#182;</a></h3><p>In the cell below, normalize the image tensors, and return them as a normalized Numpy array.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Normalize the tensors</span>
<span class="n">tensors</span> <span class="o">=</span> <span class="n">tensors</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Label: &#39;</span><span class="p">,</span> <span class="n">letters</span><span class="p">[</span><span class="mi">2000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</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">tensors</span><span class="p">[</span><span class="mi">2000</span><span class="p">]);</span>
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<pre>Label:  б
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<p>Create tensors of grayscaled images and display their shape.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Grayscaled tensors</span>
<span class="n">gray_tensors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">tensors</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">])</span>
<span class="nb">print</span> <span class="p">(</span><span class="s1">&#39;Grayscaled Tensor shape:&#39;</span><span class="p">,</span> <span class="n">gray_tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Grayscaled Tensor shape: (7590, 32, 32)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Read and display a grayscaled tensor using Matplotlib</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Label: &#39;</span><span class="p">,</span> <span class="n">letters</span><span class="p">[</span><span class="mi">2000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</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">gray_tensors</span><span class="p">[</span><span class="mi">2000</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">bone</span><span class="p">);</span>
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<pre>Label:  б
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<h3 id="One-hot-Encode">One-hot Encode<a class="anchor-link" href="#One-hot-Encode">&#182;</a></h3><p>Now we'll implement the one-hot encoding function to_categorical.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the target unique values</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">targets</span><span class="p">))</span>
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<pre>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># One-hot encode the targets, started from the zero label</span>
<span class="n">cat_targets</span> <span class="o">=</span> <span class="n">to_categorical</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="mi">33</span><span class="p">)</span>
<span class="n">cat_targets</span><span class="o">.</span><span class="n">shape</span>
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<pre>(7590, 33)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># One-hot encode the background targets</span>
<span class="n">backgrounds</span> <span class="o">=</span> <span class="n">to_categorical</span><span class="p">(</span><span class="n">backgrounds</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">backgrounds</span><span class="o">.</span><span class="n">shape</span>
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<pre>(7590, 3)</pre>
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<h3 id="Add-Background">Add Background<a class="anchor-link" href="#Add-Background">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create multi-label targets</span>
<span class="n">back_targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">cat_targets</span><span class="p">,</span> <span class="n">backgrounds</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">back_targets</span><span class="o">.</span><span class="n">shape</span>
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<pre>(7590, 36)</pre>
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<h3 id="Split">Split<a class="anchor-link" href="#Split">&#182;</a></h3><h4 id="Color-Images">Color Images<a class="anchor-link" href="#Color-Images">&#182;</a></h4><p>Apply the function train_test_split and split the data into training and testing sets. Set up the size of the testing set - 20%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Split the data</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">cat_targets</span><span class="p">,</span> 
                                                    <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> 
                                                    <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
<span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">x_test</span><span class="p">[:</span><span class="n">n</span><span class="p">],</span> <span class="n">y_test</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span>
<span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">x_test</span><span class="p">[</span><span class="n">n</span><span class="p">:],</span> <span class="n">y_test</span><span class="p">[</span><span class="n">n</span><span class="p">:]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the shape</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_valid</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_valid</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Training tensor&#39;s shape: (6072, 32, 32, 3)
Training target&#39;s shape (6072, 33)
Validating tensor&#39;s shape: (759, 32, 32, 3)
Validating target&#39;s shape (759, 33)
Testing tensor&#39;s shape: (759, 32, 32, 3)
Testing target&#39;s shape (759, 33)
</pre>
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<h4 id="Grayscaled-Images">Grayscaled Images<a class="anchor-link" href="#Grayscaled-Images">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Split the grayscaled data</span>
<span class="n">x_train2</span><span class="p">,</span> <span class="n">x_test2</span><span class="p">,</span> <span class="n">y_train2</span><span class="p">,</span> <span class="n">y_test2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">gray_tensors</span><span class="p">,</span> <span class="n">cat_targets</span><span class="p">,</span> 
                                                        <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> 
                                                        <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x_valid2</span><span class="p">,</span> <span class="n">y_valid2</span> <span class="o">=</span> <span class="n">x_test2</span><span class="p">[:</span><span class="n">n</span><span class="p">],</span> <span class="n">y_test2</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span>
<span class="n">x_test2</span><span class="p">,</span> <span class="n">y_test2</span> <span class="o">=</span> <span class="n">x_test2</span><span class="p">[</span><span class="n">n</span><span class="p">:],</span> <span class="n">y_test2</span><span class="p">[</span><span class="n">n</span><span class="p">:]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Reshape the grayscaled data</span>
<span class="n">x_train2</span><span class="p">,</span> <span class="n">x_test2</span><span class="p">,</span> <span class="n">x_valid2</span> <span class="o">=</span> \
<span class="n">x_train2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">),</span> <span class="n">x_test2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">),</span> <span class="n">x_valid2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the shape</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_train2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_train2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_valid2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_valid2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_test2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_test2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Training grayscaled tensor&#39;s shape: (6072, 32, 32, 1)
Training grayscaled target&#39;s shape (6072, 33)
Validating grayscaled tensor&#39;s shape: (759, 32, 32, 1)
Validating grayscaled target&#39;s shape (759, 33)
Testing grayscaled tensor&#39;s shape: (759, 32, 32, 1)
Testing grayscaled target&#39;s shape (759, 33)
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Convert images from grayscaled to RGB</span>
<span class="n">x_train2_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">grayscale_to_rgb</span><span class="p">(</span><span class="n">x_train2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">x_test2_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">grayscale_to_rgb</span><span class="p">(</span><span class="n">x_test2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">x_valid2_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">grayscale_to_rgb</span><span class="p">(</span><span class="n">x_valid2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="c1"># Run tensorflow session</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>
<span class="k">with</span> <span class="n">sess</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
    <span class="n">x_train2_color</span> <span class="o">=</span> <span class="n">x_train2_tensor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">x_test2_color</span> <span class="o">=</span> <span class="n">x_test2_tensor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">x_valid2_color</span> <span class="o">=</span> <span class="n">x_valid2_tensor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># Check the shape    </span>
<span class="n">x_train2_color</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_test2_color</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">x_valid2_color</span><span class="o">.</span><span class="n">shape</span>
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<pre>((6072, 32, 32, 3), (759, 32, 32, 3), (759, 32, 32, 3))</pre>
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<h4 id="Multi-Label-Targets,-Color-Images">Multi-Label Targets, Color Images<a class="anchor-link" href="#Multi-Label-Targets,-Color-Images">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Split with multi-label targets</span>
<span class="n">x_train3</span><span class="p">,</span> <span class="n">x_test3</span><span class="p">,</span> <span class="n">y_train3</span><span class="p">,</span> <span class="n">y_test3</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">back_targets</span><span class="p">,</span> 
                                                        <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> 
                                                        <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x_valid3</span><span class="p">,</span> <span class="n">y_valid3</span> <span class="o">=</span> <span class="n">x_test3</span><span class="p">[:</span><span class="n">n</span><span class="p">],</span> <span class="n">y_test3</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span>
<span class="n">x_test3</span><span class="p">,</span> <span class="n">y_test3</span> <span class="o">=</span> <span class="n">x_test3</span><span class="p">[</span><span class="n">n</span><span class="p">:],</span> <span class="n">y_test3</span><span class="p">[</span><span class="n">n</span><span class="p">:]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the shape</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_train3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_train3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_valid3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_valid3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_test3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_test3</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Training tensor&#39;s shape: (6072, 32, 32, 3)
Training target&#39;s shape (6072, 36)
Validating tensor&#39;s shape: (759, 32, 32, 3)
Validating target&#39;s shape (759, 36)
Testing tensor&#39;s shape: (759, 32, 32, 3)
Testing target&#39;s shape (759, 36)
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a list of targets</span>
<span class="n">y_train3_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_train3</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_train3</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
<span class="n">y_test3_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_test3</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_valid3</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
<span class="n">y_valid3_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_valid3</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_valid3</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
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<h4 id="Multi-Label-Targets,-Grayscaled-Images">Multi-Label Targets, Grayscaled Images<a class="anchor-link" href="#Multi-Label-Targets,-Grayscaled-Images">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Split the grayscaled data</span>
<span class="n">x_train4</span><span class="p">,</span> <span class="n">x_test4</span><span class="p">,</span> <span class="n">y_train4</span><span class="p">,</span> <span class="n">y_test4</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">gray_tensors</span><span class="p">,</span> <span class="n">back_targets</span><span class="p">,</span> 
                                                        <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> 
                                                        <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x_valid4</span><span class="p">,</span> <span class="n">y_valid4</span> <span class="o">=</span> <span class="n">x_test4</span><span class="p">[:</span><span class="n">n</span><span class="p">],</span> <span class="n">y_test4</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span>
<span class="n">x_test4</span><span class="p">,</span> <span class="n">y_test4</span> <span class="o">=</span> <span class="n">x_test4</span><span class="p">[</span><span class="n">n</span><span class="p">:],</span> <span class="n">y_test4</span><span class="p">[</span><span class="n">n</span><span class="p">:]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Reshape the grayscaled data</span>
<span class="n">x_train4</span><span class="p">,</span> <span class="n">x_test4</span><span class="p">,</span> <span class="n">x_valid4</span> <span class="o">=</span> \
<span class="n">x_train4</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">),</span> <span class="n">x_test4</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">),</span> <span class="n">x_valid4</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</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">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print the shape</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_train4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Training grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_train4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_valid4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Validating grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_valid4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing grayscaled tensor&#39;s shape:&quot;</span><span class="p">,</span> <span class="n">x_test4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Testing grayscaled target&#39;s shape&quot;</span><span class="p">,</span> <span class="n">y_test4</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Training grayscaled tensor&#39;s shape: (6072, 32, 32, 1)
Training grayscaled target&#39;s shape (6072, 36)
Validating grayscaled tensor&#39;s shape: (759, 32, 32, 1)
Validating grayscaled target&#39;s shape (759, 36)
Testing grayscaled tensor&#39;s shape: (759, 32, 32, 1)
Testing grayscaled target&#39;s shape (759, 36)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a list of targets</span>
<span class="n">y_train4_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_train4</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_train4</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
<span class="n">y_test4_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_test4</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_test4</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
<span class="n">y_valid4_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_valid4</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">33</span><span class="p">],</span> <span class="n">y_valid4</span><span class="p">[:,</span> <span class="mi">33</span><span class="p">:]]</span>
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<h2 id="Step-4.-Create-a-One-Label-Classification-Model">Step 4. Create a One-Label Classification Model<a class="anchor-link" href="#Step-4.-Create-a-One-Label-Classification-Model">&#182;</a></h2><h3 id="Color-Images">Color Images<a class="anchor-link" href="#Color-Images">&#182;</a></h3><p>Define a model architecture and compile the model for color images.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
    
    <span class="c1"># Define a model architecture    </span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">196</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalMaxPooling2D</span><span class="p">())</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1024</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span> 
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>
    
    <span class="c1"># Compile the model</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">model</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.model.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Train the model</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> 
                    <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                    <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">),</span>
                    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">])</span>
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<pre>Train on 6072 samples, validate on 759 samples
Epoch 1/100
 - 38s - loss: 3.4973 - acc: 0.0316 - val_loss: 3.4868 - val_acc: 0.0382

Epoch 00001: val_loss improved from inf to 3.48679, saving model to weights.best.letters.model.hdf5
Epoch 2/100
 - 38s - loss: 3.4509 - acc: 0.0422 - val_loss: 3.4172 - val_acc: 0.0422

Epoch 00002: val_loss improved from 3.48679 to 3.41718, saving model to weights.best.letters.model.hdf5
Epoch 3/100
 - 37s - loss: 3.3819 - acc: 0.0527 - val_loss: 3.3260 - val_acc: 0.0606

Epoch 00003: val_loss improved from 3.41718 to 3.32604, saving model to weights.best.letters.model.hdf5
Epoch 4/100
 - 34s - loss: 3.3079 - acc: 0.0624 - val_loss: 3.2908 - val_acc: 0.0619

Epoch 00004: val_loss improved from 3.32604 to 3.29080, saving model to weights.best.letters.model.hdf5
Epoch 5/100
 - 34s - loss: 3.2961 - acc: 0.0730 - val_loss: 3.2676 - val_acc: 0.0711

Epoch 00005: val_loss improved from 3.29080 to 3.26759, saving model to weights.best.letters.model.hdf5
Epoch 6/100
 - 33s - loss: 3.2662 - acc: 0.0763 - val_loss: 3.2606 - val_acc: 0.0777

Epoch 00006: val_loss improved from 3.26759 to 3.26063, saving model to weights.best.letters.model.hdf5
Epoch 7/100
 - 33s - loss: 3.2497 - acc: 0.0855 - val_loss: 3.2764 - val_acc: 0.0738

Epoch 00007: val_loss did not improve from 3.26063
Epoch 8/100
 - 34s - loss: 3.2155 - acc: 0.0916 - val_loss: 3.2109 - val_acc: 0.1067

Epoch 00008: val_loss improved from 3.26063 to 3.21088, saving model to weights.best.letters.model.hdf5
Epoch 9/100
 - 33s - loss: 3.1948 - acc: 0.0978 - val_loss: 3.2647 - val_acc: 0.1028

Epoch 00009: val_loss did not improve from 3.21088
Epoch 10/100
 - 33s - loss: 3.1825 - acc: 0.1003 - val_loss: 3.2214 - val_acc: 0.0949

Epoch 00010: val_loss did not improve from 3.21088
Epoch 11/100
 - 33s - loss: 3.1897 - acc: 0.0957 - val_loss: 3.2365 - val_acc: 0.0988

Epoch 00011: val_loss did not improve from 3.21088
Epoch 12/100
 - 34s - loss: 3.1493 - acc: 0.1039 - val_loss: 3.1513 - val_acc: 0.0962

Epoch 00012: val_loss improved from 3.21088 to 3.15125, saving model to weights.best.letters.model.hdf5
Epoch 13/100
 - 35s - loss: 3.1274 - acc: 0.1123 - val_loss: 3.0985 - val_acc: 0.1291

Epoch 00013: val_loss improved from 3.15125 to 3.09848, saving model to weights.best.letters.model.hdf5
Epoch 14/100
 - 35s - loss: 3.1151 - acc: 0.1150 - val_loss: 3.1299 - val_acc: 0.1120

Epoch 00014: val_loss did not improve from 3.09848
Epoch 15/100
 - 36s - loss: 3.1071 - acc: 0.1126 - val_loss: 3.1246 - val_acc: 0.1054

Epoch 00015: val_loss did not improve from 3.09848
Epoch 16/100
 - 34s - loss: 3.0688 - acc: 0.1238 - val_loss: 3.1691 - val_acc: 0.0962

Epoch 00016: val_loss did not improve from 3.09848
Epoch 17/100
 - 34s - loss: 3.0839 - acc: 0.1257 - val_loss: 3.0155 - val_acc: 0.1528

Epoch 00017: val_loss improved from 3.09848 to 3.01547, saving model to weights.best.letters.model.hdf5
Epoch 18/100
 - 34s - loss: 3.0015 - acc: 0.1462 - val_loss: 2.9643 - val_acc: 0.1686

Epoch 00018: val_loss improved from 3.01547 to 2.96427, saving model to weights.best.letters.model.hdf5
Epoch 19/100
 - 34s - loss: 2.8948 - acc: 0.1683 - val_loss: 2.7906 - val_acc: 0.2200

Epoch 00019: val_loss improved from 2.96427 to 2.79056, saving model to weights.best.letters.model.hdf5
Epoch 20/100
 - 34s - loss: 2.7895 - acc: 0.1851 - val_loss: 2.6816 - val_acc: 0.2279

Epoch 00020: val_loss improved from 2.79056 to 2.68162, saving model to weights.best.letters.model.hdf5
Epoch 21/100
 - 34s - loss: 2.6777 - acc: 0.2126 - val_loss: 2.5743 - val_acc: 0.2490

Epoch 00021: val_loss improved from 2.68162 to 2.57427, saving model to weights.best.letters.model.hdf5
Epoch 22/100
 - 35s - loss: 2.5336 - acc: 0.2502 - val_loss: 2.5597 - val_acc: 0.2266

Epoch 00022: val_loss improved from 2.57427 to 2.55969, saving model to weights.best.letters.model.hdf5
Epoch 23/100
 - 35s - loss: 2.4566 - acc: 0.2721 - val_loss: 2.3549 - val_acc: 0.3149

Epoch 00023: val_loss improved from 2.55969 to 2.35489, saving model to weights.best.letters.model.hdf5
Epoch 24/100
 - 35s - loss: 2.3437 - acc: 0.2958 - val_loss: 2.2143 - val_acc: 0.3399

Epoch 00024: val_loss improved from 2.35489 to 2.21435, saving model to weights.best.letters.model.hdf5
Epoch 25/100
 - 34s - loss: 2.2546 - acc: 0.3300 - val_loss: 2.1471 - val_acc: 0.3729

Epoch 00025: val_loss improved from 2.21435 to 2.14705, saving model to weights.best.letters.model.hdf5
Epoch 26/100
 - 34s - loss: 2.1510 - acc: 0.3569 - val_loss: 2.0151 - val_acc: 0.4124

Epoch 00026: val_loss improved from 2.14705 to 2.01513, saving model to weights.best.letters.model.hdf5
Epoch 27/100
 - 34s - loss: 2.0167 - acc: 0.3821 - val_loss: 1.8508 - val_acc: 0.4401

Epoch 00027: val_loss improved from 2.01513 to 1.85081, saving model to weights.best.letters.model.hdf5
Epoch 28/100
 - 34s - loss: 1.9333 - acc: 0.4124 - val_loss: 1.7703 - val_acc: 0.4717

Epoch 00028: val_loss improved from 1.85081 to 1.77030, saving model to weights.best.letters.model.hdf5
Epoch 29/100
 - 34s - loss: 1.8554 - acc: 0.4379 - val_loss: 1.6112 - val_acc: 0.5283

Epoch 00029: val_loss improved from 1.77030 to 1.61120, saving model to weights.best.letters.model.hdf5
Epoch 30/100
 - 36s - loss: 1.7146 - acc: 0.4758 - val_loss: 1.5187 - val_acc: 0.5336

Epoch 00030: val_loss improved from 1.61120 to 1.51869, saving model to weights.best.letters.model.hdf5
Epoch 31/100
 - 37s - loss: 1.6544 - acc: 0.4987 - val_loss: 1.5573 - val_acc: 0.5534

Epoch 00031: val_loss did not improve from 1.51869
Epoch 32/100
 - 36s - loss: 1.5891 - acc: 0.5158 - val_loss: 1.4517 - val_acc: 0.5823

Epoch 00032: val_loss improved from 1.51869 to 1.45170, saving model to weights.best.letters.model.hdf5
Epoch 33/100
 - 35s - loss: 1.5000 - acc: 0.5575 - val_loss: 1.3247 - val_acc: 0.6100

Epoch 00033: val_loss improved from 1.45170 to 1.32475, saving model to weights.best.letters.model.hdf5
Epoch 34/100
 - 34s - loss: 1.4398 - acc: 0.5627 - val_loss: 1.2139 - val_acc: 0.6350

Epoch 00034: val_loss improved from 1.32475 to 1.21387, saving model to weights.best.letters.model.hdf5
Epoch 35/100
 - 37s - loss: 1.3479 - acc: 0.5825 - val_loss: 1.1607 - val_acc: 0.6469

Epoch 00035: val_loss improved from 1.21387 to 1.16067, saving model to weights.best.letters.model.hdf5
Epoch 36/100
 - 40s - loss: 1.2935 - acc: 0.6089 - val_loss: 1.0876 - val_acc: 0.6970

Epoch 00036: val_loss improved from 1.16067 to 1.08764, saving model to weights.best.letters.model.hdf5
Epoch 37/100
 - 37s - loss: 1.2851 - acc: 0.6070 - val_loss: 1.1692 - val_acc: 0.6456

Epoch 00037: val_loss did not improve from 1.08764
Epoch 38/100
 - 37s - loss: 1.2390 - acc: 0.6250 - val_loss: 1.0210 - val_acc: 0.7260

Epoch 00038: val_loss improved from 1.08764 to 1.02105, saving model to weights.best.letters.model.hdf5
Epoch 39/100
 - 44s - loss: 1.1529 - acc: 0.6466 - val_loss: 1.0160 - val_acc: 0.6825

Epoch 00039: val_loss improved from 1.02105 to 1.01598, saving model to weights.best.letters.model.hdf5
Epoch 40/100
 - 42s - loss: 1.1537 - acc: 0.6367 - val_loss: 0.9967 - val_acc: 0.7194

Epoch 00040: val_loss improved from 1.01598 to 0.99674, saving model to weights.best.letters.model.hdf5
Epoch 41/100
 - 40s - loss: 1.0798 - acc: 0.6675 - val_loss: 0.9167 - val_acc: 0.7260

Epoch 00041: val_loss improved from 0.99674 to 0.91669, saving model to weights.best.letters.model.hdf5
Epoch 42/100
 - 38s - loss: 1.0402 - acc: 0.6742 - val_loss: 0.9059 - val_acc: 0.7339

Epoch 00042: val_loss improved from 0.91669 to 0.90591, saving model to weights.best.letters.model.hdf5
Epoch 43/100
 - 40s - loss: 1.0257 - acc: 0.6899 - val_loss: 0.9519 - val_acc: 0.7075

Epoch 00043: val_loss did not improve from 0.90591
Epoch 44/100
 - 38s - loss: 1.0109 - acc: 0.6828 - val_loss: 0.8724 - val_acc: 0.7339

Epoch 00044: val_loss improved from 0.90591 to 0.87236, saving model to weights.best.letters.model.hdf5
Epoch 45/100
 - 36s - loss: 0.9869 - acc: 0.6961 - val_loss: 0.8719 - val_acc: 0.7563

Epoch 00045: val_loss improved from 0.87236 to 0.87188, saving model to weights.best.letters.model.hdf5
Epoch 46/100
 - 35s - loss: 0.9219 - acc: 0.7176 - val_loss: 0.8267 - val_acc: 0.7523

Epoch 00046: val_loss improved from 0.87188 to 0.82668, saving model to weights.best.letters.model.hdf5
Epoch 47/100
 - 34s - loss: 0.9078 - acc: 0.7144 - val_loss: 0.8031 - val_acc: 0.7391

Epoch 00047: val_loss improved from 0.82668 to 0.80308, saving model to weights.best.letters.model.hdf5
Epoch 48/100
 - 34s - loss: 0.8730 - acc: 0.7269 - val_loss: 0.7489 - val_acc: 0.7642

Epoch 00048: val_loss improved from 0.80308 to 0.74894, saving model to weights.best.letters.model.hdf5
Epoch 49/100
 - 36s - loss: 0.8384 - acc: 0.7363 - val_loss: 0.8309 - val_acc: 0.7549

Epoch 00049: val_loss did not improve from 0.74894
Epoch 50/100
 - 37s - loss: 0.8444 - acc: 0.7414 - val_loss: 0.7351 - val_acc: 0.7694

Epoch 00050: val_loss improved from 0.74894 to 0.73506, saving model to weights.best.letters.model.hdf5
Epoch 51/100
 - 38s - loss: 0.8051 - acc: 0.7469 - val_loss: 0.7322 - val_acc: 0.7668

Epoch 00051: val_loss improved from 0.73506 to 0.73222, saving model to weights.best.letters.model.hdf5
Epoch 52/100
 - 38s - loss: 0.7664 - acc: 0.7559 - val_loss: 0.6674 - val_acc: 0.7945

Epoch 00052: val_loss improved from 0.73222 to 0.66744, saving model to weights.best.letters.model.hdf5
Epoch 53/100
 - 40s - loss: 0.7755 - acc: 0.7484 - val_loss: 0.6965 - val_acc: 0.7879

Epoch 00053: val_loss did not improve from 0.66744
Epoch 54/100
 - 45s - loss: 0.7433 - acc: 0.7671 - val_loss: 0.6931 - val_acc: 0.7866

Epoch 00054: val_loss did not improve from 0.66744
Epoch 55/100
 - 40s - loss: 0.7171 - acc: 0.7795 - val_loss: 0.6655 - val_acc: 0.8076

Epoch 00055: val_loss improved from 0.66744 to 0.66552, saving model to weights.best.letters.model.hdf5
Epoch 56/100
 - 38s - loss: 0.7088 - acc: 0.7731 - val_loss: 0.6290 - val_acc: 0.8011

Epoch 00056: val_loss improved from 0.66552 to 0.62897, saving model to weights.best.letters.model.hdf5
Epoch 57/100
 - 34s - loss: 0.6980 - acc: 0.7790 - val_loss: 0.6384 - val_acc: 0.8024

Epoch 00057: val_loss did not improve from 0.62897
Epoch 58/100
 - 35s - loss: 0.6852 - acc: 0.7813 - val_loss: 0.8008 - val_acc: 0.7628

Epoch 00058: val_loss did not improve from 0.62897
Epoch 59/100
 - 39s - loss: 0.6602 - acc: 0.7907 - val_loss: 0.6373 - val_acc: 0.7879

Epoch 00059: val_loss did not improve from 0.62897
Epoch 60/100
 - 38s - loss: 0.6600 - acc: 0.7844 - val_loss: 0.6089 - val_acc: 0.8076

Epoch 00060: val_loss improved from 0.62897 to 0.60893, saving model to weights.best.letters.model.hdf5
Epoch 61/100
 - 36s - loss: 0.6506 - acc: 0.8007 - val_loss: 0.5756 - val_acc: 0.8221

Epoch 00061: val_loss improved from 0.60893 to 0.57563, saving model to weights.best.letters.model.hdf5
Epoch 62/100
 - 35s - loss: 0.6248 - acc: 0.8029 - val_loss: 0.5672 - val_acc: 0.8327

Epoch 00062: val_loss improved from 0.57563 to 0.56721, saving model to weights.best.letters.model.hdf5
Epoch 63/100
 - 35s - loss: 0.6050 - acc: 0.8075 - val_loss: 0.5830 - val_acc: 0.8142

Epoch 00063: val_loss did not improve from 0.56721
Epoch 64/100
 - 35s - loss: 0.5877 - acc: 0.8123 - val_loss: 0.5512 - val_acc: 0.8406

Epoch 00064: val_loss improved from 0.56721 to 0.55123, saving model to weights.best.letters.model.hdf5
Epoch 65/100
 - 38s - loss: 0.5528 - acc: 0.8205 - val_loss: 0.5574 - val_acc: 0.8445

Epoch 00065: val_loss did not improve from 0.55123
Epoch 66/100
 - 44s - loss: 0.5379 - acc: 0.8274 - val_loss: 0.5841 - val_acc: 0.8221

Epoch 00066: val_loss did not improve from 0.55123
Epoch 67/100
 - 39s - loss: 0.5426 - acc: 0.8226 - val_loss: 0.5329 - val_acc: 0.8340

Epoch 00067: val_loss improved from 0.55123 to 0.53289, saving model to weights.best.letters.model.hdf5
Epoch 68/100
 - 38s - loss: 0.5315 - acc: 0.8300 - val_loss: 0.5696 - val_acc: 0.8169

Epoch 00068: val_loss did not improve from 0.53289
Epoch 69/100
 - 43s - loss: 0.5072 - acc: 0.8363 - val_loss: 0.5511 - val_acc: 0.8248

Epoch 00069: val_loss did not improve from 0.53289
Epoch 70/100
 - 38s - loss: 0.5211 - acc: 0.8274 - val_loss: 0.5834 - val_acc: 0.8235

Epoch 00070: val_loss did not improve from 0.53289
Epoch 71/100
 - 38s - loss: 0.5358 - acc: 0.8304 - val_loss: 0.5398 - val_acc: 0.8287

Epoch 00071: val_loss did not improve from 0.53289
Epoch 72/100
 - 35s - loss: 0.4807 - acc: 0.8388 - val_loss: 0.5227 - val_acc: 0.8498

Epoch 00072: val_loss improved from 0.53289 to 0.52269, saving model to weights.best.letters.model.hdf5
Epoch 73/100
 - 34s - loss: 0.5305 - acc: 0.8302 - val_loss: 0.5642 - val_acc: 0.8432

Epoch 00073: val_loss did not improve from 0.52269
Epoch 74/100
 - 34s - loss: 0.4546 - acc: 0.8500 - val_loss: 0.5085 - val_acc: 0.8340

Epoch 00074: val_loss improved from 0.52269 to 0.50853, saving model to weights.best.letters.model.hdf5
Epoch 75/100
 - 34s - loss: 0.4675 - acc: 0.8498 - val_loss: 0.5015 - val_acc: 0.8603

Epoch 00075: val_loss improved from 0.50853 to 0.50147, saving model to weights.best.letters.model.hdf5
Epoch 76/100
 - 34s - loss: 0.4587 - acc: 0.8521 - val_loss: 0.5837 - val_acc: 0.8155

Epoch 00076: val_loss did not improve from 0.50147
Epoch 77/100
 - 34s - loss: 0.4715 - acc: 0.8454 - val_loss: 0.4871 - val_acc: 0.8393

Epoch 00077: val_loss improved from 0.50147 to 0.48710, saving model to weights.best.letters.model.hdf5
Epoch 78/100
 - 34s - loss: 0.4287 - acc: 0.8557 - val_loss: 0.5864 - val_acc: 0.8248

Epoch 00078: val_loss did not improve from 0.48710
Epoch 79/100
 - 34s - loss: 0.4345 - acc: 0.8551 - val_loss: 0.4956 - val_acc: 0.8485

Epoch 00079: val_loss did not improve from 0.48710
Epoch 80/100
 - 35s - loss: 0.4131 - acc: 0.8720 - val_loss: 0.4882 - val_acc: 0.8617

Epoch 00080: val_loss did not improve from 0.48710
Epoch 81/100
 - 35s - loss: 0.4249 - acc: 0.8587 - val_loss: 0.5339 - val_acc: 0.8538

Epoch 00081: val_loss did not improve from 0.48710
Epoch 82/100
 - 35s - loss: 0.4159 - acc: 0.8613 - val_loss: 0.6055 - val_acc: 0.8221

Epoch 00082: val_loss did not improve from 0.48710
Epoch 83/100
 - 35s - loss: 0.4235 - acc: 0.8635 - val_loss: 0.4903 - val_acc: 0.8458

Epoch 00083: val_loss did not improve from 0.48710
Epoch 84/100
 - 35s - loss: 0.3896 - acc: 0.8707 - val_loss: 0.4961 - val_acc: 0.8564

Epoch 00084: val_loss did not improve from 0.48710
Epoch 85/100
 - 35s - loss: 0.4244 - acc: 0.8602 - val_loss: 0.4870 - val_acc: 0.8485

Epoch 00085: val_loss improved from 0.48710 to 0.48699, saving model to weights.best.letters.model.hdf5
Epoch 86/100
 - 36s - loss: 0.3924 - acc: 0.8706 - val_loss: 0.4653 - val_acc: 0.8669

Epoch 00086: val_loss improved from 0.48699 to 0.46530, saving model to weights.best.letters.model.hdf5
Epoch 87/100
 - 35s - loss: 0.3879 - acc: 0.8694 - val_loss: 0.4887 - val_acc: 0.8472

Epoch 00087: val_loss did not improve from 0.46530
Epoch 88/100
 - 35s - loss: 0.3936 - acc: 0.8742 - val_loss: 0.4748 - val_acc: 0.8564

Epoch 00088: val_loss did not improve from 0.46530
Epoch 89/100
 - 34s - loss: 0.3740 - acc: 0.8803 - val_loss: 0.5242 - val_acc: 0.8340

Epoch 00089: val_loss did not improve from 0.46530
Epoch 90/100
 - 34s - loss: 0.3732 - acc: 0.8770 - val_loss: 0.4451 - val_acc: 0.8630

Epoch 00090: val_loss improved from 0.46530 to 0.44508, saving model to weights.best.letters.model.hdf5
Epoch 91/100
 - 34s - loss: 0.3876 - acc: 0.8706 - val_loss: 0.5162 - val_acc: 0.8366

Epoch 00091: val_loss did not improve from 0.44508
Epoch 92/100
 - 34s - loss: 0.3565 - acc: 0.8783 - val_loss: 0.5071 - val_acc: 0.8458

Epoch 00092: val_loss did not improve from 0.44508
Epoch 93/100
 - 36s - loss: 0.3468 - acc: 0.8839 - val_loss: 0.4529 - val_acc: 0.8590

Epoch 00093: val_loss did not improve from 0.44508
Epoch 94/100
 - 36s - loss: 0.3591 - acc: 0.8798 - val_loss: 0.4823 - val_acc: 0.8406

Epoch 00094: val_loss did not improve from 0.44508
Epoch 95/100
 - 35s - loss: 0.3231 - acc: 0.8888 - val_loss: 0.4674 - val_acc: 0.8564

Epoch 00095: val_loss did not improve from 0.44508
Epoch 96/100
 - 34s - loss: 0.3662 - acc: 0.8818 - val_loss: 0.4527 - val_acc: 0.8669

Epoch 00096: val_loss did not improve from 0.44508
Epoch 97/100
 - 34s - loss: 0.3433 - acc: 0.8850 - val_loss: 0.4541 - val_acc: 0.8617

Epoch 00097: val_loss did not improve from 0.44508
Epoch 98/100
 - 34s - loss: 0.3269 - acc: 0.8918 - val_loss: 0.4155 - val_acc: 0.8748

Epoch 00098: val_loss improved from 0.44508 to 0.41545, saving model to weights.best.letters.model.hdf5
Epoch 99/100
 - 34s - loss: 0.3231 - acc: 0.8920 - val_loss: 0.4405 - val_acc: 0.8603

Epoch 00099: val_loss did not improve from 0.41545
Epoch 100/100
 - 34s - loss: 0.3237 - acc: 0.8939 - val_loss: 0.4865 - val_acc: 0.8524

Epoch 00100: val_loss did not improve from 0.41545
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<p>We should have an accuracy greater than 3%</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.model.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">score</span>
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<pre>759/759 [==============================] - 4s 5ms/step
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<pre>[0.41009520742261835, 0.8787878794161227]</pre>
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<p>Apply the ImageDataGenerator() function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the model with ImageDataGenerator()</span>
<span class="c1"># TODO: Define parameters</span>
<span class="n">steps</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span>
<span class="n">data_generator</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shear_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">rotation_range</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>

<span class="n">generator</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_generator</span><span class="o">.</span><span class="n">flow</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span>
                                <span class="n">steps_per_epoch</span><span class="o">=</span><span class="n">steps</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span>
                                <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">),</span> 
                                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Epoch 1/10
 - 403s - loss: 0.8083 - acc: 0.7530 - val_loss: 0.4721 - val_acc: 0.8630

Epoch 00001: val_loss improved from inf to 0.47210, saving model to weights.best.letters.model.hdf5
Epoch 2/10
 - 385s - loss: 0.6892 - acc: 0.7850 - val_loss: 0.4063 - val_acc: 0.8682

Epoch 00002: val_loss improved from 0.47210 to 0.40633, saving model to weights.best.letters.model.hdf5
Epoch 3/10
 - 396s - loss: 0.6359 - acc: 0.7989 - val_loss: 0.4051 - val_acc: 0.8682

Epoch 00003: val_loss improved from 0.40633 to 0.40511, saving model to weights.best.letters.model.hdf5
Epoch 4/10
 - 389s - loss: 0.5756 - acc: 0.8162 - val_loss: 0.3857 - val_acc: 0.8854

Epoch 00004: val_loss improved from 0.40511 to 0.38570, saving model to weights.best.letters.model.hdf5
Epoch 5/10
 - 363s - loss: 0.5420 - acc: 0.8280 - val_loss: 0.3037 - val_acc: 0.8959

Epoch 00005: val_loss improved from 0.38570 to 0.30371, saving model to weights.best.letters.model.hdf5
Epoch 6/10
 - 364s - loss: 0.5104 - acc: 0.8360 - val_loss: 0.3152 - val_acc: 0.8999

Epoch 00006: val_loss did not improve from 0.30371
Epoch 7/10
 - 362s - loss: 0.4908 - acc: 0.8442 - val_loss: 0.3073 - val_acc: 0.8933

Epoch 00007: val_loss did not improve from 0.30371
Epoch 8/10
 - 359s - loss: 0.4532 - acc: 0.8557 - val_loss: 0.3288 - val_acc: 0.8946

Epoch 00008: val_loss did not improve from 0.30371
Epoch 9/10
 - 370s - loss: 0.4446 - acc: 0.8573 - val_loss: 0.3139 - val_acc: 0.8999

Epoch 00009: val_loss did not improve from 0.30371
Epoch 10/10
 - 405s - loss: 0.4206 - acc: 0.8644 - val_loss: 0.2835 - val_acc: 0.9065

Epoch 00010: val_loss improved from 0.30371 to 0.28353, saving model to weights.best.letters.model.hdf5
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.model.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">score</span>
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<pre>759/759 [==============================] - 1s 2ms/step
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<pre>[0.2782733061096885, 0.9209486172290344]</pre>
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<p>Let's compare the results with classifying algorithms.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the classifier and get the accuracy score</span>
<span class="n">y_train_c</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_train</span><span class="p">])</span>
<span class="n">y_test_c</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_test</span><span class="p">])</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">),</span> <span class="n">y_train_c</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">),</span> <span class="n">y_test_c</span><span class="p">)</span>
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<pre>0.25296442687747034</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the classifier and get the accuracy score</span>
<span class="n">clf2</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">),</span> <span class="n">y_train_c</span><span class="p">)</span>
<span class="n">clf2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">),</span> <span class="n">y_test_c</span><span class="p">)</span>
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<pre>0.21870882740447958</pre>
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<h3 id="Grayscaled-Images">Grayscaled Images<a class="anchor-link" href="#Grayscaled-Images">&#182;</a></h3><p>Define a model architecture and compile the model for grayscaled images.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gray_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
    
    <span class="c1"># Define a model architecture</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">x_train2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span>    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span>    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalMaxPooling2D</span><span class="p">())</span> 
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1024</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span> 
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span> 
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.02</span><span class="p">))</span> 
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>    

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>
    
    <span class="c1"># Compile the model</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">model</span>

<span class="n">gray_model</span> <span class="o">=</span> <span class="n">gray_model</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[38]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gray_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.gray_model.hdf5&#39;</span><span class="p">,</span> 
                                    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[53]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Train the model</span>
<span class="n">gray_history</span> <span class="o">=</span> <span class="n">gray_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train2</span><span class="p">,</span> <span class="n">y_train2</span><span class="p">,</span> 
                              <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                              <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_valid2</span><span class="p">,</span> <span class="n">y_valid2</span><span class="p">),</span>
                              <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">gray_checkpointer</span><span class="p">])</span>
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<pre>
Epoch 00001: val_loss improved from inf to 3.49205, saving model to weights.best.letters.gray_model.hdf5

Epoch 00002: val_loss improved from 3.49205 to 3.42396, saving model to weights.best.letters.gray_model.hdf5

Epoch 00003: val_loss improved from 3.42396 to 3.40152, saving model to weights.best.letters.gray_model.hdf5

Epoch 00004: val_loss did not improve from 3.40152

Epoch 00005: val_loss improved from 3.40152 to 3.37310, saving model to weights.best.letters.gray_model.hdf5

Epoch 00006: val_loss improved from 3.37310 to 3.32898, saving model to weights.best.letters.gray_model.hdf5

Epoch 00007: val_loss improved from 3.32898 to 3.21467, saving model to weights.best.letters.gray_model.hdf5

Epoch 00008: val_loss improved from 3.21467 to 2.99934, saving model to weights.best.letters.gray_model.hdf5

Epoch 00009: val_loss improved from 2.99934 to 2.89259, saving model to weights.best.letters.gray_model.hdf5

Epoch 00010: val_loss improved from 2.89259 to 2.56374, saving model to weights.best.letters.gray_model.hdf5

Epoch 00011: val_loss did not improve from 2.56374

Epoch 00012: val_loss improved from 2.56374 to 2.34020, saving model to weights.best.letters.gray_model.hdf5

Epoch 00013: val_loss improved from 2.34020 to 2.22500, saving model to weights.best.letters.gray_model.hdf5

Epoch 00014: val_loss did not improve from 2.22500

Epoch 00015: val_loss improved from 2.22500 to 2.04112, saving model to weights.best.letters.gray_model.hdf5

Epoch 00016: val_loss improved from 2.04112 to 1.77666, saving model to weights.best.letters.gray_model.hdf5

Epoch 00017: val_loss improved from 1.77666 to 1.76763, saving model to weights.best.letters.gray_model.hdf5

Epoch 00018: val_loss improved from 1.76763 to 1.53015, saving model to weights.best.letters.gray_model.hdf5

Epoch 00019: val_loss did not improve from 1.53015

Epoch 00020: val_loss improved from 1.53015 to 1.45172, saving model to weights.best.letters.gray_model.hdf5

Epoch 00021: val_loss improved from 1.45172 to 1.37342, saving model to weights.best.letters.gray_model.hdf5

Epoch 00022: val_loss did not improve from 1.37342

Epoch 00023: val_loss improved from 1.37342 to 1.31681, saving model to weights.best.letters.gray_model.hdf5

Epoch 00024: val_loss did not improve from 1.31681

Epoch 00025: val_loss improved from 1.31681 to 1.27389, saving model to weights.best.letters.gray_model.hdf5

Epoch 00026: val_loss improved from 1.27389 to 1.09925, saving model to weights.best.letters.gray_model.hdf5

Epoch 00027: val_loss improved from 1.09925 to 1.05474, saving model to weights.best.letters.gray_model.hdf5

Epoch 00028: val_loss did not improve from 1.05474

Epoch 00029: val_loss improved from 1.05474 to 0.95061, saving model to weights.best.letters.gray_model.hdf5

Epoch 00030: val_loss did not improve from 0.95061

Epoch 00031: val_loss improved from 0.95061 to 0.87738, saving model to weights.best.letters.gray_model.hdf5

Epoch 00032: val_loss improved from 0.87738 to 0.86876, saving model to weights.best.letters.gray_model.hdf5

Epoch 00033: val_loss improved from 0.86876 to 0.86680, saving model to weights.best.letters.gray_model.hdf5

Epoch 00034: val_loss improved from 0.86680 to 0.84466, saving model to weights.best.letters.gray_model.hdf5

Epoch 00035: val_loss did not improve from 0.84466

Epoch 00036: val_loss did not improve from 0.84466

Epoch 00037: val_loss improved from 0.84466 to 0.76588, saving model to weights.best.letters.gray_model.hdf5

Epoch 00038: val_loss improved from 0.76588 to 0.73786, saving model to weights.best.letters.gray_model.hdf5

Epoch 00039: val_loss did not improve from 0.73786

Epoch 00040: val_loss improved from 0.73786 to 0.72084, saving model to weights.best.letters.gray_model.hdf5

Epoch 00041: val_loss improved from 0.72084 to 0.72049, saving model to weights.best.letters.gray_model.hdf5

Epoch 00042: val_loss improved from 0.72049 to 0.65537, saving model to weights.best.letters.gray_model.hdf5

Epoch 00043: val_loss did not improve from 0.65537

Epoch 00044: val_loss did not improve from 0.65537

Epoch 00045: val_loss did not improve from 0.65537

Epoch 00046: val_loss did not improve from 0.65537

Epoch 00047: val_loss did not improve from 0.65537

Epoch 00048: val_loss did not improve from 0.65537

Epoch 00049: val_loss did not improve from 0.65537

Epoch 00050: val_loss improved from 0.65537 to 0.61516, saving model to weights.best.letters.gray_model.hdf5

Epoch 00051: val_loss improved from 0.61516 to 0.60383, saving model to weights.best.letters.gray_model.hdf5

Epoch 00052: val_loss did not improve from 0.60383

Epoch 00053: val_loss did not improve from 0.60383

Epoch 00054: val_loss did not improve from 0.60383

Epoch 00055: val_loss did not improve from 0.60383

Epoch 00056: val_loss did not improve from 0.60383

Epoch 00057: val_loss did not improve from 0.60383

Epoch 00058: val_loss did not improve from 0.60383

Epoch 00059: val_loss improved from 0.60383 to 0.54648, saving model to weights.best.letters.gray_model.hdf5

Epoch 00060: val_loss did not improve from 0.54648

Epoch 00061: val_loss improved from 0.54648 to 0.54232, saving model to weights.best.letters.gray_model.hdf5

Epoch 00062: val_loss did not improve from 0.54232

Epoch 00063: val_loss did not improve from 0.54232

Epoch 00064: val_loss did not improve from 0.54232

Epoch 00065: val_loss did not improve from 0.54232

Epoch 00066: val_loss improved from 0.54232 to 0.49774, saving model to weights.best.letters.gray_model.hdf5

Epoch 00067: val_loss did not improve from 0.49774

Epoch 00068: val_loss did not improve from 0.49774

Epoch 00069: val_loss did not improve from 0.49774

Epoch 00070: val_loss did not improve from 0.49774

Epoch 00071: val_loss did not improve from 0.49774

Epoch 00072: val_loss did not improve from 0.49774

Epoch 00073: val_loss did not improve from 0.49774

Epoch 00074: val_loss did not improve from 0.49774

Epoch 00075: val_loss did not improve from 0.49774

Epoch 00076: val_loss did not improve from 0.49774

Epoch 00077: val_loss did not improve from 0.49774

Epoch 00078: val_loss did not improve from 0.49774

Epoch 00079: val_loss did not improve from 0.49774

Epoch 00080: val_loss did not improve from 0.49774

Epoch 00081: val_loss did not improve from 0.49774

Epoch 00082: val_loss improved from 0.49774 to 0.49454, saving model to weights.best.letters.gray_model.hdf5

Epoch 00083: val_loss did not improve from 0.49454

Epoch 00084: val_loss did not improve from 0.49454

Epoch 00085: val_loss improved from 0.49454 to 0.47416, saving model to weights.best.letters.gray_model.hdf5

Epoch 00086: val_loss did not improve from 0.47416

Epoch 00087: val_loss did not improve from 0.47416

Epoch 00088: val_loss did not improve from 0.47416

Epoch 00089: val_loss did not improve from 0.47416

Epoch 00090: val_loss did not improve from 0.47416

Epoch 00091: val_loss did not improve from 0.47416

Epoch 00092: val_loss did not improve from 0.47416

Epoch 00093: val_loss did not improve from 0.47416

Epoch 00094: val_loss did not improve from 0.47416

Epoch 00095: val_loss did not improve from 0.47416

Epoch 00096: val_loss did not improve from 0.47416

Epoch 00097: val_loss did not improve from 0.47416

Epoch 00098: val_loss did not improve from 0.47416

Epoch 00099: val_loss improved from 0.47416 to 0.45422, saving model to weights.best.letters.gray_model.hdf5

Epoch 00100: val_loss did not improve from 0.45422
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<div class="prompt input_prompt">In&nbsp;[54]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot the training history</span>
<span class="n">history_plot</span><span class="p">(</span><span class="n">gray_history</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
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<p>Try to reach an accuracy greater than 80%</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">gray_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.gray_model.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">gray_score</span> <span class="o">=</span> <span class="n">gray_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test2</span><span class="p">,</span> <span class="n">y_test2</span><span class="p">)</span>
<span class="n">gray_score</span>
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<pre>759/759 [==============================] - 1s 1ms/step
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<pre>[0.5873400140578253, 0.836627141289089]</pre>
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<p>Apply the ImageDataGenerator() function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the model with ImageDataGenerator()</span>
<span class="c1"># TODO: Define parameters</span>
<span class="n">steps</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span>
<span class="n">data_generator</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shear_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">rotation_range</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>

<span class="n">gray_generator</span> <span class="o">=</span> <span class="n">gray_model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_generator</span><span class="o">.</span><span class="n">flow</span><span class="p">(</span><span class="n">x_train2</span><span class="p">,</span> <span class="n">y_train2</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">),</span>
                                          <span class="n">steps_per_epoch</span> <span class="o">=</span> <span class="n">steps</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="n">epochs</span><span class="p">,</span>
                                          <span class="n">validation_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_test2</span><span class="p">,</span> <span class="n">y_test2</span><span class="p">),</span> 
                                          <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">gray_checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Epoch 1/10
 - 285s - loss: 1.1199 - acc: 0.6897 - val_loss: 0.6100 - val_acc: 0.8208

Epoch 00001: val_loss improved from inf to 0.61002, saving model to weights.best.letters.gray_model.hdf5
Epoch 2/10
 - 329s - loss: 1.0183 - acc: 0.7148 - val_loss: 0.5219 - val_acc: 0.8458

Epoch 00002: val_loss improved from 0.61002 to 0.52192, saving model to weights.best.letters.gray_model.hdf5
Epoch 3/10
 - 312s - loss: 0.9944 - acc: 0.7206 - val_loss: 0.6310 - val_acc: 0.8300

Epoch 00003: val_loss did not improve from 0.52192
Epoch 4/10
 - 329s - loss: 0.9633 - acc: 0.7299 - val_loss: 0.5925 - val_acc: 0.8419

Epoch 00004: val_loss did not improve from 0.52192
Epoch 5/10
 - 370s - loss: 0.9591 - acc: 0.7320 - val_loss: 0.4841 - val_acc: 0.8538

Epoch 00005: val_loss improved from 0.52192 to 0.48406, saving model to weights.best.letters.gray_model.hdf5
Epoch 6/10
 - 288s - loss: 0.9461 - acc: 0.7366 - val_loss: 0.4849 - val_acc: 0.8709

Epoch 00006: val_loss did not improve from 0.48406
Epoch 7/10
 - 364s - loss: 0.9330 - acc: 0.7410 - val_loss: 0.4513 - val_acc: 0.8775

Epoch 00007: val_loss improved from 0.48406 to 0.45134, saving model to weights.best.letters.gray_model.hdf5
Epoch 8/10
 - 335s - loss: 0.9460 - acc: 0.7394 - val_loss: 0.5245 - val_acc: 0.8564

Epoch 00008: val_loss did not improve from 0.45134
Epoch 9/10
 - 290s - loss: 0.9431 - acc: 0.7402 - val_loss: 0.4481 - val_acc: 0.8656

Epoch 00009: val_loss improved from 0.45134 to 0.44808, saving model to weights.best.letters.gray_model.hdf5
Epoch 10/10
 - 297s - loss: 0.9507 - acc: 0.7400 - val_loss: 0.4305 - val_acc: 0.8748

Epoch 00010: val_loss improved from 0.44808 to 0.43046, saving model to weights.best.letters.gray_model.hdf5
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">gray_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.gray_model.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">gray_score</span> <span class="o">=</span> <span class="n">gray_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test2</span><span class="p">,</span> <span class="n">y_test2</span><span class="p">)</span>
<span class="n">gray_score</span>
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<pre>759/759 [==============================] - 1s 1ms/step
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<pre>[0.43045948651939514, 0.8748353102461622]</pre>
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<p>Let's compare the results with classifying algorithms.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the classifier and get the accuracy score</span>
<span class="n">y_train2_c</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_train2</span><span class="p">])</span>
<span class="n">y_test2_c</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_test2</span><span class="p">])</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">),</span> <span class="n">y_train2_c</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">),</span> <span class="n">y_test2_c</span><span class="p">)</span>
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<pre>0.22924901185770752</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the classifier and get the accuracy score</span>
<span class="n">clf2</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">),</span> <span class="n">y_train2_c</span><span class="p">)</span>
<span class="n">clf2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">x_test2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">),</span> <span class="n">y_test2_c</span><span class="p">)</span>
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<pre>0.1541501976284585</pre>
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<h2 id="Step-5.-Create-a-Multi-Label-Classification-Model">Step 5. Create a Multi-Label Classification Model<a class="anchor-link" href="#Step-5.-Create-a-Multi-Label-Classification-Model">&#182;</a></h2><h3 id="Color-Images">Color Images<a class="anchor-link" href="#Color-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">multi_model</span><span class="p">():</span>    
    <span class="n">model_input</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">model_input</span><span class="p">)</span>
    <span class="c1"># TODO: Define a model architecture</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)(</span><span class="n">model_input</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>       
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
              
    <span class="n">x</span> <span class="o">=</span> <span class="n">GlobalMaxPooling2D</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
<span class="c1">#    x = Dense(128, activation=&#39;relu&#39;)(x)    </span>
<span class="c1">#    x = Dropout(0.25)(x)    </span>
    
    <span class="n">y1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="n">model_input</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">])</span>
    
    <span class="c1"># TODO: Compile the model</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">multi_model</span> <span class="o">=</span> <span class="n">multi_model</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Display the model architecture</span>
<span class="n">multi_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 32, 32, 3)     0                                            
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 32, 32, 32)    2432        input_1[0][0]                    
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 16, 16, 32)    0           conv2d_1[0][0]                   
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 16, 16, 32)    0           max_pooling2d_1[0][0]            
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 16, 16, 128)   102528      dropout_1[0][0]                  
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 8, 8, 128)     0           conv2d_2[0][0]                   
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 8, 8, 128)     0           max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
global_max_pooling2d_1 (GlobalMa (None, 128)           0           dropout_2[0][0]                  
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 1024)          132096      global_max_pooling2d_1[0][0]     
____________________________________________________________________________________________________
dropout_3 (Dropout)              (None, 1024)          0           dense_1[0][0]                    
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 33)            33825       dropout_3[0][0]                  
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 3)             3075        dropout_3[0][0]                  
====================================================================================================
Total params: 273,956
Trainable params: 273,956
Non-trainable params: 0
____________________________________________________________________________________________________
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Train the model</span>
<span class="n">multi_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.multi.hdf5&#39;</span><span class="p">,</span> 
                                     <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">multi_history</span> <span class="o">=</span> <span class="n">multi_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train3</span><span class="p">,</span> <span class="n">y_train3_list</span><span class="p">,</span> 
                                <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test3</span><span class="p">,</span> <span class="n">y_test3_list</span><span class="p">),</span> 
                                <span class="n">epochs</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">multi_checkpointer</span><span class="p">])</span>
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<pre>Epoch 00000: val_loss improved from inf to 4.24103, saving model to weights.best.letters.multi.hdf5
Epoch 00001: val_loss did not improve
Epoch 00002: val_loss improved from 4.24103 to 4.21858, saving model to weights.best.letters.multi.hdf5
Epoch 00003: val_loss improved from 4.21858 to 4.14341, saving model to weights.best.letters.multi.hdf5
Epoch 00004: val_loss improved from 4.14341 to 4.09419, saving model to weights.best.letters.multi.hdf5
Epoch 00005: val_loss improved from 4.09419 to 4.09170, saving model to weights.best.letters.multi.hdf5
Epoch 00006: val_loss improved from 4.09170 to 4.08563, saving model to weights.best.letters.multi.hdf5
Epoch 00007: val_loss improved from 4.08563 to 4.07615, saving model to weights.best.letters.multi.hdf5
Epoch 00008: val_loss improved from 4.07615 to 3.96762, saving model to weights.best.letters.multi.hdf5
Epoch 00009: val_loss improved from 3.96762 to 3.82059, saving model to weights.best.letters.multi.hdf5
Epoch 00010: val_loss improved from 3.82059 to 3.76746, saving model to weights.best.letters.multi.hdf5
Epoch 00011: val_loss improved from 3.76746 to 3.56167, saving model to weights.best.letters.multi.hdf5
Epoch 00012: val_loss did not improve
Epoch 00013: val_loss improved from 3.56167 to 3.52598, saving model to weights.best.letters.multi.hdf5
Epoch 00014: val_loss improved from 3.52598 to 3.44174, saving model to weights.best.letters.multi.hdf5
Epoch 00015: val_loss improved from 3.44174 to 3.28350, saving model to weights.best.letters.multi.hdf5
Epoch 00016: val_loss did not improve
Epoch 00017: val_loss improved from 3.28350 to 3.21433, saving model to weights.best.letters.multi.hdf5
Epoch 00018: val_loss improved from 3.21433 to 3.13028, saving model to weights.best.letters.multi.hdf5
Epoch 00019: val_loss improved from 3.13028 to 3.01444, saving model to weights.best.letters.multi.hdf5
Epoch 00020: val_loss did not improve
Epoch 00021: val_loss improved from 3.01444 to 2.86931, saving model to weights.best.letters.multi.hdf5
Epoch 00022: val_loss did not improve
Epoch 00023: val_loss did not improve
Epoch 00024: val_loss improved from 2.86931 to 2.78363, saving model to weights.best.letters.multi.hdf5
Epoch 00025: val_loss did not improve
Epoch 00026: val_loss did not improve
Epoch 00027: val_loss did not improve
Epoch 00028: val_loss did not improve
Epoch 00029: val_loss did not improve
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<p>We should have an accuracy greater than 3% for the first target (letter) and 30% for the second target (background).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">multi_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.multi.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">multi_scores</span> <span class="o">=</span> <span class="n">multi_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test3</span><span class="p">,</span> <span class="n">y_test3_list</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Scores: </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="p">,</span> <span class="p">(</span><span class="n">multi_scores</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">multi_scores</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Second label. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">multi_scores</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>Scores: 
 [2.7836263308569063, 1.7998781450809382, 0.98374816928456421, 0.51646903742286365, 0.6613965736547478]
First label. Accuracy: 51.65%
Second label. Accuracy: 66.14%
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<h3 id="Grayscaled-Images">Grayscaled Images<a class="anchor-link" href="#Grayscaled-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">gray_multi_model</span><span class="p">():</span>    
    <span class="n">model_input</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">model_input</span><span class="p">)</span>
    <span class="c1"># TODO: Define a model architecture</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)(</span><span class="n">model_input</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>       
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
              
    <span class="n">x</span> <span class="o">=</span> <span class="n">GlobalMaxPooling2D</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">y1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
       
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="n">model_input</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">])</span>
    <span class="c1"># TODO: Compile the model</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>   
    <span class="k">return</span> <span class="n">model</span>

<span class="n">gray_multi_model</span> <span class="o">=</span> <span class="n">gray_multi_model</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Train the model</span>
<span class="n">gray_multi_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.gray_multi.hdf5&#39;</span><span class="p">,</span> 
                                          <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">gray_multi_history</span> <span class="o">=</span> <span class="n">gray_multi_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train4</span><span class="p">,</span> <span class="n">y_train4_list</span><span class="p">,</span> 
                                          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test4</span><span class="p">,</span> <span class="n">y_test4_list</span><span class="p">),</span> 
                                          <span class="n">epochs</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                                          <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">gray_multi_checkpointer</span><span class="p">])</span>
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<pre>Epoch 00000: val_loss improved from inf to 4.21238, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00001: val_loss improved from 4.21238 to 4.14618, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00002: val_loss improved from 4.14618 to 4.09458, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00003: val_loss did not improve
Epoch 00004: val_loss did not improve
Epoch 00005: val_loss improved from 4.09458 to 4.01250, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00006: val_loss did not improve
Epoch 00007: val_loss improved from 4.01250 to 3.99474, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00008: val_loss did not improve
Epoch 00009: val_loss did not improve
Epoch 00010: val_loss did not improve
Epoch 00011: val_loss did not improve
Epoch 00012: val_loss improved from 3.99474 to 3.98766, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00013: val_loss improved from 3.98766 to 3.94370, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00014: val_loss did not improve
Epoch 00015: val_loss did not improve
Epoch 00016: val_loss did not improve
Epoch 00017: val_loss improved from 3.94370 to 3.92857, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00018: val_loss did not improve
Epoch 00019: val_loss did not improve
Epoch 00020: val_loss improved from 3.92857 to 3.92817, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00021: val_loss did not improve
Epoch 00022: val_loss did not improve
Epoch 00023: val_loss did not improve
Epoch 00024: val_loss improved from 3.92817 to 3.84638, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00025: val_loss did not improve
Epoch 00026: val_loss did not improve
Epoch 00027: val_loss did not improve
Epoch 00028: val_loss improved from 3.84638 to 3.81901, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00029: val_loss improved from 3.81901 to 3.81234, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00030: val_loss did not improve
Epoch 00031: val_loss did not improve
Epoch 00032: val_loss improved from 3.81234 to 3.75132, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00033: val_loss did not improve
Epoch 00034: val_loss improved from 3.75132 to 3.63502, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00035: val_loss did not improve
Epoch 00036: val_loss did not improve
Epoch 00037: val_loss improved from 3.63502 to 3.55845, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00038: val_loss improved from 3.55845 to 3.54641, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00039: val_loss improved from 3.54641 to 3.54189, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00040: val_loss improved from 3.54189 to 3.53803, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00041: val_loss improved from 3.53803 to 3.42176, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00042: val_loss did not improve
Epoch 00043: val_loss did not improve
Epoch 00044: val_loss did not improve
Epoch 00045: val_loss improved from 3.42176 to 3.19178, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00046: val_loss did not improve
Epoch 00047: val_loss did not improve
Epoch 00048: val_loss improved from 3.19178 to 3.16260, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00049: val_loss did not improve
Epoch 00050: val_loss improved from 3.16260 to 3.12460, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00051: val_loss did not improve
Epoch 00052: val_loss improved from 3.12460 to 2.97462, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00053: val_loss did not improve
Epoch 00054: val_loss did not improve
Epoch 00055: val_loss did not improve
Epoch 00056: val_loss improved from 2.97462 to 2.84980, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00057: val_loss did not improve
Epoch 00058: val_loss improved from 2.84980 to 2.81775, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00059: val_loss did not improve
Epoch 00060: val_loss did not improve
Epoch 00061: val_loss improved from 2.81775 to 2.72539, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00062: val_loss did not improve
Epoch 00063: val_loss did not improve
Epoch 00064: val_loss improved from 2.72539 to 2.69906, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00065: val_loss did not improve
Epoch 00066: val_loss improved from 2.69906 to 2.54668, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00067: val_loss did not improve
Epoch 00068: val_loss improved from 2.54668 to 2.50314, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00069: val_loss did not improve
Epoch 00070: val_loss improved from 2.50314 to 2.42454, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00071: val_loss did not improve
Epoch 00072: val_loss improved from 2.42454 to 2.31276, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00073: val_loss did not improve
Epoch 00074: val_loss did not improve
Epoch 00075: val_loss improved from 2.31276 to 2.30224, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00076: val_loss improved from 2.30224 to 2.29320, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00077: val_loss did not improve
Epoch 00078: val_loss improved from 2.29320 to 2.27964, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00079: val_loss improved from 2.27964 to 2.15521, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00080: val_loss improved from 2.15521 to 2.12911, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00081: val_loss did not improve
Epoch 00082: val_loss did not improve
Epoch 00083: val_loss improved from 2.12911 to 2.08030, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00084: val_loss did not improve
Epoch 00085: val_loss improved from 2.08030 to 2.03928, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00086: val_loss improved from 2.03928 to 2.02203, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00087: val_loss improved from 2.02203 to 2.01164, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00088: val_loss did not improve
Epoch 00089: val_loss improved from 2.01164 to 1.92088, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00090: val_loss improved from 1.92088 to 1.86093, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00091: val_loss did not improve
Epoch 00092: val_loss improved from 1.86093 to 1.80908, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00093: val_loss did not improve
Epoch 00094: val_loss improved from 1.80908 to 1.67775, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00095: val_loss did not improve
Epoch 00096: val_loss did not improve
Epoch 00097: val_loss did not improve
Epoch 00098: val_loss improved from 1.67775 to 1.61598, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00099: val_loss did not improve
Epoch 00100: val_loss did not improve
Epoch 00101: val_loss did not improve
Epoch 00102: val_loss did not improve
Epoch 00103: val_loss did not improve
Epoch 00104: val_loss improved from 1.61598 to 1.55320, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00105: val_loss did not improve
Epoch 00106: val_loss did not improve
Epoch 00107: val_loss did not improve
Epoch 00108: val_loss improved from 1.55320 to 1.50381, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00109: val_loss improved from 1.50381 to 1.45387, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00110: val_loss improved from 1.45387 to 1.39179, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00111: val_loss did not improve
Epoch 00112: val_loss did not improve
Epoch 00113: val_loss improved from 1.39179 to 1.37310, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00114: val_loss did not improve
Epoch 00115: val_loss did not improve
Epoch 00116: val_loss did not improve
Epoch 00117: val_loss did not improve
Epoch 00118: val_loss did not improve
Epoch 00119: val_loss improved from 1.37310 to 1.32442, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00120: val_loss did not improve
Epoch 00121: val_loss did not improve
Epoch 00122: val_loss did not improve
Epoch 00123: val_loss did not improve
Epoch 00124: val_loss did not improve
Epoch 00125: val_loss did not improve
Epoch 00126: val_loss did not improve
Epoch 00127: val_loss improved from 1.32442 to 1.30072, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00128: val_loss improved from 1.30072 to 1.19615, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00129: val_loss did not improve
Epoch 00130: val_loss did not improve
Epoch 00131: val_loss did not improve
Epoch 00132: val_loss did not improve
Epoch 00133: val_loss improved from 1.19615 to 1.16374, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00134: val_loss did not improve
Epoch 00135: val_loss did not improve
Epoch 00136: val_loss did not improve
Epoch 00137: val_loss did not improve
Epoch 00138: val_loss did not improve
Epoch 00139: val_loss did not improve
Epoch 00140: val_loss did not improve
Epoch 00141: val_loss did not improve
Epoch 00142: val_loss did not improve
Epoch 00143: val_loss did not improve
Epoch 00144: val_loss did not improve
Epoch 00145: val_loss improved from 1.16374 to 1.12270, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00146: val_loss did not improve
Epoch 00147: val_loss did not improve
Epoch 00148: val_loss did not improve
Epoch 00149: val_loss did not improve
Epoch 00150: val_loss did not improve
Epoch 00151: val_loss did not improve
Epoch 00152: val_loss did not improve
Epoch 00153: val_loss did not improve
Epoch 00154: val_loss did not improve
Epoch 00155: val_loss did not improve
Epoch 00156: val_loss improved from 1.12270 to 1.11291, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00157: val_loss did not improve
Epoch 00158: val_loss did not improve
Epoch 00159: val_loss did not improve
Epoch 00160: val_loss did not improve
Epoch 00161: val_loss did not improve
Epoch 00162: val_loss did not improve
Epoch 00163: val_loss did not improve
Epoch 00164: val_loss did not improve
Epoch 00165: val_loss did not improve
Epoch 00166: val_loss did not improve
Epoch 00167: val_loss did not improve
Epoch 00168: val_loss did not improve
Epoch 00169: val_loss did not improve
Epoch 00170: val_loss improved from 1.11291 to 1.06591, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00171: val_loss improved from 1.06591 to 1.03498, saving model to weights.best.letters.gray_multi.hdf5
Epoch 00172: val_loss did not improve
Epoch 00173: val_loss did not improve
Epoch 00174: val_loss did not improve
Epoch 00175: val_loss did not improve
Epoch 00176: val_loss did not improve
Epoch 00177: val_loss did not improve
Epoch 00178: val_loss did not improve
Epoch 00179: val_loss did not improve
Epoch 00180: val_loss did not improve
Epoch 00181: val_loss did not improve
Epoch 00182: val_loss did not improve
Epoch 00183: val_loss did not improve
Epoch 00184: val_loss did not improve
Epoch 00185: val_loss did not improve
Epoch 00186: val_loss did not improve
Epoch 00187: val_loss did not improve
Epoch 00188: val_loss did not improve
Epoch 00189: val_loss did not improve
Epoch 00190: val_loss did not improve
Epoch 00191: val_loss did not improve
Epoch 00192: val_loss did not improve
Epoch 00193: val_loss did not improve
Epoch 00194: val_loss did not improve
Epoch 00195: val_loss did not improve
Epoch 00196: val_loss did not improve
Epoch 00197: val_loss did not improve
Epoch 00198: val_loss did not improve
Epoch 00199: val_loss improved from 1.03498 to 1.02463, saving model to weights.best.letters.gray_multi.hdf5
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<p>We should have an accuracy greater than 3% for the first target (letter) and 30% for the second target (background).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">gray_multi_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.gray_multi.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">gray_multi_scores</span> <span class="o">=</span> <span class="n">gray_multi_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test4</span><span class="p">,</span> <span class="n">y_test4_list</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Scores: </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="p">,</span> <span class="p">(</span><span class="n">gray_multi_scores</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First label. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">gray_multi_scores</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Second label. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">gray_multi_scores</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>Scores: 
 [1.0246299333251982, 0.98210754664676148, 0.042522395086107834, 0.72200263543876575, 0.98814229249011853]
First label. Accuracy: 72.20%
Second label. Accuracy: 98.81%
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<h2 id="Step-6.-Keras-Applications">Step 6. Keras Applications<a class="anchor-link" href="#Step-6.-Keras-Applications">&#182;</a></h2><p>Choose one of the keras applications and try to reach an accuracy greater than 30%</p>
<h3 id="Color-Images">Color Images<a class="anchor-link" href="#Color-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create bottleneck features</span>
<span class="n">resize_x_train</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                           <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_train</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">resize_x_test</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_test</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                          <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_test</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">resize_x_valid</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_valid</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                          <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_valid</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>

<span class="n">iv3_x_train</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_train</span><span class="p">)</span>
<span class="n">iv3_x_test</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_test</span><span class="p">)</span>
<span class="n">iv3_x_valid</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_valid</span><span class="p">)</span>

<span class="n">iv3_base_model</span> <span class="o">=</span> <span class="n">InceptionV3</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">x_train_bn</span> <span class="o">=</span> <span class="n">iv3_base_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_train</span><span class="p">)</span>
<span class="n">x_test_bn</span> <span class="o">=</span> <span class="n">iv3_base_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_test</span><span class="p">)</span>
<span class="n">x_valid_bn</span> <span class="o">=</span> <span class="n">iv3_base_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_valid</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Save bottleneck features</span>
<span class="n">x_train_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_train_bn</span><span class="p">)</span>
<span class="n">x_test_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_test_bn</span><span class="p">)</span>
<span class="n">x_valid_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_valid_bn</span><span class="p">)</span>

<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_train_bn.npy&#39;</span><span class="p">,</span> <span class="n">x_train_bn</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_test_bn.npy&#39;</span><span class="p">,</span> <span class="n">x_test_bn</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_valid_bn.npy&#39;</span><span class="p">,</span> <span class="n">x_valid_bn</span><span class="p">)</span>

<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;y_train.npy&#39;</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;y_test.npy&#39;</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;y_valid.npy&#39;</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load bottleneck features</span>
<span class="n">x_train_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_train_bn.npy&#39;</span><span class="p">)</span>
<span class="n">x_test_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_test_bn.npy&#39;</span><span class="p">)</span>
<span class="n">x_valid_bn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_valid_bn.npy&#39;</span><span class="p">)</span>

<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;y_train.npy&#39;</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;y_test.npy&#39;</span><span class="p">)</span>
<span class="n">y_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;y_valid.npy&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">iv3_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
    <span class="c1"># TODO: Define a model architecture</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">x_train_bn</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2048</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
        
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>
    
    <span class="c1"># TODO: Compile the model </span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>    

    <span class="k">return</span> <span class="n">model</span>

<span class="n">iv3_model</span> <span class="o">=</span> <span class="n">iv3_model</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the model </span>
<span class="n">iv3_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.iv3.hdf5&#39;</span><span class="p">,</span> 
                                     <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">iv3_history</span> <span class="o">=</span> <span class="n">iv3_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train_bn</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> 
                            <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_valid_bn</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">),</span>
                            <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> 
                            <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">iv3_checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">);</span>
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<pre>Epoch 00000: val_loss improved from inf to 3.18183, saving model to weights.best.letters.iv3.hdf5
Epoch 00001: val_loss improved from 3.18183 to 2.90532, saving model to weights.best.letters.iv3.hdf5
Epoch 00002: val_loss improved from 2.90532 to 2.62292, saving model to weights.best.letters.iv3.hdf5
Epoch 00003: val_loss improved from 2.62292 to 2.22312, saving model to weights.best.letters.iv3.hdf5
Epoch 00004: val_loss improved from 2.22312 to 2.08494, saving model to weights.best.letters.iv3.hdf5
Epoch 00005: val_loss improved from 2.08494 to 2.06108, saving model to weights.best.letters.iv3.hdf5
Epoch 00006: val_loss improved from 2.06108 to 2.06026, saving model to weights.best.letters.iv3.hdf5
Epoch 00007: val_loss improved from 2.06026 to 1.89141, saving model to weights.best.letters.iv3.hdf5
Epoch 00008: val_loss improved from 1.89141 to 1.76281, saving model to weights.best.letters.iv3.hdf5
Epoch 00009: val_loss improved from 1.76281 to 1.74077, saving model to weights.best.letters.iv3.hdf5
Epoch 00010: val_loss did not improve
Epoch 00011: val_loss did not improve
Epoch 00012: val_loss improved from 1.74077 to 1.59924, saving model to weights.best.letters.iv3.hdf5
Epoch 00013: val_loss did not improve
Epoch 00014: val_loss improved from 1.59924 to 1.53354, saving model to weights.best.letters.iv3.hdf5
Epoch 00015: val_loss improved from 1.53354 to 1.51100, saving model to weights.best.letters.iv3.hdf5
Epoch 00016: val_loss improved from 1.51100 to 1.38904, saving model to weights.best.letters.iv3.hdf5
Epoch 00017: val_loss did not improve
Epoch 00018: val_loss did not improve
Epoch 00019: val_loss did not improve
Epoch 00020: val_loss did not improve
Epoch 00021: val_loss did not improve
Epoch 00022: val_loss did not improve
Epoch 00023: val_loss did not improve
Epoch 00024: val_loss improved from 1.38904 to 1.38291, saving model to weights.best.letters.iv3.hdf5
Epoch 00025: val_loss did not improve
Epoch 00026: val_loss did not improve
Epoch 00027: val_loss did not improve
Epoch 00028: val_loss did not improve
Epoch 00029: val_loss improved from 1.38291 to 1.33327, saving model to weights.best.letters.iv3.hdf5
Epoch 00030: val_loss did not improve
Epoch 00031: val_loss did not improve
Epoch 00032: val_loss did not improve
Epoch 00033: val_loss improved from 1.33327 to 1.30414, saving model to weights.best.letters.iv3.hdf5
Epoch 00034: val_loss did not improve
Epoch 00035: val_loss improved from 1.30414 to 1.27271, saving model to weights.best.letters.iv3.hdf5
Epoch 00036: val_loss did not improve
Epoch 00037: val_loss did not improve
Epoch 00038: val_loss did not improve
Epoch 00039: val_loss did not improve
Epoch 00040: val_loss did not improve
Epoch 00041: val_loss did not improve
Epoch 00042: val_loss did not improve
Epoch 00043: val_loss did not improve
Epoch 00044: val_loss did not improve
Epoch 00045: val_loss did not improve
Epoch 00046: val_loss improved from 1.27271 to 1.25457, saving model to weights.best.letters.iv3.hdf5
Epoch 00047: val_loss did not improve
Epoch 00048: val_loss did not improve
Epoch 00049: val_loss did not improve
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot the training history</span>
<span class="n">history_plot</span><span class="p">(</span><span class="n">iv3_history</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">iv3_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.iv3.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">iv3_scores</span> <span class="o">=</span> <span class="n">iv3_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test_bn</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">iv3_scores</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="n">iv3_scores</span>
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<pre>759/759 [==============================] - 0s     
Accuracy: 65.61%
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<pre>[1.2880885231950381, 0.65612648229196924]</pre>
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<h3 id="Grayscaled-Images">Grayscaled Images<a class="anchor-link" href="#Grayscaled-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create bottleneck features</span>
<span class="n">resize_x_train2</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_train2_color</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_train2_color</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">resize_x_test2</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_test2_color</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                           <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_test2_color</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">resize_x_valid2</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">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">x_valid2_color</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span><span class="mi">139</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> 
                           <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_valid2_color</span><span class="p">))])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>

<span class="n">iv3_x_train2</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_train2</span><span class="p">)</span>
<span class="n">iv3_x_test2</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_test2</span><span class="p">)</span>
<span class="n">iv3_x_valid2</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">resize_x_valid2</span><span class="p">)</span>

<span class="n">iv3_base_model2</span> <span class="o">=</span> <span class="n">InceptionV3</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">x_train_bn2</span> <span class="o">=</span> <span class="n">iv3_base_model2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_train2</span><span class="p">)</span>
<span class="n">x_test_bn2</span> <span class="o">=</span> <span class="n">iv3_base_model2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_test2</span><span class="p">)</span>
<span class="n">x_valid_bn2</span> <span class="o">=</span> <span class="n">iv3_base_model2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">iv3_x_valid2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Save bottleneck features</span>
<span class="n">x_train_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_train_bn2</span><span class="p">)</span>
<span class="n">x_test_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_test_bn2</span><span class="p">)</span>
<span class="n">x_valid_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x_valid_bn2</span><span class="p">)</span>

<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_train_bn2.npy&#39;</span><span class="p">,</span> <span class="n">x_train_bn2</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_test_bn2.npy&#39;</span><span class="p">,</span> <span class="n">x_test_bn2</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;x_valid_bn2.npy&#39;</span><span class="p">,</span> <span class="n">x_valid_bn2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load bottleneck features</span>
<span class="n">x_train_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_train_bn2.npy&#39;</span><span class="p">)</span>
<span class="n">x_test_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_test_bn2.npy&#39;</span><span class="p">)</span>
<span class="n">x_valid_bn2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;x_valid_bn2.npy&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">iv3_gray_model</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
    <span class="c1"># TODO: Define a model architecture</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">x_train_bn2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
        
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>
    <span class="c1"># TODO: Compile the model     </span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">iv3_gray_model</span> <span class="o">=</span> <span class="n">iv3_gray_model</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the model </span>
<span class="n">iv3_gray_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.letters.iv3_gray.hdf5&#39;</span><span class="p">,</span> 
                                        <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">iv3_gray_history</span> <span class="o">=</span> <span class="n">iv3_gray_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train_bn2</span><span class="p">,</span> <span class="n">y_train2</span><span class="p">,</span> 
                                      <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_valid_bn2</span><span class="p">,</span> <span class="n">y_valid2</span><span class="p">),</span>
                                      <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> 
                                      <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">iv3_gray_checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">);</span>
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<pre>Epoch 00000: val_loss improved from inf to 2.65396, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00001: val_loss improved from 2.65396 to 2.24445, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00002: val_loss improved from 2.24445 to 2.00583, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00003: val_loss improved from 2.00583 to 1.98057, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00004: val_loss improved from 1.98057 to 1.65476, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00005: val_loss did not improve
Epoch 00006: val_loss improved from 1.65476 to 1.36385, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00007: val_loss improved from 1.36385 to 1.23775, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00008: val_loss did not improve
Epoch 00009: val_loss did not improve
Epoch 00010: val_loss did not improve
Epoch 00011: val_loss improved from 1.23775 to 1.22006, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00012: val_loss did not improve
Epoch 00013: val_loss improved from 1.22006 to 1.09533, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00014: val_loss did not improve
Epoch 00015: val_loss did not improve
Epoch 00016: val_loss did not improve
Epoch 00017: val_loss did not improve
Epoch 00018: val_loss did not improve
Epoch 00019: val_loss did not improve
Epoch 00020: val_loss did not improve
Epoch 00021: val_loss did not improve
Epoch 00022: val_loss improved from 1.09533 to 1.02848, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00023: val_loss did not improve
Epoch 00024: val_loss did not improve
Epoch 00025: val_loss did not improve
Epoch 00026: val_loss improved from 1.02848 to 1.01993, saving model to weights.best.letters.iv3_gray.hdf5
Epoch 00027: val_loss did not improve
Epoch 00028: val_loss did not improve
Epoch 00029: val_loss did not improve
Epoch 00030: val_loss did not improve
Epoch 00031: val_loss did not improve
Epoch 00032: val_loss did not improve
Epoch 00033: val_loss did not improve
Epoch 00034: val_loss did not improve
Epoch 00035: val_loss did not improve
Epoch 00036: val_loss did not improve
Epoch 00037: val_loss did not improve
Epoch 00038: val_loss did not improve
Epoch 00039: val_loss did not improve
Epoch 00040: val_loss did not improve
Epoch 00041: val_loss did not improve
Epoch 00042: val_loss did not improve
Epoch 00043: val_loss did not improve
Epoch 00044: val_loss did not improve
Epoch 00045: val_loss did not improve
Epoch 00046: val_loss did not improve
Epoch 00047: val_loss did not improve
Epoch 00048: val_loss did not improve
Epoch 00049: val_loss did not improve
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[47]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot the training history</span>
<span class="n">history_plot</span><span class="p">(</span><span class="n">iv3_gray_history</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

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>
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<div class="prompt input_prompt">In&nbsp;[48]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the model with the best validation accuracy</span>
<span class="n">iv3_gray_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.letters.iv3_gray.hdf5&#39;</span><span class="p">)</span>
<span class="c1"># Calculate classification accuracy on the testing set</span>
<span class="n">iv3_gray_scores</span> <span class="o">=</span> <span class="n">iv3_gray_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test_bn2</span><span class="p">,</span> <span class="n">y_test2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">iv3_gray_scores</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="n">iv3_gray_scores</span>
</pre></div>

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<pre>736/759 [============================&gt;.] - ETA: 0sAccuracy: 66.01%
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<div class="prompt output_prompt">Out[48]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[1.1513739697075644, 0.66007905177605164]</pre>
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<h2 id="Step-7.-Predictions">Step 7. Predictions<a class="anchor-link" href="#Step-7.-Predictions">&#182;</a></h2><p>Display predictions for the models with the best accuracy.</p>
<h3 id="Color-Images">Color Images<a class="anchor-link" href="#Color-Images">&#182;</a></h3>
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<div class="prompt input_prompt">In&nbsp;[49]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a list of symbols</span>
<span class="n">symbols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;а&#39;</span><span class="p">,</span><span class="s1">&#39;б&#39;</span><span class="p">,</span><span class="s1">&#39;в&#39;</span><span class="p">,</span><span class="s1">&#39;г&#39;</span><span class="p">,</span><span class="s1">&#39;д&#39;</span><span class="p">,</span><span class="s1">&#39;е&#39;</span><span class="p">,</span><span class="s1">&#39;ё&#39;</span><span class="p">,</span><span class="s1">&#39;ж&#39;</span><span class="p">,</span><span class="s1">&#39;з&#39;</span><span class="p">,</span><span class="s1">&#39;и&#39;</span><span class="p">,</span><span class="s1">&#39;й&#39;</span><span class="p">,</span>
           <span class="s1">&#39;к&#39;</span><span class="p">,</span><span class="s1">&#39;л&#39;</span><span class="p">,</span><span class="s1">&#39;м&#39;</span><span class="p">,</span><span class="s1">&#39;н&#39;</span><span class="p">,</span><span class="s1">&#39;о&#39;</span><span class="p">,</span><span class="s1">&#39;п&#39;</span><span class="p">,</span><span class="s1">&#39;р&#39;</span><span class="p">,</span><span class="s1">&#39;с&#39;</span><span class="p">,</span><span class="s1">&#39;т&#39;</span><span class="p">,</span><span class="s1">&#39;у&#39;</span><span class="p">,</span><span class="s1">&#39;ф&#39;</span><span class="p">,</span>
           <span class="s1">&#39;х&#39;</span><span class="p">,</span><span class="s1">&#39;ц&#39;</span><span class="p">,</span><span class="s1">&#39;ч&#39;</span><span class="p">,</span><span class="s1">&#39;ш&#39;</span><span class="p">,</span><span class="s1">&#39;щ&#39;</span><span class="p">,</span><span class="s1">&#39;ъ&#39;</span><span class="p">,</span><span class="s1">&#39;ы&#39;</span><span class="p">,</span><span class="s1">&#39;ь&#39;</span><span class="p">,</span><span class="s1">&#39;э&#39;</span><span class="p">,</span><span class="s1">&#39;ю&#39;</span><span class="p">,</span><span class="s1">&#39;я&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Model predictions for the testing dataset</span>
<span class="n">y_test_predict</span> <span class="o">=</span> <span class="n">iv3_model</span><span class="o">.</span><span class="n">predict_classes</span><span class="p">(</span><span class="n">x_test_bn</span><span class="p">)</span>
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<pre>759/759 [==============================] - 60s    
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Display true labels and predictions</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">18</span><span class="p">,</span> <span class="mi">18</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">x_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</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">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">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">ax</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">squeeze</span><span class="p">(</span><span class="n">x_test</span><span class="p">[</span><span class="n">idx</span><span class="p">]))</span>
    <span class="n">pred_idx</span> <span class="o">=</span> <span class="n">y_test_predict</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
    <span class="n">true_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">symbols</span><span class="p">[</span><span class="n">pred_idx</span><span class="p">],</span> <span class="n">symbols</span><span class="p">[</span><span class="n">true_idx</span><span class="p">]),</span>
                 <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;#4876ff&quot;</span> <span class="k">if</span> <span class="n">pred_idx</span> <span class="o">==</span> <span class="n">true_idx</span> <span class="k">else</span> <span class="s2">&quot;darkred&quot;</span><span class="p">))</span>
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<h3 id="Grayscaled-Images">Grayscaled Images<a class="anchor-link" href="#Grayscaled-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Model predictions for the testing dataset</span>
<span class="n">y_test2_predict</span> <span class="o">=</span> <span class="n">gray_model</span><span class="o">.</span><span class="n">predict_classes</span><span class="p">(</span><span class="n">x_test2</span><span class="p">)</span>
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<pre>720/720 [==============================] - 4s     
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Display true labels and predictions</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">18</span><span class="p">,</span> <span class="mi">18</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">x_test2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</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">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">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">ax</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">squeeze</span><span class="p">(</span><span class="n">x_test2</span><span class="p">[</span><span class="n">idx</span><span class="p">]),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">bone</span><span class="p">)</span>
    <span class="n">pred_idx</span> <span class="o">=</span> <span class="n">y_test2_predict</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
    <span class="n">true_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test2</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">symbols</span><span class="p">[</span><span class="n">pred_idx</span><span class="p">],</span> <span class="n">symbols</span><span class="p">[</span><span class="n">true_idx</span><span class="p">]),</span>
                 <span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;#4876ff&quot;</span> <span class="k">if</span> <span class="n">pred_idx</span> <span class="o">==</span> <span class="n">true_idx</span> <span class="k">else</span> <span class="s2">&quot;darkred&quot;</span><span class="p">))</span>
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