report.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="Artificial-Intelligence-Nanodegree">Artificial Intelligence Nanodegree<a class="anchor-link" href="#Artificial-Intelligence-Nanodegree">&#182;</a></h1><h2 id="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">&#182;</a></h2><h2 id="Project:-Write-an-Algorithm-for-a-Dog-Identification-App">Project: Write an Algorithm for a Dog Identification App<a class="anchor-link" href="#Project:-Write-an-Algorithm-for-a-Dog-Identification-App">&#182;</a></h2><hr>
<p>In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'(IMPLEMENTATION)'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
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<p>The rubric contains <em>optional</em> "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.</p>
<hr>
<h3 id="Why-We're-Here">Why We're Here<a class="anchor-link" href="#Why-We're-Here">&#182;</a></h3><p>In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).</p>
<p><img src="images/sample_dog_output.png" alt="Sample Dog Output"></p>
<p>In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!</p>
<h3 id="The-Road-Ahead">The Road Ahead<a class="anchor-link" href="#The-Road-Ahead">&#182;</a></h3><p>We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.</p>
<ul>
<li><a href="#step0">Step 0</a>: Import Datasets</li>
<li><a href="#step1">Step 1</a>: Detect Humans</li>
<li><a href="#step2">Step 2</a>: Detect Dogs</li>
<li><a href="#step3">Step 3</a>: Create a CNN to Classify Dog Breeds (from Scratch)</li>
<li><a href="#step4">Step 4</a>: Use a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step5">Step 5</a>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step6">Step 6</a>: Write your Algorithm</li>
<li><a href="#step7">Step 7</a>: Test Your Algorithm</li>
</ul>
<hr>
<p><a id='step0'></a></p>
<h2 id="Step-0:-Import-Datasets">Step 0: Import Datasets<a class="anchor-link" href="#Step-0:-Import-Datasets">&#182;</a></h2><h3 id="Import-Dog-Dataset">Import Dog Dataset<a class="anchor-link" href="#Import-Dog-Dataset">&#182;</a></h3><p>In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the <code>load_files</code> function from the scikit-learn library:</p>
<ul>
<li><code>train_files</code>, <code>valid_files</code>, <code>test_files</code> - numpy arrays containing file paths to images</li>
<li><code>train_targets</code>, <code>valid_targets</code>, <code>test_targets</code> - numpy arrays containing onehot-encoded classification labels </li>
<li><code>dog_names</code> - list of string-valued dog breed names for translating labels</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_files</span>       
<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">np_utils</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>

<span class="c1"># define function to load train, test, and validation datasets</span>
<span class="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">load_files</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
    <span class="n">dog_files</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">data</span><span class="p">[</span><span class="s1">&#39;filenames&#39;</span><span class="p">])</span>
    <span class="n">dog_targets</span> <span class="o">=</span> <span class="n">np_utils</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">data</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]),</span> <span class="mi">133</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">dog_files</span><span class="p">,</span> <span class="n">dog_targets</span>

<span class="c1"># load train, test, and validation datasets</span>
<span class="n">train_files</span><span class="p">,</span> <span class="n">train_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;dogImages/train&#39;</span><span class="p">)</span>
<span class="n">valid_files</span><span class="p">,</span> <span class="n">valid_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;dogImages/valid&#39;</span><span class="p">)</span>
<span class="n">test_files</span><span class="p">,</span> <span class="n">test_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;dogImages/test&#39;</span><span class="p">)</span>

<span class="c1"># load list of dog names</span>
<span class="n">dog_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">20</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;dogImages/train/*/&quot;</span><span class="p">))]</span>

<span class="c1"># print statistics about the dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total dog categories.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">dog_names</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%s</span><span class="s1"> total dog images.</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">train_files</span><span class="p">,</span> <span class="n">valid_files</span><span class="p">,</span> <span class="n">test_files</span><span class="p">])))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> training dog images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_files</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> validation dog images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_files</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> test dog images.&#39;</span><span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_files</span><span class="p">))</span>
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<pre>There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.
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<h3 id="Import-Human-Dataset">Import Human Dataset<a class="anchor-link" href="#Import-Human-Dataset">&#182;</a></h3><p>In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array <code>human_files</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">8675309</span><span class="p">)</span>

<span class="c1"># load filenames in shuffled human dataset</span>
<span class="n">human_files</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">glob</span><span class="p">(</span><span class="s2">&quot;lfw/*/*&quot;</span><span class="p">))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">human_files</span><span class="p">)</span>

<span class="c1"># print statistics about the dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total human images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">human_files</span><span class="p">))</span>
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<pre>There are 13233 total human images.
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<p><a id='step1'></a></p>
<h2 id="Step-1:-Detect-Humans">Step 1: Detect Humans<a class="anchor-link" href="#Step-1:-Detect-Humans">&#182;</a></h2><p>We use OpenCV's implementation of <a href="http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html">Haar feature-based cascade classifiers</a> to detect human faces in images.  OpenCV provides many pre-trained face detectors, stored as XML files on <a href="https://github.com/opencv/opencv/tree/master/data/haarcascades">github</a>.  We have downloaded one of these detectors and stored it in the <code>haarcascades</code> directory.</p>
<p>In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">cv2</span>                
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>                        
<span class="o">%</span><span class="k">matplotlib</span> inline                               

<span class="c1"># extract pre-trained face detector</span>
<span class="n">face_cascade</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">CascadeClassifier</span><span class="p">(</span><span class="s1">&#39;haarcascades/haarcascade_frontalface_alt.xml&#39;</span><span class="p">)</span>

<span class="c1"># load color (BGR) image</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="n">human_files</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="c1"># convert BGR image to grayscale</span>
<span class="n">gray</span> <span class="o">=</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_BGR2GRAY</span><span class="p">)</span>

<span class="c1"># find faces in image</span>
<span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>

<span class="c1"># print number of faces detected in the image</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of faces detected:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">))</span>

<span class="c1"># get bounding box for each detected face</span>
<span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">w</span><span class="p">,</span><span class="n">h</span><span class="p">)</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span>
    <span class="c1"># add bounding box to color image</span>
    <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">img</span><span class="p">,(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">),(</span><span class="n">x</span><span class="o">+</span><span class="n">w</span><span class="p">,</span><span class="n">y</span><span class="o">+</span><span class="n">h</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
    
<span class="c1"># convert BGR image to RGB for plotting</span>
<span class="n">cv_rgb</span> <span class="o">=</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="c1"># display the image, along with bounding box</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv_rgb</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Number of faces detected: 1
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<p>Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The <code>detectMultiScale</code> function executes the classifier stored in <code>face_cascade</code> and takes the grayscale image as a parameter.</p>
<p>In the above code, <code>faces</code> is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as <code>x</code> and <code>y</code>) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as <code>w</code> and <code>h</code>) specify the width and height of the box.</p>
<h3 id="Write-a-Human-Face-Detector">Write a Human Face Detector<a class="anchor-link" href="#Write-a-Human-Face-Detector">&#182;</a></h3><p>We can use this procedure to write a function that returns <code>True</code> if a human face is detected in an image and <code>False</code> otherwise.  This function, aptly named <code>face_detector</code>, takes a string-valued file path to an image as input and appears in the code block below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># returns &quot;True&quot; if face is detected in image stored at img_path</span>
<span class="k">def</span> <span class="nf">face_detector</span><span class="p">(</span><span class="n">img_path</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="n">img_path</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</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_BGR2GRAY</span><span class="p">)</span>
    <span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
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<h3 id="(IMPLEMENTATION)-Assess-the-Human-Face-Detector">(IMPLEMENTATION) Assess the Human Face Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Human-Face-Detector">&#182;</a></h3><p><strong>Question 1:</strong> Use the code cell below to test the performance of the <code>face_detector</code> function.</p>
<ul>
<li>What percentage of the first 100 images in <code>human_files</code> have a detected human face?  </li>
<li>What percentage of the first 100 images in <code>dog_files</code> have a detected human face? </li>
</ul>
<p>Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays <code>human_files_short</code> and <code>dog_files_short</code>.</p>
<p><strong>Answer:</strong></p>
<ul>
<li>Faces detected in 98.000% of human images</li>
<li>Faces detected in 11.000% of dog images</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">train_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="c1"># Do NOT modify the code above this line.</span>
<span class="c1">## TODO: Test the performance of the face_detector algorithm </span>
<span class="c1">## on the images in human_files_short and dog_files_short.</span>

<span class="k">def</span> <span class="nf">detector_performance_measurer</span><span class="p">(</span><span class="n">detector</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="n">files</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span><span class="o">*</span><span class="nb">sum</span><span class="p">(</span><span class="n">detector</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">files</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">))</span><span class="o">*</span><span class="mi">100</span>
    <span class="k">return</span> <span class="n">inner</span>

<span class="n">files_with_faces</span> <span class="o">=</span> <span class="n">detector_performance_measurer</span><span class="p">(</span><span class="n">face_detector</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of dog images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">))</span>
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<pre>Faces detected in 98.000% of human images
Faces detected in 11.000% of dog images
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<p><strong>Question 2:</strong> This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?</p>
<p><strong>Answer:</strong></p>
<p>We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this <em>optional</em> task, report performance on each of the datasets.</p>
<p>I composed dataset containing 10 images of human face profiles. I'm going to use OpenCV metrics as a benchmark:</p>
<p><strong>OpenCV</strong></p>
<ul>
<li>Faces detected in 99.000% of human images</li>
<li>Faces detected in 11.000% of dog images</li>
<li>Face profiles detected in 20% of 10 human profile images</li>
</ul>
<p>I tried to use two other implementations to detect faces with the following results:</p>
<p><strong>OpenFace</strong> <a href="https://github.com/cmusatyalab/openface">https://github.com/cmusatyalab/openface</a></p>
<ul>
<li>Faces detected in 99.000% of human images</li>
<li>Faces detected in 11.000% of dog images</li>
<li>Face profiles detected in 40% of 10 human profile images</li>
</ul>
<p>This approach achieves 99% accuracy for detecting frontal faces (over 98% for OpenCV implementation). It also bumps face profile recognition to 40%.
It keeps false positives at 11%, which is the same as OpenCV-based implementation</p>
<p><strong>FaceNet</strong> <a href="https://github.com/davidsandberg/facenet">https://github.com/davidsandberg/facenet</a></p>
<ul>
<li>Faces detected in 100.000% of human images</li>
<li>Faces detected in 25.000% of dog images</li>
<li>Face profiles detected in 80% of 10 human profile images</li>
</ul>
<p>This approach could recognize faces in 100% of human images, but false positives are high at 25%.
This suggests that this alrorithms is much more "forgiving", therefore it could recognize faces profiles in 80% of cases.</p>
<p>It's worth noting that both approaches named OpenFace and FaceNet above are based on FaceNet paper from Google: <a href="https://arxiv.org/abs/1503.03832">https://arxiv.org/abs/1503.03832</a>. Also, both of them are using dlib library.</p>

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<h1 id="cmusatyalab/openface">cmusatyalab/openface<a class="anchor-link" href="#cmusatyalab/openface">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## (Optional) TODO: Report the performance of another  </span>
<span class="c1">## face detection algorithm on the LFW dataset</span>
<span class="c1">### Feel free to use as many code cells as needed</span>

<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">os</span>

<span class="kn">import</span> <span class="nn">openface</span>


<span class="n">modelDir</span> <span class="o">=</span> <span class="s1">&#39;/home/ubuntu/openface/models&#39;</span>
<span class="n">dlibModelDir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">modelDir</span><span class="p">,</span> <span class="s1">&#39;dlib&#39;</span><span class="p">)</span>
<span class="n">openfaceModelDir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">modelDir</span><span class="p">,</span> <span class="s1">&#39;openface&#39;</span><span class="p">)</span>
<span class="n">align</span> <span class="o">=</span> <span class="n">openface</span><span class="o">.</span><span class="n">AlignDlib</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dlibModelDir</span><span class="p">,</span> <span class="s1">&#39;shape_predictor_68_face_landmarks.dat&#39;</span><span class="p">))</span>

<span class="k">def</span> <span class="nf">get_face_dlib</span><span class="p">(</span><span class="n">imgPath</span><span class="p">):</span>
    <span class="n">bgrImg</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="n">imgPath</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">bgrImg</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Unable to load image: </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">imgPath</span><span class="p">))</span>

    <span class="n">rgbImg</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">bgrImg</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">bb1</span> <span class="o">=</span> <span class="n">align</span><span class="o">.</span><span class="n">getLargestFaceBoundingBox</span><span class="p">(</span><span class="n">rgbImg</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">bb1</span>

<span class="k">def</span> <span class="nf">face_detector_dlib</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">get_face_dlib</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_openface</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of dog images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_openface</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">))</span>
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<pre>Faces detected in 99.000% of human images
Faces detected in 11.000% of dog images
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">profiles</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;user-uploads/human_profile</span><span class="si">%s</span><span class="s1">.jpg&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Face profiles detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_openface</span><span class="p">(</span><span class="n">profiles</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Face profiles detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces</span><span class="p">(</span><span class="n">profiles</span><span class="p">))</span>
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<pre>Face profiles detected in 40.000% of human images
Face profiles detected in 20.000% of human images
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<h1 id="davidsandberg/facenet">davidsandberg/facenet<a class="anchor-link" href="#davidsandberg/facenet">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">facenet.src</span> <span class="k">import</span> <span class="n">align</span> <span class="k">as</span> <span class="n">facenet_align</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">scipy</span> <span class="k">import</span> <span class="n">misc</span>

<span class="kn">from</span> <span class="nn">facenet.src.align</span> <span class="k">import</span> <span class="n">detect_face</span>
<span class="kn">import</span> <span class="nn">facenet</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">misc</span>

<span class="n">gpu_memory_fraction</span> <span class="o">=</span> <span class="mf">0.3</span>

<span class="k">class</span> <span class="nc">Detection</span><span class="p">:</span>
    <span class="c1"># face detection parameters</span>
    <span class="n">minsize</span> <span class="o">=</span> <span class="mi">20</span>  <span class="c1"># minimum size of face</span>
    <span class="n">threshold</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">]</span>  <span class="c1"># three steps&#39;s threshold</span>
    <span class="n">factor</span> <span class="o">=</span> <span class="mf">0.709</span>  <span class="c1"># scale factor</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">face_crop_size</span><span class="o">=</span><span class="mi">160</span><span class="p">,</span> <span class="n">face_crop_margin</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pnet</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnet</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">onet</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_setup_mtcnn</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">face_crop_size</span> <span class="o">=</span> <span class="n">face_crop_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">face_crop_margin</span> <span class="o">=</span> <span class="n">face_crop_margin</span>

    <span class="k">def</span> <span class="nf">_setup_mtcnn</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
            <span class="n">gpu_options</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">GPUOptions</span><span class="p">(</span><span class="n">per_process_gpu_memory_fraction</span><span class="o">=</span><span class="n">gpu_memory_fraction</span><span class="p">)</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="n">config</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ConfigProto</span><span class="p">(</span><span class="n">gpu_options</span><span class="o">=</span><span class="n">gpu_options</span><span class="p">,</span> <span class="n">log_device_placement</span><span class="o">=</span><span class="kc">False</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="k">return</span> <span class="n">detect_face</span><span class="o">.</span><span class="n">create_mtcnn</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">find_faces</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image</span><span class="p">):</span>
        <span class="n">faces</span> <span class="o">=</span> <span class="p">[]</span>

        <span class="n">bounding_boxes</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">facenet_align</span><span class="o">.</span><span class="n">detect_face</span><span class="o">.</span><span class="n">detect_face</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">minsize</span><span class="p">,</span>
                                                          <span class="bp">self</span><span class="o">.</span><span class="n">pnet</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnet</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">onet</span><span class="p">,</span>
                                                          <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">factor</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">bounding_boxes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">bounding_boxes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>

<span class="k">def</span> <span class="nf">face_detector_facenet</span><span class="p">():</span>
    <span class="n">d</span> <span class="o">=</span> <span class="n">Detection</span><span class="p">()</span>
    <span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="n">img_path</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="n">img_path</span><span class="p">)</span>
        <span class="n">rgbImg</span> <span class="o">=</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="k">return</span> <span class="n">d</span><span class="o">.</span><span class="n">find_faces</span><span class="p">(</span><span class="n">rgbImg</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">inner</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">files_with_faces_facenet</span> <span class="o">=</span> <span class="n">detector_performance_measurer</span><span class="p">(</span><span class="n">face_detector_facenet</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_facenet</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of dog images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_facenet</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">))</span>
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<pre>Faces detected in 100.000% of human images
Faces detected in 25.000% of dog images
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Faces detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_faces_facenet</span><span class="p">(</span><span class="n">profiles</span><span class="p">))</span>
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<pre>Faces detected in 80.000% of human images
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<p><a id='step2'></a></p>
<h2 id="Step-2:-Detect-Dogs">Step 2: Detect Dogs<a class="anchor-link" href="#Step-2:-Detect-Dogs">&#182;</a></h2><p>In this section, we use a pre-trained <a href="http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006">ResNet-50</a> model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on <a href="http://www.image-net.org/">ImageNet</a>, a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">1000 categories</a>.  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">ResNet50</span>

<span class="c1"># define ResNet50 model</span>
<span class="n">ResNet50_model</span> <span class="o">=</span> <span class="n">ResNet50</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>
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<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape</p>
<p>$$
(\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}),
$$</p>
<p>where <code>nb_samples</code> corresponds to the total number of images (or samples), and <code>rows</code>, <code>columns</code>, and <code>channels</code> correspond to the number of rows, columns, and channels for each image, respectively.</p>
<p>The <code>path_to_tensor</code> function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape</p>
<p>$$
(1, 224, 224, 3).
$$</p>
<p>The <code>paths_to_tensor</code> function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape</p>
<p>$$
(\text{nb_samples}, 224, 224, 3).
$$</p>
<p>Here, <code>nb_samples</code> is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of <code>nb_samples</code> as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span>                  
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>

<span class="k">def</span> <span class="nf">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># loads RGB image as PIL.Image.Image type</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</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">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
    <span class="c1"># convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">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="c1"># convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor</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="k">def</span> <span class="nf">paths_to_tensor</span><span class="p">(</span><span class="n">img_paths</span><span class="p">):</span>
    <span class="n">list_of_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_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>
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<h3 id="Making-Predictions-with-ResNet-50">Making Predictions with ResNet-50<a class="anchor-link" href="#Making-Predictions-with-ResNet-50">&#182;</a></h3><p>Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing.  First, the RGB image is converted to BGR by reordering the channels.  All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image.  This is implemented in the imported function <code>preprocess_input</code>.  If you're curious, you can check the code for <code>preprocess_input</code> <a href="https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py">here</a>.</p>
<p>Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions.  This is accomplished with the <code>predict</code> method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category.  This is implemented in the <code>ResNet50_predict_labels</code> function below.</p>
<p>By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">preprocess_input</span><span class="p">,</span> <span class="n">decode_predictions</span>

<span class="k">def</span> <span class="nf">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># returns prediction vector for image located at img_path</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ResNet50_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
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<h3 id="Write-a-Dog-Detector">Write a Dog Detector<a class="anchor-link" href="#Write-a-Dog-Detector">&#182;</a></h3><p>While looking at the <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from <code>'Chihuahua'</code> to <code>'Mexican hairless'</code>.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the <code>ResNet50_predict_labels</code> function above returns a value between 151 and 268 (inclusive).</p>
<p>We use these ideas to complete the <code>dog_detector</code> function below, which returns <code>True</code> if a dog is detected in an image (and <code>False</code> if not).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### returns &quot;True&quot; if a dog is detected in the image stored at img_path</span>
<span class="k">def</span> <span class="nf">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="n">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="k">return</span> <span class="p">((</span><span class="n">prediction</span> <span class="o">&lt;=</span> <span class="mi">268</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">prediction</span> <span class="o">&gt;=</span> <span class="mi">151</span><span class="p">))</span> 
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<h3 id="(IMPLEMENTATION)-Assess-the-Dog-Detector">(IMPLEMENTATION) Assess the Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Dog-Detector">&#182;</a></h3><p><strong>Question 3:</strong> Use the code cell below to test the performance of your <code>dog_detector</code> function.</p>
<ul>
<li>What percentage of the images in <code>human_files_short</code> have a detected dog?  </li>
<li>What percentage of the images in <code>dog_files_short</code> have a detected dog?</li>
</ul>
<p><strong>Answer:</strong></p>
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<li>Dogs detected in 1.000% of human images</li>
<li>Dogs detected in 100.000% of dog images</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Test the performance of the dog_detector function</span>
<span class="c1">### on the images in human_files_short and dog_files_short.</span>
<span class="n">files_with_dogs</span> <span class="o">=</span> <span class="n">detector_performance_measurer</span><span class="p">(</span><span class="n">dog_detector</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dogs detected in </span><span class="si">%2.3f%%</span><span class="s2"> of human images&quot;</span> <span class="o">%</span> <span class="n">files_with_dogs</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dogs detected in </span><span class="si">%2.3f%%</span><span class="s2"> of dog images&quot;</span> <span class="o">%</span> <span class="n">files_with_dogs</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">))</span>
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<pre>Dogs detected in 1.000% of human images
Dogs detected in 100.000% of dog images
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<p><a id='step3'></a></p>
<h2 id="Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">Step 3: Create a CNN to Classify Dog Breeds (from Scratch)<a class="anchor-link" href="#Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">&#182;</a></h2><p>Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN <em>from scratch</em> (so, you can't use transfer learning <em>yet</em>!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</p>
<p>Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.</p>
<p>We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that <em>even a human</em> would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.</p>
<table>
<thead><tr>
<th>Brittany</th>
<th>Welsh Springer Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Brittany_02625.jpg" width="100"></td>
<td><img src="images/Welsh_springer_spaniel_08203.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).</p>
<table>
<thead><tr>
<th>Curly-Coated Retriever</th>
<th>American Water Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Curly-coated_retriever_03896.jpg" width="200"></td>
<td><img src="images/American_water_spaniel_00648.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.</p>
<table>
<thead><tr>
<th>Yellow Labrador</th>
<th>Chocolate Labrador</th>
<th>Black Labrador</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Labrador_retriever_06457.jpg" width="150"></td>
<td><img src="images/Labrador_retriever_06455.jpg" width="240"></td>
<td><img src="images/Labrador_retriever_06449.jpg" width="220"></td>
</tr>
</tbody>
</table>
<p>We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.</p>
<p>Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun!</p>
<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>We rescale the images by dividing every pixel in every image by 255.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</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>                 

<span class="c1"># pre-process the data for Keras</span>
<span class="n">train_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">train_files</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="o">/</span><span class="mi">255</span>
<span class="n">valid_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">valid_files</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="o">/</span><span class="mi">255</span>
<span class="n">test_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">test_files</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="o">/</span><span class="mi">255</span>
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<pre>100%|██████████| 6680/6680 [00:50&lt;00:00, 132.96it/s]
100%|██████████| 835/835 [00:05&lt;00:00, 146.84it/s]
100%|██████████| 836/836 [00:05&lt;00:00, 148.45it/s]
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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

<pre><code>    model.summary()

</code></pre>
<p>We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains &gt;1% test accuracy in 5 epochs:</p>
<p><img src="images/sample_cnn.png" alt="Sample CNN"></p>
<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.</p>
<p><strong>Answer:</strong></p>
<ul>
<li>MODEL 1: I started with very simple CNN (see Model 1), it yielded 1.1962% accuracy.</li>
<li>MODEL 2: I switched MaxPooling2D to AveragePooling2D because I felt it's better to smoothen fur color. It helped and yielded 6.6986% accuracy</li>
<li>MODEL 3: It's a good start, so I decided to add more Conv2D layers to detect more sophisticated patterns, it means I increased number of filters and reduced kernel_size. Also, I noticed, that previous model was improving until the last epoch, so it made sense to add more epochs until it starts to overfit.</li>
<li>MODELS 4, 5, and 8: Adding more Dense layers and Droput layers didn't help and reduced the accuracy of the model</li>
<li>MODEL 6: Going back to "base model 3" and trying add one more Conv2D layer, even more filters, even smaller kernel_size. Test accuracy 13.0383%</li>
<li>MODEL 7: Repeating the same steps, i.e adding more filters, reducing kernel size. This model yielded 14.7129% accuracy on a test set.</li>
<li><strong>MODEL 8 (BEST ONE)</strong>: Test accuracy <strong>15.6699%</strong>. Here I tried to identify patterns from small to big. In first layer, identify small number of small patterns. Then gradually, layer after layer, combine small patterns together to identify more more complex patterns. I also, removed 1 Dense layer in the end to help avoid overfitting.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></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">AveragePooling2D</span><span class="p">,</span> <span class="n">MaxPooling2D</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">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>

<span class="c1"># MODEL 1</span>
<span class="c1"># Simplest model: 5 epochs, 1.1962%</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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="mi">4</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">Flatten</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">512</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">Dense</span><span class="p">(</span><span class="mi">133</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"># MODEL 2</span>
<span class="c1"># Using AveragePooling2D: 5 epochs, 6.6986%</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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">Flatten</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">512</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">Dense</span><span class="p">(</span><span class="mi">133</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"># MODEL 3</span>
<span class="c1"># Adding Conv2D + GlobalAveragePooling layers, remove Flatten layer</span>
<span class="c1"># This model kept improving over 5 epochs, trying more epochs until it starts to overfit</span>
<span class="c1"># Same model with 10 epochs yielded 7.1770% accuracy. Yay!</span>
<span class="c1"># Trying more epochs, because it was still steadily improving even with 10</span>
<span class="c1"># With 20 epochs this model yields 10.7656% accuracy! But it struggles a bit,</span>
<span class="c1"># accuracy goes up and down towards the end. Can it be that Dense layers overfitting? Let&#39;s add Dropout!</span>
<span class="c1"># This model was kept improving even on 20 epochs. It has potential, need to try it with more epochs.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">GlobalAveragePooling2D</span><span class="p">())</span>
<span class="c1">#model.add(Flatten())</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">512</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">Dense</span><span class="p">(</span><span class="mi">133</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"># MODEL 4</span>
<span class="c1"># Adding Dropout units to the previous model. 20 epochs, only 7.1770% accuracy :(</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">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">Dense</span><span class="p">(</span><span class="mi">512</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">0.3</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">133</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"># MODEL 5</span>
<span class="c1"># Trying to adding one more dense layer, but with higher dropout to keep number of active nodes in Dense layers</span>
<span class="c1"># roughly the same</span>
<span class="c1"># Bad move, 6.2201%. Dropouts don&#39;t help here.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">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">Dense</span><span class="p">(</span><span class="mi">512</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">0.5</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">512</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">Dense</span><span class="p">(</span><span class="mi">133</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"># MODEL 6</span>
<span class="c1"># Adding one move Conv2D layer with even more features, and even smaller kernel size.</span>
<span class="c1"># Test accuracy: 13.0383% (Whaaat?!)</span>
<span class="c1"># And this model has potential, it was improving until the last epoch (20th)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</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="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</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="n">filters</span><span class="o">=</span><span class="mi">64</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">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">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">Dense</span><span class="p">(</span><span class="mi">512</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">Dense</span><span class="p">(</span><span class="mi">133</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"># MODEL 7</span>
<span class="c1"># Well the question is, what if I keep going into this direction? More Conv2D layers!</span>
<span class="c1"># Wow, it actually helped... Test accuracy: 14.7129%!</span>
<span class="k">def</span> <span class="nf">get_model_coarse_to_fine</span><span class="p">(</span><span class="n">input_shape</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="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">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="n">input_shape</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</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="n">filters</span><span class="o">=</span><span class="mi">64</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">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</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">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</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">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</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">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">Dense</span><span class="p">(</span><span class="mi">512</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">Dense</span><span class="p">(</span><span class="mi">133</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="k">return</span> <span class="n">model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">get_model_coarse_to_fine</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>

<span class="c1">########################################</span>
<span class="c1"># BEST MODEL. Test accuracy: 15.6699%! #</span>
<span class="c1">########################################</span>
<span class="c1"># MODEL 8</span>
<span class="c1"># Tried the opposite logic. First, identify small number of small patterns,</span>
<span class="c1"># then gradually, layer after layer, combine them together to identify more more complex patterns.</span>
<span class="c1"># Also, removing 1 Dense layer in the end to help avoid overfitting.</span>
<span class="k">def</span> <span class="nf">get_model_fine_to_coarse</span><span class="p">(</span><span class="n">input_shape</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="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">16</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">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">input_shape</span><span class="o">=</span><span class="n">input_shape</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="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">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">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">4</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="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;valid&#39;</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">AveragePooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">8</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="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">16</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">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">GlobalAveragePooling2D</span><span class="p">())</span>
    <span class="c1">#model.add(Dense(512, activation=&#39;relu&#39;))</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">133</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="k">return</span> <span class="n">model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">get_model_fine_to_coarse</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>

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

<span class="c1"># MODEL 8</span>
<span class="c1"># Again, adding more Dense layers didn&#39;t help. Test accuracy dropped: 9.8086%</span>
<span class="c1"># model = Sequential()</span>
<span class="c1">#model.add(Conv2D(filters=16, kernel_size=8, padding=&#39;valid&#39;, activation=&#39;relu&#39;, input_shape=(224, 224, 3)))</span>
<span class="c1">#model.add(AveragePooling2D(pool_size=8))</span>
<span class="c1">#model.add(Conv2D(filters=32, kernel_size=4, padding=&#39;valid&#39;, activation=&#39;relu&#39;))</span>
<span class="c1">#model.add(MaxPooling2D(pool_size=4))</span>
<span class="c1">#model.add(Conv2D(filters=64, kernel_size=2, padding=&#39;same&#39;, activation=&#39;relu&#39;))</span>
<span class="c1">#model.add(MaxPooling2D(pool_size=2))</span>
<span class="c1">#model.add(Conv2D(filters=128, kernel_size=2, padding=&#39;same&#39;, activation=&#39;relu&#39;))</span>
<span class="c1">#model.add(GlobalAveragePooling2D())</span>
<span class="c1">#model.add(Dropout(0.4))</span>
<span class="c1">#model.add(Dense(512, activation=&#39;relu&#39;))</span>
<span class="c1">#model.add(Dropout(0.4))</span>
<span class="c1">#model.add(Dense(512, activation=&#39;relu&#39;))</span>
<span class="c1">#model.add(Dense(133, activation=&#39;softmax&#39;))</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_36 (Conv2D)           (None, 223, 223, 16)      208       
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 111, 111, 16)      0         
_________________________________________________________________
conv2d_37 (Conv2D)           (None, 108, 108, 32)      8224      
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 27, 27, 32)        0         
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 20, 20, 64)        131136    
_________________________________________________________________
average_pooling2d_14 (Averag (None, 2, 2, 64)          0         
_________________________________________________________________
conv2d_39 (Conv2D)           (None, 2, 2, 128)         2097280   
_________________________________________________________________
global_average_pooling2d_13  (None, 128)               0         
_________________________________________________________________
dense_33 (Dense)             (None, 133)               17157     
=================================================================
Total params: 2,254,005
Trainable params: 2,254,005
Non-trainable params: 0
_________________________________________________________________
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<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</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">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#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>
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<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span><span class="p">,</span> <span class="n">EarlyStopping</span>

<span class="c1">### TODO: specify the number of epochs that you would like to use to train the model.</span>

<span class="n">epochs</span> <span class="o">=</span> <span class="mi">20</span>

<span class="c1">### Do NOT modify the code below this line.</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;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</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">early_stop</span> <span class="o">=</span> <span class="n">EarlyStopping</span><span class="p">(</span><span class="n">patience</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">,</span> <span class="n">valid_targets</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">batch_size</span><span class="o">=</span><span class="mi">20</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">early_stop</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.8842 - acc: 0.0096- ETA: 2s - loss: 4.8859 - acc: - ETA: Epoch 00000: val_loss improved from inf to 4.83806, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 24s - loss: 4.8839 - acc: 0.0099 - val_loss: 4.8381 - val_acc: 0.0168
Epoch 2/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.7450 - acc: 0.0168- ETA: 5s - loss: 4.7557 -  - ETA: 5s - loss: 4.7557 - acc - ETA: 4s - loss: 4.7549 - acc: 0.018 - ETA: 4s Epoch 00001: val_loss improved from 4.83806 to 4.73167, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 4.7442 - acc: 0.0169 - val_loss: 4.7317 - val_acc: 0.0156
Epoch 3/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.5264 - acc: 0.0312Epoch 00002: val_loss improved from 4.73167 to 4.61401, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 4.5269 - acc: 0.0313 - val_loss: 4.6140 - val_acc: 0.0263
Epoch 4/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.3236 - acc: 0.0486- ETA: Epoch 00003: val_loss improved from 4.61401 to 4.27503, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 4.3227 - acc: 0.0487 - val_loss: 4.2750 - val_acc: 0.0455
Epoch 5/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.1904 - acc: 0.0571Epoch 00004: val_loss improved from 4.27503 to 4.17899, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 4.1907 - acc: 0.0570 - val_loss: 4.1790 - val_acc: 0.0695
Epoch 6/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.0708 - acc: 0.0721- ETA: 9s - loss: 4.09 - ETA: 8s - loss: 4.0993 - acc: 0. - ETA: - ETA: 1s - loss: 4.071Epoch 00005: val_loss improved from 4.17899 to 4.07984, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 4.0709 - acc: 0.0722 - val_loss: 4.0798 - val_acc: 0.0683
Epoch 7/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.9474 - acc: 0.0824- ETA: 8s - loss: 3 - ETAEpoch 00006: val_loss improved from 4.07984 to 3.99205, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.9479 - acc: 0.0825 - val_loss: 3.9920 - val_acc: 0.0814
Epoch 8/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.8582 - acc: 0.0977  ETA: 10s - loss: 3.8639 - acc: 0 - ETA: 9s - loss:  - ETA: 5s - loss: 3.8666 -  - ETA: 5s - los - ETA: 0s - loss: 3.8566 Epoch 00007: val_loss improved from 3.99205 to 3.93554, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.8595 - acc: 0.0978 - val_loss: 3.9355 - val_acc: 0.0802
Epoch 9/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.7618 - acc: 0.1153- - ETA: 0s - loss: 3.7649 - acc: 0.Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.7619 - acc: 0.1151 - val_loss: 4.1321 - val_acc: 0.0778
Epoch 10/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.6791 - acc: 0.1221  ETA: 10s - loss: 3.6781 -  - ETA: 0s - loss: 3.6799 - acc: 0 - ETA: 0s - loss: 3.6807 - acc: 0. - ETA: 0s - loss: 3.6795 - acc: 0.122Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.6803 - acc: 0.1222 - val_loss: 4.0824 - val_acc: 0.0850
Epoch 11/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.6027 - acc: 0.1405- ETA: 3s - loss: - ETA: 2s  - ETA: 0s - loss: 3.6029 - acc: 0.1Epoch 00010: val_loss improved from 3.93554 to 3.91252, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.6037 - acc: 0.1403 - val_loss: 3.9125 - val_acc: 0.0934
Epoch 12/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.5249 - acc: 0.1521- ETA: 1s - loss: 3.52 - ETA: 0s - loss: 3.5251 - acc: 0.152Epoch 00011: val_loss improved from 3.91252 to 3.87105, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.5262 - acc: 0.1519 - val_loss: 3.8710 - val_acc: 0.0946
Epoch 13/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.4587 - acc: 0.1640Epoch 00012: val_loss improved from 3.87105 to 3.85523, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.4568 - acc: 0.1642 - val_loss: 3.8552 - val_acc: 0.1150
Epoch 14/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.3891 - acc: 0.1730Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.3898 - acc: 0.1729 - val_loss: 4.0286 - val_acc: 0.1078
Epoch 15/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.3248 - acc: 0.1797Epoch 00014: val_loss improved from 3.85523 to 3.69729, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.3256 - acc: 0.1793 - val_loss: 3.6973 - val_acc: 0.1090
Epoch 16/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.2540 - acc: 0.1973  ETA: 12s - loss: 3. - ETA: 12s  - ETA: 2s - ETA: 0s - loss: 3.2502 - acc: 0.1Epoch 00015: val_loss improved from 3.69729 to 3.67076, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 23s - loss: 3.2538 - acc: 0.1972 - val_loss: 3.6708 - val_acc: 0.1317
Epoch 17/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.1891 - acc: 0.2116Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.1865 - acc: 0.2121 - val_loss: 3.7719 - val_acc: 0.1377
Epoch 18/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.1283 - acc: 0.2231Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.1281 - acc: 0.2231 - val_loss: 3.9405 - val_acc: 0.1269
Epoch 19/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.0490 - acc: 0.2314- ETA: 1s - loss: 3.04Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 3.0489 - acc: 0.2316 - val_loss: 3.8862 - val_acc: 0.1269
Epoch 20/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 2.9990 - acc: 0.2431- ETA: 6s - loss: 3.Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 23s - loss: 2.9991 - acc: 0.2428 - val_loss: 3.8082 - val_acc: 0.1293
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<pre>&lt;keras.callbacks.History at 0x7f73d93d57f0&gt;</pre>
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<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">)</span>
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<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span>
<span class="n">dog_breed_predictions</span> <span class="o">=</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">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">tensor</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="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">test_tensors</span><span class="p">]</span>

<span class="c1"># report test accuracy</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</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">dog_breed_predictions</span><span class="p">)</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">test_targets</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="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 14.4737%
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<p><a id='step4'></a></p>
<h2 id="Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">Step 4: Use a CNN to Classify Dog Breeds<a class="anchor-link" href="#Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">&#182;</a></h2><p>To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.</p>
<h3 id="Obtain-Bottleneck-Features">Obtain Bottleneck Features<a class="anchor-link" href="#Obtain-Bottleneck-Features">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</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;bottleneck_features/DogVGG16Data.npz&#39;</span><span class="p">)</span>
<span class="n">train_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
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<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3><p>The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG16_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">input_shape</span><span class="o">=</span><span class="n">train_VGG16</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">VGG16_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">133</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">VGG16_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 #   
=================================================================
global_average_pooling2d_50  (None, 512)               0         
_________________________________________________________________
dense_141 (Dense)            (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________
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<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_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>
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<h3 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h3>
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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;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</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">VGG16_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_VGG16</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_VGG16</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</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">1</span><span class="p">)</span>
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<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 11.9523 - acc: 0.1320Epoch 00000: val_loss improved from inf to 10.32643, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s - loss: 11.9496 - acc: 0.1323 - val_loss: 10.3264 - val_acc: 0.2299
Epoch 2/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 9.5297 - acc: 0.3103Epoch 00001: val_loss improved from 10.32643 to 9.57603, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.5438 - acc: 0.3096 - val_loss: 9.5760 - val_acc: 0.3186
Epoch 3/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 9.1197 - acc: 0.3773Epoch 00002: val_loss improved from 9.57603 to 9.49363, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 9.1117 - acc: 0.3780 - val_loss: 9.4936 - val_acc: 0.3305
Epoch 4/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 8.9353 - acc: 0.4014Epoch 00003: val_loss improved from 9.49363 to 9.35800, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.9400 - acc: 0.4012 - val_loss: 9.3580 - val_acc: 0.3473
Epoch 5/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 8.8093 - acc: 0.4210Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 1s - loss: 8.8189 - acc: 0.4201 - val_loss: 9.3913 - val_acc: 0.3461
Epoch 6/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 8.7570 - acc: 0.4315Epoch 00005: val_loss improved from 9.35800 to 9.24581, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.7575 - acc: 0.4314 - val_loss: 9.2458 - val_acc: 0.3605
Epoch 7/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 8.6015 - acc: 0.4483Epoch 00006: val_loss improved from 9.24581 to 9.16547, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.6073 - acc: 0.4482 - val_loss: 9.1655 - val_acc: 0.3749
Epoch 8/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 8.5329 - acc: 0.4533Epoch 00007: val_loss improved from 9.16547 to 9.05800, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.5407 - acc: 0.4528 - val_loss: 9.0580 - val_acc: 0.3760
Epoch 9/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 8.4777 - acc: 0.4603Epoch 00008: val_loss improved from 9.05800 to 9.03290, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.4760 - acc: 0.4603 - val_loss: 9.0329 - val_acc: 0.3820
Epoch 10/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 8.4368 - acc: 0.4643Epoch 00009: val_loss improved from 9.03290 to 8.85899, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.4211 - acc: 0.4651 - val_loss: 8.8590 - val_acc: 0.3892
Epoch 11/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 8.2812 - acc: 0.4715Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 1s - loss: 8.2823 - acc: 0.4716 - val_loss: 8.9067 - val_acc: 0.3832
Epoch 12/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 8.1960 - acc: 0.4796Epoch 00011: val_loss improved from 8.85899 to 8.84589, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 8.1907 - acc: 0.4798 - val_loss: 8.8459 - val_acc: 0.3832
Epoch 13/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 7.9695 - acc: 0.4893Epoch 00012: val_loss improved from 8.84589 to 8.64602, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.9749 - acc: 0.4891 - val_loss: 8.6460 - val_acc: 0.3964
Epoch 14/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 7.8949 - acc: 0.4991Epoch 00013: val_loss improved from 8.64602 to 8.57044, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.9084 - acc: 0.4984 - val_loss: 8.5704 - val_acc: 0.4108
Epoch 15/20
6500/6680 [============================&gt;.] - ETA: 0s - loss: 7.8456 - acc: 0.4982Epoch 00014: val_loss improved from 8.57044 to 8.55966, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.8405 - acc: 0.4985 - val_loss: 8.5597 - val_acc: 0.3988
Epoch 16/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 7.6776 - acc: 0.5053Epoch 00015: val_loss improved from 8.55966 to 8.46670, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.6760 - acc: 0.5054 - val_loss: 8.4667 - val_acc: 0.3892
Epoch 17/20
6480/6680 [============================&gt;.] - ETA: 0s - loss: 7.5062 - acc: 0.5190Epoch 00016: val_loss improved from 8.46670 to 8.19694, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.5015 - acc: 0.5193 - val_loss: 8.1969 - val_acc: 0.4299
Epoch 18/20
6480/6680 [============================&gt;.] - ETA: 0s - loss: 7.4437 - acc: 0.5255Epoch 00017: val_loss improved from 8.19694 to 8.14135, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.4458 - acc: 0.5246 - val_loss: 8.1414 - val_acc: 0.4251
Epoch 19/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 7.3444 - acc: 0.5285Epoch 00018: val_loss improved from 8.14135 to 8.09858, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.3297 - acc: 0.5295 - val_loss: 8.0986 - val_acc: 0.4299
Epoch 20/20
6460/6680 [============================&gt;.] - ETA: 0s - loss: 7.1494 - acc: 0.5420Epoch 00019: val_loss improved from 8.09858 to 8.00958, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 1s - loss: 7.1656 - acc: 0.5412 - val_loss: 8.0096 - val_acc: 0.4383
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<pre>&lt;keras.callbacks.History at 0x7fd1586166a0&gt;</pre>
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<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">)</span>
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<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span>
<span class="n">VGG16_predictions</span> <span class="o">=</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">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</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="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_VGG16</span><span class="p">]</span>

<span class="c1"># report test accuracy</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</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">VGG16_predictions</span><span class="p">)</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">test_targets</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="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 43.7799%
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<h3 id="Predict-Dog-Breed-with-the-Model">Predict Dog Breed with the Model<a class="anchor-link" href="#Predict-Dog-Breed-with-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span>

<span class="k">def</span> <span class="nf">VGG16_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># extract bottleneck features</span>
    <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_VGG16</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="c1"># obtain predicted vector</span>
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span>
    <span class="c1"># return dog breed that is predicted by the model</span>
    <span class="k">return</span> <span class="n">dog_names</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">predicted_vector</span><span class="p">)]</span>
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<p><a id='step5'></a></p>
<h2 id="Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">&#182;</a></h2><p>You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.</p>
<p>In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:</p>
<ul>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz">VGG-19</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz">ResNet-50</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz">Inception</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz">Xception</a> bottleneck features</li>
</ul>
<p>The files are encoded as such:</p>

<pre><code>Dog{network}Data.npz

</code></pre>
<p>where <code>{network}</code>, in the above filename, can be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.  Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the <code>bottleneck_features/</code> folder in the repository.</p>
<h3 id="(IMPLEMENTATION)-Obtain-Bottleneck-Features">(IMPLEMENTATION) Obtain Bottleneck Features<a class="anchor-link" href="#(IMPLEMENTATION)-Obtain-Bottleneck-Features">&#182;</a></h3><p>In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:</p>

<pre><code>bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Obtain bottleneck features from another pre-trained CNN.</span>
<span class="n">bottleneck_features</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;bottleneck_features/DogVGG19Data.npz&#39;</span><span class="p">)</span>
<span class="n">train_VGG19</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_VGG19</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_VGG19</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

<pre><code>    &lt;your model's name&gt;.summary()

</code></pre>
<p><strong>Question 5:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.</p>
<p><strong>Answer:</strong> 
I tried bottleneck features from each available precomputed datasets, Xception showed the best result with test accuracy: 84.3301%. I used sigmoid activation function instead of relu because my CNN (without precomputed features) is not deep, so vanishing gradient is less of a problem. Using sigmoid over relu helped to improve accuracy and reduced overfitting. I tried to make this network more complex by adding more convolutional and dense layers, using regularization and dropouts, but couldn't beat this "simple" model.</p>

<pre><code>Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_17 (Conv2D)           (None, 7, 7, 64)          1179712   
_________________________________________________________________
global_average_pooling2d_8 ( (None, 64)                0         
_________________________________________________________________
dense_18 (Dense)             (None, 133)               8645      
=================================================================
Total params: 1,188,357
Trainable params: 1,188,357
Non-trainable params: 0</code></pre>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test accuracy: 69.0191%</span>
<span class="n">VGG19_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG19_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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG19</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">VGG19_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">0.8</span><span class="p">))</span>
<span class="n">VGG19_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">VGG19_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">133</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"># Test accuracy: 72.7273%</span>
<span class="n">VGG19_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG19_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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG19</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">VGG19_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">0.8</span><span class="p">))</span>
<span class="n">VGG19_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">VGG19_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">133</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"># Test accuracy: 73.6842%</span>
<span class="c1"># It doesn&#39;t overfit, val_loss keeps improving on each iteration</span>
<span class="n">VGG19_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG19_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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG19</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">VGG19_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">0.8</span><span class="p">))</span>
<span class="n">VGG19_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">VGG19_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">133</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"># Test accuracy: 77.0335%</span>
<span class="n">VGG19_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG19_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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG19</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">VGG19_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">VGG19_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">133</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"># TEST MODEL</span>
<span class="c1">#VGG19_model = Sequential()</span>
<span class="c1">#VGG19_model.add(Conv2D(filters=64, kernel_size=5, padding=&#39;same&#39;, activation=&#39;sigmoid&#39;, input_shape=train_VGG19.shape[1:]))</span>
<span class="c1">#VGG19_model.add(AveragePooling2D(pool_size=2))</span>
<span class="c1">#VGG19_model.add(Dropout(0.4))</span>
<span class="c1">#VGG19_model.add(Conv2D(filters=32, kernel_size=3, padding=&#39;same&#39;, activation=&#39;sigmoid&#39;))</span>
<span class="c1">#VGG19_model.add(GlobalAveragePooling2D())</span>
<span class="c1">#VGG19_model.add(Dense(133, activation=&#39;softmax&#39;))</span>


<span class="n">VGG19_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 #   
=================================================================
conv2d_173 (Conv2D)          (None, 7, 7, 64)          294976    
_________________________________________________________________
global_average_pooling2d_68  (None, 64)                0         
_________________________________________________________________
dense_158 (Dense)            (None, 133)               8645      
=================================================================
Total params: 303,621
Trainable params: 303,621
Non-trainable params: 0
_________________________________________________________________
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<h3 id="(IMPLEMENTATION)-Compile-the-Model">(IMPLEMENTATION) Compile the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG19_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>
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<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

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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;saved_models/weights.best.VGG19.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</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">VGG19_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_VGG19</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_VGG19</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</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">1</span><span class="p">)</span>
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<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 4.1163 - acc: 0.2277Epoch 00000: val_loss improved from inf to 3.48295, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 3s - loss: 4.1099 - acc: 0.2287 - val_loss: 3.4829 - val_acc: 0.3665
Epoch 2/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 2.9684 - acc: 0.4723Epoch 00001: val_loss improved from 3.48295 to 2.65910, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 2.9647 - acc: 0.4731 - val_loss: 2.6591 - val_acc: 0.5246
Epoch 3/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 2.2691 - acc: 0.5936Epoch 00002: val_loss improved from 2.65910 to 2.16391, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 2.2653 - acc: 0.5952 - val_loss: 2.1639 - val_acc: 0.5784
Epoch 4/20
6560/6680 [============================&gt;.] - ETA: 0s - loss: 1.8110 - acc: 0.6817Epoch 00003: val_loss improved from 2.16391 to 1.84705, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 1.8089 - acc: 0.6816 - val_loss: 1.8471 - val_acc: 0.6132
Epoch 5/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 1.4993 - acc: 0.7350Epoch 00004: val_loss improved from 1.84705 to 1.61211, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 1.5000 - acc: 0.7350 - val_loss: 1.6121 - val_acc: 0.6635
Epoch 6/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 1.2724 - acc: 0.7706Epoch 00005: val_loss improved from 1.61211 to 1.45055, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 1.2724 - acc: 0.7699 - val_loss: 1.4506 - val_acc: 0.6838
Epoch 7/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 1.0971 - acc: 0.7997Epoch 00006: val_loss improved from 1.45055 to 1.33778, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 1.0971 - acc: 0.7996 - val_loss: 1.3378 - val_acc: 0.6958
Epoch 8/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.9638 - acc: 0.8239Epoch 00007: val_loss improved from 1.33778 to 1.22899, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.9627 - acc: 0.8243 - val_loss: 1.2290 - val_acc: 0.7186
Epoch 9/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.8545 - acc: 0.8509Epoch 00008: val_loss improved from 1.22899 to 1.15629, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.8546 - acc: 0.8501 - val_loss: 1.1563 - val_acc: 0.7186
Epoch 10/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.7636 - acc: 0.8650Epoch 00009: val_loss improved from 1.15629 to 1.10212, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.7632 - acc: 0.8653 - val_loss: 1.1021 - val_acc: 0.7485
Epoch 11/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.6889 - acc: 0.8835Epoch 00010: val_loss improved from 1.10212 to 1.04664, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.6895 - acc: 0.8826 - val_loss: 1.0466 - val_acc: 0.7401
Epoch 12/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.6197 - acc: 0.8958Epoch 00011: val_loss improved from 1.04664 to 1.01275, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.6208 - acc: 0.8958 - val_loss: 1.0127 - val_acc: 0.7509
Epoch 13/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.5657 - acc: 0.9089Epoch 00012: val_loss improved from 1.01275 to 0.97938, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.5658 - acc: 0.9088 - val_loss: 0.9794 - val_acc: 0.7485
Epoch 14/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.5181 - acc: 0.9168Epoch 00013: val_loss improved from 0.97938 to 0.94075, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.5178 - acc: 0.9174 - val_loss: 0.9407 - val_acc: 0.7641
Epoch 15/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.4739 - acc: 0.9276Epoch 00014: val_loss improved from 0.94075 to 0.90327, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.4734 - acc: 0.9278 - val_loss: 0.9033 - val_acc: 0.7677
Epoch 16/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.4335 - acc: 0.9326Epoch 00015: val_loss improved from 0.90327 to 0.89386, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.4344 - acc: 0.9323 - val_loss: 0.8939 - val_acc: 0.7533
Epoch 17/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.3973 - acc: 0.9400Epoch 00016: val_loss improved from 0.89386 to 0.88650, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.3984 - acc: 0.9397 - val_loss: 0.8865 - val_acc: 0.7569
Epoch 18/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.3696 - acc: 0.9455Epoch 00017: val_loss improved from 0.88650 to 0.85239, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.3696 - acc: 0.9454 - val_loss: 0.8524 - val_acc: 0.7689
Epoch 19/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.3406 - acc: 0.9512Epoch 00018: val_loss improved from 0.85239 to 0.83634, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.3406 - acc: 0.9513 - val_loss: 0.8363 - val_acc: 0.7725
Epoch 20/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.3166 - acc: 0.9547Epoch 00019: val_loss improved from 0.83634 to 0.82660, saving model to saved_models/weights.best.VGG19.hdf5
6680/6680 [==============================] - 2s - loss: 0.3163 - acc: 0.9546 - val_loss: 0.8266 - val_acc: 0.7725
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<pre>&lt;keras.callbacks.History at 0x7fd13bf63898&gt;</pre>
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<h3 id="(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">(IMPLEMENTATION) Load the Model with the Best Validation Loss<a class="anchor-link" href="#(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG19_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.VGG19.hdf5&#39;</span><span class="p">)</span>
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<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span>
<span class="n">VGG19_predictions</span> <span class="o">=</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">VGG19_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</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="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_VGG19</span><span class="p">]</span>

<span class="c1"># report test accuracy</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</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">VGG19_predictions</span><span class="p">)</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">test_targets</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="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">VGG19_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 76.5550%
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<h3 id="ResNet-50-experiments">ResNet-50 experiments<a class="anchor-link" href="#ResNet-50-experiments">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>

<span class="k">def</span> <span class="nf">get_bottleneck_features</span><span class="p">(</span><span class="n">features_id</span><span class="p">):</span>
    <span class="n">bottleneck_features</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;bottleneck_features/Dog</span><span class="si">%s</span><span class="s1">Data.npz&#39;</span> <span class="o">%</span> <span class="n">features_id</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">],</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">],</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>

<span class="k">def</span> <span class="nf">compile_model</span><span class="p">(</span><span class="n">model</span><span class="p">):</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">def</span> <span class="nf">train_and_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span><span class="p">,</span> <span class="n">model_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">):</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="n">model_id</span> <span class="o">=</span> <span class="n">model_id</span> <span class="ow">or</span> <span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="p">())</span>   
    
    <span class="n">best_features_file</span> <span class="o">=</span> <span class="s2">&quot;saved_models/weights.best.</span><span class="si">%s</span><span class="s2">.hdf5&quot;</span> <span class="o">%</span> <span class="n">model_id</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="n">best_features_file</span><span class="p">,</span> 
                                   <span class="n">verbose</span><span class="o">=</span><span class="mi">1</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">early_stop</span> <span class="o">=</span> <span class="n">EarlyStopping</span><span class="p">(</span><span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_set</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
              <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_set</span><span class="p">,</span> <span class="n">valid_targets</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">batch_size</span><span class="o">=</span><span class="n">batch_size</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">early_stop</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">best_features_file</span><span class="p">)</span>
    <span class="n">predictions</span> <span class="o">=</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">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</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="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_set</span><span class="p">]</span>
    <span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</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">predictions</span><span class="p">)</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">test_targets</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="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="n">best_features_file</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">features_id</span> <span class="o">=</span> <span class="s1">&#39;Resnet50&#39;</span>
<span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span> <span class="o">=</span> <span class="n">get_bottleneck_features</span><span class="p">(</span><span class="n">features_id</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="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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_set</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">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">Dense</span><span class="p">(</span><span class="mi">133</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="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

<span class="n">model</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">train_and_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span><span class="p">,</span> <span class="n">features_id</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_40 (Conv2D)           (None, 1, 1, 64)          1179712   
_________________________________________________________________
global_average_pooling2d_14  (None, 64)                0         
_________________________________________________________________
dense_34 (Dense)             (None, 133)               8645      
=================================================================
Total params: 1,188,357
Trainable params: 1,188,357
Non-trainable params: 0
_________________________________________________________________
None
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 3.7219 - acc: 0.3409Epoch 00000: val_loss improved from inf to 2.71138, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 3s - loss: 3.7105 - acc: 0.3433 - val_loss: 2.7114 - val_acc: 0.5593
Epoch 2/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 2.0183 - acc: 0.6627Epoch 00001: val_loss improved from 2.71138 to 1.58674, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 2.0113 - acc: 0.6641 - val_loss: 1.5867 - val_acc: 0.6946
Epoch 3/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 1.1891 - acc: 0.7771Epoch 00002: val_loss improved from 1.58674 to 1.11330, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 1.1851 - acc: 0.7778 - val_loss: 1.1133 - val_acc: 0.7581
Epoch 4/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.8055 - acc: 0.8397Epoch 00003: val_loss improved from 1.11330 to 0.88191, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.8037 - acc: 0.8403 - val_loss: 0.8819 - val_acc: 0.7784
Epoch 5/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.5900 - acc: 0.8803Epoch 00004: val_loss improved from 0.88191 to 0.76425, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.5888 - acc: 0.8804 - val_loss: 0.7642 - val_acc: 0.8072
Epoch 6/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.4552 - acc: 0.9089Epoch 00005: val_loss improved from 0.76425 to 0.70080, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.4555 - acc: 0.9085 - val_loss: 0.7008 - val_acc: 0.8204
Epoch 7/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.3590 - acc: 0.9288Epoch 00006: val_loss improved from 0.70080 to 0.64659, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.3598 - acc: 0.9283 - val_loss: 0.6466 - val_acc: 0.8240
Epoch 8/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.2848 - acc: 0.9482Epoch 00007: val_loss improved from 0.64659 to 0.62112, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.2847 - acc: 0.9482 - val_loss: 0.6211 - val_acc: 0.8204
Epoch 9/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.2292 - acc: 0.9614Epoch 00008: val_loss improved from 0.62112 to 0.60513, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.2292 - acc: 0.9614 - val_loss: 0.6051 - val_acc: 0.8192
Epoch 10/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1841 - acc: 0.9711Epoch 00009: val_loss improved from 0.60513 to 0.59061, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.1843 - acc: 0.9708 - val_loss: 0.5906 - val_acc: 0.8347
Epoch 11/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1471 - acc: 0.9776Epoch 00010: val_loss improved from 0.59061 to 0.58412, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.1471 - acc: 0.9775 - val_loss: 0.5841 - val_acc: 0.8275
Epoch 12/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1209 - acc: 0.9835Epoch 00011: val_loss improved from 0.58412 to 0.57915, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.1210 - acc: 0.9832 - val_loss: 0.5791 - val_acc: 0.8192
Epoch 13/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0967 - acc: 0.9879Epoch 00012: val_loss improved from 0.57915 to 0.57006, saving model to saved_models/weights.best.Resnet50.hdf5
6680/6680 [==============================] - 2s - loss: 0.0965 - acc: 0.9879 - val_loss: 0.5701 - val_acc: 0.8287
Epoch 14/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0767 - acc: 0.9912Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0769 - acc: 0.9910 - val_loss: 0.5830 - val_acc: 0.8240
Epoch 15/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0608 - acc: 0.9945Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0608 - acc: 0.9945 - val_loss: 0.5980 - val_acc: 0.8263
Epoch 16/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0501 - acc: 0.9953Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0502 - acc: 0.9952 - val_loss: 0.5844 - val_acc: 0.8287
Epoch 17/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0392 - acc: 0.9962Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 2s - loss: 0.0392 - acc: 0.9961 - val_loss: 0.5713 - val_acc: 0.8240
Epoch 00016: early stopping
Test accuracy: 81.2201%
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<div class="prompt input_prompt">In&nbsp;[36]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">features_id</span> <span class="o">=</span> <span class="s1">&#39;Xception&#39;</span>
<span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span> <span class="o">=</span> <span class="n">get_bottleneck_features</span><span class="p">(</span><span class="n">features_id</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="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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_set</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">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">Dense</span><span class="p">(</span><span class="mi">133</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="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

<span class="n">model</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">train_and_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span><span class="p">,</span> <span class="n">features_id</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_41 (Conv2D)           (None, 7, 7, 64)          1179712   
_________________________________________________________________
global_average_pooling2d_15  (None, 64)                0         
_________________________________________________________________
dense_35 (Dense)             (None, 133)               8645      
=================================================================
Total params: 1,188,357
Trainable params: 1,188,357
Non-trainable params: 0
_________________________________________________________________
None
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.1946 - acc: 0.5467Epoch 00000: val_loss improved from inf to 2.17928, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 3.1918 - acc: 0.5472 - val_loss: 2.1793 - val_acc: 0.6994
Epoch 2/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 1.5298 - acc: 0.7627Epoch 00001: val_loss improved from 2.17928 to 1.17075, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 1.5251 - acc: 0.7630 - val_loss: 1.1707 - val_acc: 0.7665
Epoch 3/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.8510 - acc: 0.8373Epoch 00002: val_loss improved from 1.17075 to 0.81409, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.8488 - acc: 0.8376 - val_loss: 0.8141 - val_acc: 0.8132
Epoch 4/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.5688 - acc: 0.8806Epoch 00003: val_loss improved from 0.81409 to 0.68284, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.5672 - acc: 0.8808 - val_loss: 0.6828 - val_acc: 0.8251
Epoch 5/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.4226 - acc: 0.9097Epoch 00004: val_loss improved from 0.68284 to 0.60958, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.4241 - acc: 0.9094 - val_loss: 0.6096 - val_acc: 0.8275
Epoch 6/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.3293 - acc: 0.9282Epoch 00005: val_loss improved from 0.60958 to 0.56499, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.3286 - acc: 0.9287 - val_loss: 0.5650 - val_acc: 0.8407
Epoch 7/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.2592 - acc: 0.9444Epoch 00006: val_loss improved from 0.56499 to 0.56045, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.2594 - acc: 0.9442 - val_loss: 0.5605 - val_acc: 0.8395
Epoch 8/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.2051 - acc: 0.9574Epoch 00007: val_loss improved from 0.56045 to 0.53581, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.2057 - acc: 0.9573 - val_loss: 0.5358 - val_acc: 0.8359
Epoch 9/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1629 - acc: 0.9676Epoch 00008: val_loss improved from 0.53581 to 0.52568, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.1627 - acc: 0.9677 - val_loss: 0.5257 - val_acc: 0.8455
Epoch 10/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.1338 - acc: 0.9735Epoch 00009: val_loss improved from 0.52568 to 0.49886, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.1337 - acc: 0.9735 - val_loss: 0.4989 - val_acc: 0.8467
Epoch 11/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.1080 - acc: 0.9790Epoch 00010: val_loss improved from 0.49886 to 0.48198, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.1077 - acc: 0.9790 - val_loss: 0.4820 - val_acc: 0.8527
Epoch 12/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0853 - acc: 0.9843Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0852 - acc: 0.9843 - val_loss: 0.5084 - val_acc: 0.8419
Epoch 13/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.0689 - acc: 0.9883Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0690 - acc: 0.9883 - val_loss: 0.5002 - val_acc: 0.8551
Epoch 14/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.0554 - acc: 0.9917Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0552 - acc: 0.9918 - val_loss: 0.5098 - val_acc: 0.8455
Epoch 15/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.0471 - acc: 0.9907Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0473 - acc: 0.9906 - val_loss: 0.5054 - val_acc: 0.8503
Epoch 00014: early stopping
Test accuracy: 84.3301%
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[68]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">features_id</span> <span class="o">=</span> <span class="s1">&#39;InceptionV3&#39;</span>
<span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span> <span class="o">=</span> <span class="n">get_bottleneck_features</span><span class="p">(</span><span class="n">features_id</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="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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_set</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">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">Dense</span><span class="p">(</span><span class="mi">133</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="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

<span class="n">model</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">train_and_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span><span class="p">,</span> <span class="n">features_id</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_174 (Conv2D)          (None, 5, 5, 64)          1179712   
_________________________________________________________________
global_average_pooling2d_69  (None, 64)                0         
_________________________________________________________________
dense_159 (Dense)            (None, 133)               8645      
=================================================================
Total params: 1,188,357
Trainable params: 1,188,357
Non-trainable params: 0
_________________________________________________________________
None
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 3.0918 - acc: 0.5462Epoch 00000: val_loss improved from inf to 1.98738, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 5s - loss: 3.0773 - acc: 0.5484 - val_loss: 1.9874 - val_acc: 0.6886
Epoch 2/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 1.3687 - acc: 0.7588Epoch 00001: val_loss improved from 1.98738 to 1.05939, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 1.3634 - acc: 0.7599 - val_loss: 1.0594 - val_acc: 0.7605
Epoch 3/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.7998 - acc: 0.8248Epoch 00002: val_loss improved from 1.05939 to 0.79971, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.7987 - acc: 0.8244 - val_loss: 0.7997 - val_acc: 0.8012
Epoch 4/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.5697 - acc: 0.8663Epoch 00003: val_loss improved from 0.79971 to 0.66417, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.5688 - acc: 0.8665 - val_loss: 0.6642 - val_acc: 0.8144
Epoch 5/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.4342 - acc: 0.8915Epoch 00004: val_loss improved from 0.66417 to 0.60764, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.4343 - acc: 0.8912 - val_loss: 0.6076 - val_acc: 0.8216
Epoch 6/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.3515 - acc: 0.9151Epoch 00005: val_loss improved from 0.60764 to 0.59229, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.3503 - acc: 0.9156 - val_loss: 0.5923 - val_acc: 0.8192
Epoch 7/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.2830 - acc: 0.9321Epoch 00006: val_loss improved from 0.59229 to 0.55142, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.2836 - acc: 0.9314 - val_loss: 0.5514 - val_acc: 0.8323
Epoch 8/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.2310 - acc: 0.9443Epoch 00007: val_loss improved from 0.55142 to 0.55113, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.2308 - acc: 0.9445 - val_loss: 0.5511 - val_acc: 0.8287
Epoch 9/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.1891 - acc: 0.9536Epoch 00008: val_loss improved from 0.55113 to 0.54027, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.1888 - acc: 0.9536 - val_loss: 0.5403 - val_acc: 0.8299
Epoch 10/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.1541 - acc: 0.9665Epoch 00009: val_loss improved from 0.54027 to 0.53878, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.1538 - acc: 0.9666 - val_loss: 0.5388 - val_acc: 0.8419
Epoch 11/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.1265 - acc: 0.9737Epoch 00010: val_loss improved from 0.53878 to 0.53577, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.1269 - acc: 0.9735 - val_loss: 0.5358 - val_acc: 0.8299
Epoch 12/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.1053 - acc: 0.9784Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.1046 - acc: 0.9787 - val_loss: 0.5409 - val_acc: 0.8479
Epoch 13/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0858 - acc: 0.9847Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0859 - acc: 0.9846 - val_loss: 0.5444 - val_acc: 0.8431
Epoch 14/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0686 - acc: 0.9876Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0690 - acc: 0.9876 - val_loss: 0.5369 - val_acc: 0.8395
Epoch 15/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0580 - acc: 0.9902Epoch 00014: val_loss improved from 0.53577 to 0.52767, saving model to saved_models/weights.best.InceptionV3.hdf5
6680/6680 [==============================] - 4s - loss: 0.0582 - acc: 0.9901 - val_loss: 0.5277 - val_acc: 0.8467
Epoch 16/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.0472 - acc: 0.9915Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0470 - acc: 0.9915 - val_loss: 0.5737 - val_acc: 0.8407
Epoch 17/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0390 - acc: 0.9943Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0390 - acc: 0.9943 - val_loss: 0.5580 - val_acc: 0.8539
Epoch 18/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0317 - acc: 0.9955Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0317 - acc: 0.9955 - val_loss: 0.5864 - val_acc: 0.8395
Epoch 19/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0272 - acc: 0.9961Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0271 - acc: 0.9961 - val_loss: 0.5700 - val_acc: 0.8407
Epoch 20/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0216 - acc: 0.9968Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 4s - loss: 0.0219 - acc: 0.9967 - val_loss: 0.5803 - val_acc: 0.8335
Test accuracy: 81.6986%
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">regularizers</span>

<span class="n">features_id</span> <span class="o">=</span> <span class="s1">&#39;Xception&#39;</span>
<span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span> <span class="o">=</span> <span class="n">get_bottleneck_features</span><span class="p">(</span><span class="n">features_id</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="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">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</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">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_set</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">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">Dense</span><span class="p">(</span><span class="mi">133</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="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

<span class="n">model</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">train_and_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">valid_set</span><span class="p">,</span> <span class="n">test_set</span><span class="p">,</span> <span class="n">features_id</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_42 (Conv2D)           (None, 7, 7, 64)          1179712   
_________________________________________________________________
global_average_pooling2d_16  (None, 64)                0         
_________________________________________________________________
dense_36 (Dense)             (None, 133)               8645      
=================================================================
Total params: 1,188,357
Trainable params: 1,188,357
Non-trainable params: 0
_________________________________________________________________
None
Train on 6680 samples, validate on 835 samples
Epoch 1/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 3.1415 - acc: 0.5658Epoch 00000: val_loss improved from inf to 2.07484, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 3.1288 - acc: 0.5668 - val_loss: 2.0748 - val_acc: 0.7054
Epoch 2/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 1.4717 - acc: 0.7687Epoch 00001: val_loss improved from 2.07484 to 1.12724, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 1.4702 - acc: 0.7687 - val_loss: 1.1272 - val_acc: 0.7796
Epoch 3/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.8235 - acc: 0.8393Epoch 00002: val_loss improved from 1.12724 to 0.79615, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 0.8230 - acc: 0.8397 - val_loss: 0.7961 - val_acc: 0.8251
Epoch 4/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.5603 - acc: 0.8802Epoch 00003: val_loss improved from 0.79615 to 0.65699, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 0.5615 - acc: 0.8793 - val_loss: 0.6570 - val_acc: 0.8275
Epoch 5/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.4139 - acc: 0.9105Epoch 00004: val_loss improved from 0.65699 to 0.59344, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.4151 - acc: 0.9103 - val_loss: 0.5934 - val_acc: 0.8383
Epoch 6/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.3242 - acc: 0.9328Epoch 00005: val_loss improved from 0.59344 to 0.53783, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 6s - loss: 0.3240 - acc: 0.9332 - val_loss: 0.5378 - val_acc: 0.8443
Epoch 7/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.2558 - acc: 0.9450Epoch 00006: val_loss improved from 0.53783 to 0.52375, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.2556 - acc: 0.9446 - val_loss: 0.5237 - val_acc: 0.8455
Epoch 8/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.2056 - acc: 0.9565Epoch 00007: val_loss improved from 0.52375 to 0.51482, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.2059 - acc: 0.9561 - val_loss: 0.5148 - val_acc: 0.8359
Epoch 9/20
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.1638 - acc: 0.9658Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.1638 - acc: 0.9659 - val_loss: 0.5149 - val_acc: 0.8467
Epoch 10/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.1321 - acc: 0.9745Epoch 00009: val_loss improved from 0.51482 to 0.49961, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.1320 - acc: 0.9746 - val_loss: 0.4996 - val_acc: 0.8419
Epoch 11/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.1072 - acc: 0.9790Epoch 00010: val_loss improved from 0.49961 to 0.47396, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.1070 - acc: 0.9792 - val_loss: 0.4740 - val_acc: 0.8503
Epoch 12/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0870 - acc: 0.9846Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0869 - acc: 0.9844 - val_loss: 0.4884 - val_acc: 0.8491
Epoch 13/20
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.0710 - acc: 0.9874Epoch 00012: val_loss improved from 0.47396 to 0.45601, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 5s - loss: 0.0711 - acc: 0.9874 - val_loss: 0.4560 - val_acc: 0.8587
Epoch 14/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.0559 - acc: 0.9893Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0562 - acc: 0.9892 - val_loss: 0.4812 - val_acc: 0.8563
Epoch 15/20
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.0459 - acc: 0.9914Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0459 - acc: 0.9915 - val_loss: 0.4965 - val_acc: 0.8479
Epoch 16/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0385 - acc: 0.9926Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0384 - acc: 0.9927 - val_loss: 0.4975 - val_acc: 0.8479
Epoch 17/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.0324 - acc: 0.9937Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 5s - loss: 0.0323 - acc: 0.9937 - val_loss: 0.5012 - val_acc: 0.8431
Epoch 00016: early stopping
Test accuracy: 84.5694%
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<h3 id="(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">(IMPLEMENTATION) Predict Dog Breed with the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">&#182;</a></h3><p>Write a function that takes an image path as input and returns the dog breed (<code>Affenpinscher</code>, <code>Afghan_hound</code>, etc) that is predicted by your model.</p>
<p>Similar to the analogous function in Step 5, your function should have three steps:</p>
<ol>
<li>Extract the bottleneck features corresponding to the chosen CNN model.</li>
<li>Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.</li>
<li>Use the <code>dog_names</code> array defined in Step 0 of this notebook to return the corresponding breed.</li>
</ol>
<p>The functions to extract the bottleneck features can be found in <code>extract_bottleneck_features.py</code>, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function</p>

<pre><code>extract_{network}

</code></pre>
<p>where <code>{network}</code>, in the above filename, should be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write a function that takes a path to an image as input</span>
<span class="c1">### and returns the dog breed that is predicted by the model.</span>

<span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span>

<span class="n">networks</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;VGG19&#39;</span><span class="p">:</span> <span class="n">extract_VGG19</span><span class="p">,</span>
            <span class="s1">&#39;Resnet50&#39;</span><span class="p">:</span> <span class="n">extract_Resnet50</span><span class="p">,</span>
            <span class="s1">&#39;InceptionV3&#39;</span><span class="p">:</span> <span class="n">extract_InceptionV3</span><span class="p">,</span>
            <span class="s1">&#39;Xception&#39;</span><span class="p">:</span> <span class="n">extract_Xception</span><span class="p">}</span>

<span class="k">def</span> <span class="nf">predict_dog_breed</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">network_id</span><span class="p">):</span>
    <span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">networks</span><span class="p">[</span><span class="n">network_id</span><span class="p">](</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">file_path</span><span class="p">))</span>
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_features</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">dog_names</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">predicted_vector</span><span class="p">)]</span>
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<p><a id='step6'></a></p>
<h2 id="Step-6:-Write-your-Algorithm">Step 6: Write your Algorithm<a class="anchor-link" href="#Step-6:-Write-your-Algorithm">&#182;</a></h2><p>Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,</p>
<ul>
<li>if a <strong>dog</strong> is detected in the image, return the predicted breed.</li>
<li>if a <strong>human</strong> is detected in the image, return the resembling dog breed.</li>
<li>if <strong>neither</strong> is detected in the image, provide output that indicates an error.</li>
</ul>
<p>You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the <code>face_detector</code> and <code>dog_detector</code> functions developed above.  You are <strong>required</strong> to use your CNN from Step 5 to predict dog breed.</p>
<p>Some sample output for our algorithm is provided below, but feel free to design your own user experience!</p>
<p><img src="images/sample_human_output.png" alt="Sample Human Output"></p>
<h3 id="(IMPLEMENTATION)-Write-your-Algorithm">(IMPLEMENTATION) Write your Algorithm<a class="anchor-link" href="#(IMPLEMENTATION)-Write-your-Algorithm">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write your algorithm.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>

<span class="k">def</span> <span class="nf">predict_or_fail_func</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">network_id</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
            <span class="n">breed</span> <span class="o">=</span> <span class="n">predict_dog_breed</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">network_id</span><span class="p">)</span>
            <span class="k">return</span> <span class="s2">&quot;We think this image contains a human. And this human resembles </span><span class="si">%s</span><span class="s2">! A little (:&quot;</span> <span class="o">%</span> <span class="n">breed</span>
        <span class="k">elif</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
            <span class="n">breed</span> <span class="o">=</span> <span class="n">predict_dog_breed</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">,</span> <span class="n">network_id</span><span class="p">)</span>
            <span class="k">return</span> <span class="s2">&quot;It&#39;s a </span><span class="si">%s</span><span class="s2">! Right?..&quot;</span> <span class="o">%</span> <span class="n">breed</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Seems like provided image contains neither humans nor dogs!&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">inner</span>

<span class="n">predict_of_fail</span> <span class="o">=</span> <span class="n">predict_or_fail_func</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;Xception&#39;</span><span class="p">)</span>
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<p><a id='step7'></a></p>
<h2 id="Step-7:-Test-Your-Algorithm">Step 7: Test Your Algorithm<a class="anchor-link" href="#Step-7:-Test-Your-Algorithm">&#182;</a></h2><p>In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that <strong>you</strong> look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?</p>
<h3 id="(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">(IMPLEMENTATION) Test Your Algorithm on Sample Images!<a class="anchor-link" href="#(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">&#182;</a></h3><p>Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.</p>
<p><strong>Question 6:</strong> Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.</p>
<p><strong>Answer:</strong></p>
<ul>
<li>The algorithms is actually doing pretty good. Even though it's hard for me to tell the different between Boykin Spaniel and Field Spaniel. It could correctly identify breed of 5 out of 7 random dog images from the Internet. It works much better than I expected! For humans, it's completely off. I was having hard time to figure out why this or that dog was chosen as closest to that particular human face.</li>
</ul>
<p>Potential points of improvement:</p>
<ul>
<li>Use regularization to penalize too big weights</li>
<li>Potentially removing image background can help</li>
<li>We can train and use several models and then pick the prediction on which most of them agree</li>
<li>We can train models specific to a dog "family". For example, train the network that recognizes only different kinds of Spaniels. So after a more general model predicts that the dog is a Spaniel, more specialized model can further see what exactly Spaniel that is.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## TODO: Execute your algorithm from Step 6 on</span>
<span class="c1">## at least 6 images on your computer.</span>
<span class="c1">## Feel free to use as many code cells as needed.</span>
<span class="n">files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;dog1.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog2.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog3.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog4.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog5.jpg&#39;</span><span class="p">,</span>
         <span class="s1">&#39;dog6.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;dog7.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;human1.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;human2.jpg&#39;</span><span class="p">,</span> <span class="s1">&#39;human3.jpg&#39;</span><span class="p">,</span>
         <span class="s1">&#39;tree1.jpg&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">predict_of_fail</span><span class="p">(</span><span class="s1">&#39;user-uploads/</span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">file</span><span class="p">))</span>
    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
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<pre>It&#39;s a Boykin_spaniel! Right?..
It&#39;s a Field_spaniel! Right?..
It&#39;s a Labrador_retriever! Right?..
It&#39;s a Cairn_terrier! Right?..
It&#39;s a Pekingese! Right?..
It&#39;s a Australian_shepherd! Right?..
It&#39;s a English_cocker_spaniel! Right?..
We think this image contains a human. And this human resembles Portuguese_water_dog! A little (:
We think this image contains a human. And this human resembles Cardigan_welsh_corgi! A little (:
We think this image contains a human. And this human resembles Cardigan_welsh_corgi! A little (:
Seems like provided image contains neither humans nor dogs!
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