report.html.html

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 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="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">&#182;</a></h1><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 Jupyter 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 <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 Jupyter 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>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step5">Step 5</a>: Write your Algorithm</li>
<li><a href="#step6">Step 6</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><p>Make sure that you've downloaded the required human and dog datasets:</p>
<ul>
<li><p>Download the <a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip">dog dataset</a>.  Unzip the folder and place it in this project's home directory, at the location <code>/dogImages</code>.</p>
</li>
<li><p>Download the <a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip">human dataset</a>.  Unzip the folder and place it in the home diretcory, at location <code>/lfw</code>.</p>
</li>
</ul>
<p><em>Note: If you are using a Windows machine, you are encouraged to use <a href="http://www.7-zip.org/">7zip</a> to extract the folder.</em></p>
<p>In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays <code>human_files</code> and <code>dog_files</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>

<span class="c1"># For the sake of a later step, I decided to </span>
<span class="c1"># place the images in a data_images folder.</span>

<span class="c1"># load filenames for human and dog images</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;data_images/humans/*/*&quot;</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">glob</span><span class="p">(</span><span class="s2">&quot;data_images/dogs/*/*/*&quot;</span><span class="p">))</span>

<span class="c1"># print number of images in each 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>
<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 images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">dog_files</span><span class="p">))</span>
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<pre>There are 13233 total human images.
There are 8351 total dog 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>In this section, 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.</p>
<p>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.  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">0</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>
</pre></div>

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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>

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<p><strong>Answer:</strong> 
(You can print out your results and/or write your percentages in this cell)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</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">dog_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="c1"># Define counters</span>
<span class="n">counters</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;human&#39;</span><span class="p">:</span> <span class="n">Counter</span><span class="p">(),</span>
    <span class="s1">&#39;dog&#39;</span><span class="p">:</span> <span class="n">Counter</span><span class="p">()</span>
<span class="p">}</span>

<span class="k">def</span> <span class="nf">count_faces</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">counter</span><span class="p">):</span>
    <span class="c1">## Track total files given</span>
    <span class="n">counter</span><span class="p">[</span><span class="s1">&#39;tests&#39;</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="c1">## Detect a face for every image</span>
    <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">files</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
            <span class="n">counter</span><span class="p">[</span><span class="s1">&#39;faces&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>

<span class="c1">## Count faces in human umages and dog images</span>
<span class="n">count_faces</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;human&#39;</span><span class="p">])</span>
<span class="n">count_faces</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">,</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;dog&#39;</span><span class="p">])</span>
        
<span class="n">human_accuracy</span> <span class="o">=</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;human&#39;</span><span class="p">][</span><span class="s1">&#39;faces&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;human&#39;</span><span class="p">][</span><span class="s1">&#39;tests&#39;</span><span class="p">]</span>
<span class="n">dog_accuracy</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;dog&#39;</span><span class="p">][</span><span class="s1">&#39;faces&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;dog&#39;</span><span class="p">][</span><span class="s1">&#39;tests&#39;</span><span class="p">])</span>

<span class="c1"># &quot;Face Detector:\nOut of {} tests, {:.3f} accuracy&quot;.format(total_tests, accuracy),</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Human Images: Faces </span><span class="si">{}</span><span class="s2"> - Images </span><span class="si">{}</span><span class="s2"> - Accuracy </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">counters</span><span class="p">[</span><span class="s1">&#39;human&#39;</span><span class="p">][</span><span class="s1">&#39;faces&#39;</span><span class="p">],</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;human&#39;</span><span class="p">][</span><span class="s1">&#39;tests&#39;</span><span class="p">],</span> <span class="n">human_accuracy</span><span class="p">),</span>
      <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Dog Images: Faces </span><span class="si">{}</span><span class="s2"> - Images </span><span class="si">{}</span><span class="s2"> - Accuracy </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">counters</span><span class="p">[</span><span class="s1">&#39;dog&#39;</span><span class="p">][</span><span class="s1">&#39;faces&#39;</span><span class="p">],</span> <span class="n">counters</span><span class="p">[</span><span class="s1">&#39;dog&#39;</span><span class="p">][</span><span class="s1">&#39;tests&#39;</span><span class="p">],</span> <span class="n">dog_accuracy</span><span class="p">),</span>
      <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Reliability: </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">human_accuracy</span> <span class="o">+</span> <span class="n">dog_accuracy</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
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<pre>100%|██████████| 100/100 [00:01&lt;00:00, 61.30it/s]
100%|██████████| 100/100 [00:09&lt;00:00, 12.52it/s]
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<pre>Human Images: Faces 96 - Images 100 - Accuracy 0.960 
Dog Images: Faces 18 - Images 100 - Accuracy 0.820 
Reliability: 0.890
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<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 <code>human_files_short</code> and <code>dog_files_short</code>.</p>

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<hr>
<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 <a href="http://pytorch.org/docs/master/torchvision/models.html">pre-trained model</a> to detect dogs in images.</p>
<h3 id="Obtain-Pre-trained-VGG-16-Model">Obtain Pre-trained VGG-16 Model<a class="anchor-link" href="#Obtain-Pre-trained-VGG-16-Model">&#182;</a></h3><p>The code cell below downloads the VGG-16 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>.</p>

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

<span class="c1"># define VGG16 model</span>
<span class="n">VGG16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># check if CUDA is available</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># move model to GPU if CUDA is available</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using cuda&#39;</span><span class="p">)</span>
    <span class="n">VGG16</span> <span class="o">=</span> <span class="n">VGG16</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<pre>Using cuda
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<p>Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.</p>

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<h3 id="(IMPLEMENTATION)-Making-Predictions-with-a-Pre-trained-Model">(IMPLEMENTATION) Making Predictions with a Pre-trained Model<a class="anchor-link" href="#(IMPLEMENTATION)-Making-Predictions-with-a-Pre-trained-Model">&#182;</a></h3><p>In the next code cell, you will write a function that accepts a path to an image (such as <code>'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg'</code>) as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model.  The output should always be an integer between 0 and 999, inclusive.</p>
<p>Before writing the function, make sure that you take the time to learn  how to appropriately pre-process tensors for pre-trained models in the <a href="http://pytorch.org/docs/stable/torchvision/models.html">PyTorch documentation</a>.</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">Image</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Variable</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>


<span class="k">def</span> <span class="nf">VGG16_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Use pre-trained VGG-16 model to obtain index corresponding to </span>
<span class="sd">    predicted ImageNet class for image at specified path</span>
<span class="sd">    </span>
<span class="sd">    Args:</span>
<span class="sd">        img_path: path to an image</span>
<span class="sd">        </span>
<span class="sd">    Returns:</span>
<span class="sd">        Index corresponding to VGG-16 model&#39;s prediction</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="c1">## TODO: Complete the function.</span>
    <span class="c1">## Load and pre-process an image from the given img_path</span>
    <span class="c1">## Return the *index* of the predicted class for that image</span>

    <span class="c1"># Steps to take:</span>
    <span class="c1"># 1) resize the image to 256 x 256 then crop the center out at 224x224.</span>
    <span class="c1"># 2) normalize it with mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225] (docs)</span>
    <span class="c1"># 3) convert it to a PyTorch Tensor.</span>

    <span class="c1"># Handle transforms using torchvision     </span>
    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> 
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                         <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>
    <span class="c1"># Load image and run through transform      </span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    
    
    <span class="c1"># Pretrained models for PyTorch expect the Tensor dimensions to be the following</span>
    <span class="c1"># </span>
    <span class="c1"># (NUMBER_OF_INPUT_IMAGES, NUMBER_OF_COLOR_CHANNLES, HEIGHT, WIDTH).</span>
    <span class="c1">#</span>
    <span class="c1"># We can insert a new dimension using unsqueeze method on tensor</span>
    <span class="c1"># to make change from [3, 224, 224] to [1, 3, 224, 224]</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

    <span class="c1"># Prediction placeholder</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="kc">False</span>
    
    <span class="c1"># Depending on whether cuda is being used, make sure the passed tensor</span>
    <span class="c1"># is being moved between the cpu and GPU pre/post calculation.</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">VGG16</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">VGG16</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>
    
    <span class="c1"># NOTE! PyTorch expects to have it&#39;s inputs be Variables. The PyTorch Variable object is utility</span>
    <span class="c1"># class that takes a tensor as an argument and achieves our goal.</span>

    <span class="c1"># To access the index of the highest predicted class value, we access</span>
    <span class="c1"># the data of the prediction and find the index of it&#39;s highest propbablity</span>
    <span class="k">return</span> <span class="n">prediction</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>
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<h3 id="(IMPLEMENTATION)-Write-a-Dog-Detector">(IMPLEMENTATION) Write a Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-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 VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).</p>
<p>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="n">dog_labels_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">151</span><span class="p">,</span> <span class="mi">269</span><span class="p">)</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">model</span><span class="o">=</span><span class="s1">&#39;vgg16&#39;</span><span class="p">):</span>
    <span class="c1">## TODO: Complete the function.</span>
    <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;vgg16&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">VGG16_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="ow">in</span> <span class="n">dog_labels_range</span>
    <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;vgg19&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">VGG19_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="ow">in</span> <span class="n">dog_labels_range</span>

<span class="n">dog_detector</span><span class="p">(</span><span class="s1">&#39;data_images/dogs/train/001.Affenpinscher/Affenpinscher_00001.jpg&#39;</span><span class="p">)</span>
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<pre>True</pre>
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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 2:</strong> Use the code cell below to test the performance of your <code>dog_detector</code> function.</p>
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<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>
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<p><strong>Answer:</strong></p>

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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="k">def</span> <span class="nf">dog_detection_benchmark</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">has_dogs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s1">&#39;vgg16&#39;</span><span class="p">):</span>
    <span class="c1"># Define counter</span>
    <span class="n">counter</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">()</span>
    
    <span class="c1"># Track number of tests</span>
    <span class="n">image_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>

    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">image_count</span><span class="p">):</span>
        <span class="c1"># If image has face     </span>
        <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">):</span>
            <span class="n">counter</span><span class="p">[</span><span class="s1">&#39;dog_images&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>

    <span class="c1"># Calculate out results</span>
    <span class="n">accuracy</span> <span class="o">=</span> <span class="n">counter</span><span class="p">[</span><span class="s1">&#39;dog_images&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="n">image_count</span>

    <span class="k">if</span> <span class="n">has_dogs</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracy</span>

    <span class="c1"># Print performance</span>
    <span class="n">wwo</span> <span class="o">=</span> <span class="s1">&#39;Evaluating images </span><span class="si">{}</span><span class="s1"> dogs&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;with&#39;</span> <span class="k">if</span> <span class="n">has_dogs</span> <span class="k">else</span> <span class="s1">&#39;without&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{}</span><span class="s2"> model correctly classifed </span><span class="si">{:.3f}% o</span><span class="s2">f </span><span class="si">{}</span><span class="s2"> images </span><span class="si">{}</span><span class="s2"> dogs&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="n">image_count</span><span class="p">,</span> <span class="n">wwo</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Dog detection performance for vgg16 model</span>
<span class="n">dog_detection_benchmark</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">)</span>
<span class="n">dog_detection_benchmark</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span> <span class="n">has_dogs</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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vgg16 model correctly classifed 91.000% of 100 images Evaluating images with dogs dogs

vgg16 model correctly classifed 99.000% of 100 images Evaluating images without dogs dogs
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<p>We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as <a href="http://pytorch.org/docs/master/torchvision/models.html#inception-v3">Inception-v3</a>, <a href="http://pytorch.org/docs/master/torchvision/models.html#id3">ResNet-50</a>, etc).  Please use the code cell below to test other pre-trained PyTorch models.  If you decide to pursue this <em>optional</em> task, report performance on <code>human_files_short</code> and <code>dog_files_short</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### (Optional) </span>
<span class="c1">### TODO: Report the performance of another pre-trained network.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="c1"># define vgg19 model</span>
<span class="n">VGG19</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg19</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># check if CUDA is available</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># move model to GPU if CUDA is available</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="n">VGG19</span> <span class="o">=</span> <span class="n">VGG19</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Prepare image using transforms for resnet input and incapsulate logic in a method.</span>
<span class="k">def</span> <span class="nf">VGG19_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># Handle transforms using torchvision     </span>
    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> 
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                         <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>
    <span class="c1"># Load image and run through transform      </span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="c1"># Add dimension to tensor for number of images</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="c1"># Prediction placeholder</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="c1"># Depending on whether cuda is being used, make sure the passed tensor</span>
    <span class="c1"># is being moved between the cpu and GPU pre/post calculation.</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">VGG19</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">VGG19</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>

    <span class="c1"># To access the index of the highest predicted class value, we access</span>
    <span class="c1"># the data of the prediction and find the index of it&#39;s highest propbablity</span>
    <span class="k">return</span> <span class="n">prediction</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">()</span>


<span class="n">dog_detection_benchmark</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s1">&#39;vgg19&#39;</span><span class="p">)</span>
<span class="n">dog_detection_benchmark</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span> <span class="n">has_dogs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s1">&#39;vgg19&#39;</span><span class="p">)</span>
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vgg19 model correctly classifed 92.000% of 100 images Evaluating images with dogs dogs

vgg19 model correctly classifed 99.000% of 100 images Evaluating images without dogs dogs
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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 10%.  In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</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 trouble 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="(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">&#182;</a></h3><p>Use the code cell below to write three separate <a href="http://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader">data loaders</a> for the training, validation, and test datasets of dog images (located at <code>dogImages/train</code>, <code>dogImages/valid</code>, and <code>dogImages/test</code>, respectively).  You may find <a href="http://pytorch.org/docs/stable/torchvision/datasets.html">this documentation on custom datasets</a> to be a useful resource.  If you are interested in augmenting your training and/or validation data, check out the wide variety of <a href="http://pytorch.org/docs/stable/torchvision/transforms.html?highlight=transform">transforms</a>!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</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">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>

<span class="c1">### TODO: Write data loaders for training, validation, and test sets</span>
<span class="c1">## Specify appropriate transforms, and batch_sizes </span>

<span class="c1"># Common parameters/attributes</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">200</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">num_workers</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;data_images/&#39;</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># Data transform will resize and images to 300 x 300, crop out the </span>
<span class="c1"># center at as a 250x250 square and convert the image to a tensor.</span>
<span class="n">image_transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">300</span><span class="p">),</span>
                                <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">280</span><span class="p">),</span> 
                                <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">()])</span>

<span class="c1"># Load in data directories from each relevant folder and transform</span>
<span class="n">data_scratch</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/train&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">),</span>
    <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/valid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">),</span>
    <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">)</span>
<span class="p">}</span>

<span class="c1"># Prepare test/train/valid data loaders</span>
<span class="n">loaders_scratch</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_scratch</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_scratch</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_scratch</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">}</span>
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<p><strong>Question 3:</strong> Describe your chosen procedure for preprocessing the data.</p>
<ul>
<li>How does your code resize the images (by cropping, stretching, etc)?  What size did you pick for the input tensor, and why?</li>
<li>Did you decide to augment the dataset?  If so, how (through translations, flips, rotations, etc)?  If not, why not?</li>
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<p><strong>Answer</strong>:</p>
<p>I've gotten comfortable with the VGG16 model, and like starting from there as a baseline for what I imagine may work well at for this task...</p>
<p>Being that the convolutional layers in general move from subtle pattern recognition to large features, I want my model to start with a larger, more detailed image. The images being fed into the classifier will be dogs, so I'm not that concerned with the later convolution layers, since to differentiation between dog breeds will largely be in the "textures" (coat patterns/length) and general shapes (spots, no spots).</p>

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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.  Use the template in the code cell below.</p>

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

<span class="c1"># define the CNN architecture</span>
<span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="c1">### TODO: choose an architecture, and complete the class</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="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="c1"># Conv Layer 1: First layer intended for detailed feature extraction</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">45</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1_3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">45</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        
        <span class="c1"># Conv Layer 2</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2_3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        
        <span class="c1"># Pooling layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        
        <span class="c1"># Linear layers (FCL)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">5</span> <span class="o">*</span> <span class="mi">70</span> <span class="o">*</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">250</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="mi">133</span><span class="p">)</span>
        
        <span class="c1"># Dropout layers</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
    
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="c1">## Conv layer 1</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1_1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1_2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1_3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        
        <span class="c1">## Downsize image</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="c1">## Conv Layer 2</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>  
        
        <span class="c1">## Downsize image</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="c1">## Resize tensor to linear dim</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span> <span class="o">*</span> <span class="mi">70</span> <span class="o">*</span> <span class="mi">70</span><span class="p">)</span>
        
        <span class="c1">## Use dropout</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="c1"># Fully connected pipeline</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

<span class="c1">#-#-# You so NOT have to modify the code below this line. #-#-#</span>

<span class="c1"># instantiate the CNN</span>
<span class="n">model_scratch</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>

<span class="c1"># move tensors to GPU if CUDA is available</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using cuda&#39;</span><span class="p">)</span>
    <span class="n">model_scratch</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<pre>Using cuda
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<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.</p>

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<p><strong>Answer:</strong></p>

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<h3 id="(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">(IMPLEMENTATION) Specify Loss Function and Optimizer<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">&#182;</a></h3><p>Use the next code cell to specify a <a href="http://pytorch.org/docs/stable/nn.html#loss-functions">loss function</a> and <a href="http://pytorch.org/docs/stable/optim.html">optimizer</a>.  Save the chosen loss function as <code>criterion_scratch</code>, and the optimizer as <code>optimizer_scratch</code> below.</p>

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

<span class="c1">### TODO: select loss function</span>
<span class="n">criterion_scratch</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>

<span class="c1">### TODO: select optimizer</span>
<span class="n">optimizer_scratch</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model_scratch</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.025</span><span class="p">)</span>
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<h2 id="Clear-GPU-cache">Clear GPU cache<a class="anchor-link" href="#Clear-GPU-cache">&#182;</a></h2><p>My GPU memory was filling up and unable to process this next training cycle. Online I found the following suggestion.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
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<h3 id="(IMPLEMENTATION)-Train-and-Validate-the-Model">(IMPLEMENTATION) Train and Validate the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-and-Validate-the-Model">&#182;</a></h3><p>Train and validate your model in the code cell below.  <a href="http://pytorch.org/docs/master/notes/serialization.html">Save the final model parameters</a> at filepath <code>'model_scratch.pt'</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load large images, prevent truncated image error</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="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">loaders</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="n">save_path</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;returns trained model&quot;&quot;&quot;</span>
    <span class="c1"># initialize tracker for minimum validation loss</span>
    <span class="n">valid_loss_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">Inf</span> 
    
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
        <span class="c1"># initialize variables to monitor training and validation loss</span>
        <span class="n">train_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="n">valid_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        
        <span class="c1">###################</span>
        <span class="c1"># train the model #</span>
        <span class="c1">###################</span>
        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">loaders</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]):</span>
            <span class="c1"># move to GPU</span>
            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
                <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
                
            <span class="c1">## find the loss and update the model parameters accordingly</span>
            <span class="c1">## record the average training loss, using something like</span>
            
            <span class="c1"># clear the gradients of all optimized variables</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            <span class="c1"># forward pass: compute predicted outputs by passing inputs to the model</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
            <span class="c1"># calculate the batch loss</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
            <span class="c1"># backward pass: compute gradient of the loss with respect to model parameters</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            <span class="c1"># perform a single optimization step (parameter update)</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            <span class="c1"># update training loss</span>
            <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">train_loss</span><span class="p">))</span>
            
        <span class="c1">######################    </span>
        <span class="c1"># validate the model #</span>
        <span class="c1">###################### </span>
        <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">loaders</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]):</span>
            <span class="c1"># move to GPU</span>
            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
                <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="c1">## update the average validation loss</span>
    
            <span class="c1"># forward pass: compute predicted outputs by passing inputs to the model</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
            <span class="c1"># calculate the batch loss</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
            <span class="c1"># update validation loss</span>
            <span class="n">valid_loss</span> <span class="o">=</span> <span class="n">valid_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">valid_loss</span><span class="p">))</span>
            
        <span class="c1"># print training/validation statistics </span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch: </span><span class="si">{}</span><span class="s1"> </span><span class="se">\t</span><span class="s1">Training Loss: </span><span class="si">{:.6f}</span><span class="s1"> </span><span class="se">\t</span><span class="s1">Validation Loss: </span><span class="si">{:.6f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">epoch</span><span class="p">,</span> 
            <span class="n">train_loss</span><span class="p">,</span>
            <span class="n">valid_loss</span>
            <span class="p">))</span>
        
        <span class="c1">## TODO: save the model if validation loss has decreased</span>
        <span class="c1"># Save model if validation loss has decreased since last min</span>
        <span class="k">if</span> <span class="n">valid_loss</span> <span class="o">&lt;=</span> <span class="n">valid_loss_min</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation loss decreased (</span><span class="si">{:.6f}</span><span class="s1"> --&gt; </span><span class="si">{:.6f}</span><span class="s1">).  Saving model ...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">valid_loss_min</span><span class="p">,</span>
            <span class="n">valid_loss</span><span class="p">))</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">save_path</span><span class="p">)</span>
            <span class="n">valid_loss_min</span> <span class="o">=</span> <span class="n">valid_loss</span>
            
    <span class="c1"># return trained model</span>
    <span class="k">return</span> <span class="n">model</span>
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<div class="prompt input_prompt">In&nbsp;[19]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># train the model</span>
<span class="n">model_scratch</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">loaders_scratch</span><span class="p">,</span> <span class="n">model_scratch</span><span class="p">,</span> <span class="n">optimizer_scratch</span><span class="p">,</span> 
                      <span class="n">criterion_scratch</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="s1">&#39;model_scratch.pt&#39;</span><span class="p">)</span>

<span class="c1"># load the model that got the best validation accuracy</span>
<span class="n">model_scratch</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;model_scratch.pt&#39;</span><span class="p">))</span>
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<pre>Epoch: 1 	Training Loss: 4.880341 	Validation Loss: 4.872962
Validation loss decreased (inf --&gt; 4.872962).  Saving model ...
Epoch: 2 	Training Loss: 4.874009 	Validation Loss: 4.872228
Validation loss decreased (4.872962 --&gt; 4.872228).  Saving model ...
Epoch: 3 	Training Loss: 4.871522 	Validation Loss: 4.868874
Validation loss decreased (4.872228 --&gt; 4.868874).  Saving model ...
Epoch: 4 	Training Loss: 4.868633 	Validation Loss: 4.868736
Validation loss decreased (4.868874 --&gt; 4.868736).  Saving model ...
Epoch: 5 	Training Loss: 4.868582 	Validation Loss: 4.869026
Epoch: 6 	Training Loss: 4.868794 	Validation Loss: 4.868694
Validation loss decreased (4.868736 --&gt; 4.868694).  Saving model ...
Epoch: 7 	Training Loss: 4.867422 	Validation Loss: 4.868180
Validation loss decreased (4.868694 --&gt; 4.868180).  Saving model ...
Epoch: 8 	Training Loss: 4.867002 	Validation Loss: 4.868587
Epoch: 9 	Training Loss: 4.867708 	Validation Loss: 4.870273
Epoch: 10 	Training Loss: 4.866976 	Validation Loss: 4.868464
Epoch: 11 	Training Loss: 4.866434 	Validation Loss: 4.868221
Epoch: 12 	Training Loss: 4.865241 	Validation Loss: 4.869108
Epoch: 13 	Training Loss: 4.865422 	Validation Loss: 4.865107
Validation loss decreased (4.868180 --&gt; 4.865107).  Saving model ...
Epoch: 14 	Training Loss: 4.864056 	Validation Loss: 4.862507
Validation loss decreased (4.865107 --&gt; 4.862507).  Saving model ...
Epoch: 15 	Training Loss: 4.855582 	Validation Loss: 4.843859
Validation loss decreased (4.862507 --&gt; 4.843859).  Saving model ...
Epoch: 16 	Training Loss: 4.838360 	Validation Loss: 4.831202
Validation loss decreased (4.843859 --&gt; 4.831202).  Saving model ...
Epoch: 17 	Training Loss: 4.810301 	Validation Loss: 4.794061
Validation loss decreased (4.831202 --&gt; 4.794061).  Saving model ...
Epoch: 18 	Training Loss: 4.784320 	Validation Loss: 4.789313
Validation loss decreased (4.794061 --&gt; 4.789313).  Saving model ...
Epoch: 19 	Training Loss: 4.766418 	Validation Loss: 4.761996
Validation loss decreased (4.789313 --&gt; 4.761996).  Saving model ...
Epoch: 20 	Training Loss: 4.753371 	Validation Loss: 4.735563
Validation loss decreased (4.761996 --&gt; 4.735563).  Saving model ...
Epoch: 21 	Training Loss: 4.739175 	Validation Loss: 4.726181
Validation loss decreased (4.735563 --&gt; 4.726181).  Saving model ...
Epoch: 22 	Training Loss: 4.730777 	Validation Loss: 4.704348
Validation loss decreased (4.726181 --&gt; 4.704348).  Saving model ...
Epoch: 23 	Training Loss: 4.719575 	Validation Loss: 4.715103
Epoch: 24 	Training Loss: 4.700461 	Validation Loss: 4.713972
Epoch: 25 	Training Loss: 4.694102 	Validation Loss: 4.718571
Epoch: 26 	Training Loss: 4.685789 	Validation Loss: 4.667683
Validation loss decreased (4.704348 --&gt; 4.667683).  Saving model ...
Epoch: 27 	Training Loss: 4.678551 	Validation Loss: 4.738264
Epoch: 28 	Training Loss: 4.655915 	Validation Loss: 4.655213
Validation loss decreased (4.667683 --&gt; 4.655213).  Saving model ...
Epoch: 29 	Training Loss: 4.638356 	Validation Loss: 4.624770
Validation loss decreased (4.655213 --&gt; 4.624770).  Saving model ...
Epoch: 30 	Training Loss: 4.599441 	Validation Loss: 4.580359
Validation loss decreased (4.624770 --&gt; 4.580359).  Saving model ...
Epoch: 31 	Training Loss: 4.579650 	Validation Loss: 4.583634
Epoch: 32 	Training Loss: 4.550874 	Validation Loss: 4.541313
Validation loss decreased (4.580359 --&gt; 4.541313).  Saving model ...
Epoch: 33 	Training Loss: 4.524090 	Validation Loss: 4.576626
Epoch: 34 	Training Loss: 4.510769 	Validation Loss: 4.528195
Validation loss decreased (4.541313 --&gt; 4.528195).  Saving model ...
Epoch: 35 	Training Loss: 4.490856 	Validation Loss: 4.605874
Epoch: 36 	Training Loss: 4.471101 	Validation Loss: 4.477000
Validation loss decreased (4.528195 --&gt; 4.477000).  Saving model ...
Epoch: 37 	Training Loss: 4.445481 	Validation Loss: 4.467291
Validation loss decreased (4.477000 --&gt; 4.467291).  Saving model ...
Epoch: 38 	Training Loss: 4.441258 	Validation Loss: 4.482244
Epoch: 39 	Training Loss: 4.422474 	Validation Loss: 4.417659
Validation loss decreased (4.467291 --&gt; 4.417659).  Saving model ...
Epoch: 40 	Training Loss: 4.396149 	Validation Loss: 4.442686
Epoch: 41 	Training Loss: 4.377169 	Validation Loss: 4.504769
Epoch: 42 	Training Loss: 4.373462 	Validation Loss: 4.359071
Validation loss decreased (4.417659 --&gt; 4.359071).  Saving model ...
Epoch: 43 	Training Loss: 4.326887 	Validation Loss: 4.436461
Epoch: 44 	Training Loss: 4.332285 	Validation Loss: 4.445480
Epoch: 45 	Training Loss: 4.313554 	Validation Loss: 4.393168
Epoch: 46 	Training Loss: 4.304152 	Validation Loss: 4.344779
Validation loss decreased (4.359071 --&gt; 4.344779).  Saving model ...
Epoch: 47 	Training Loss: 4.302211 	Validation Loss: 4.380830
Epoch: 48 	Training Loss: 4.283996 	Validation Loss: 4.357018
Epoch: 49 	Training Loss: 4.255819 	Validation Loss: 4.336133
Validation loss decreased (4.344779 --&gt; 4.336133).  Saving model ...
Epoch: 50 	Training Loss: 4.254862 	Validation Loss: 4.340689
Epoch: 51 	Training Loss: 4.232910 	Validation Loss: 4.313267
Validation loss decreased (4.336133 --&gt; 4.313267).  Saving model ...
Epoch: 52 	Training Loss: 4.217608 	Validation Loss: 4.285990
Validation loss decreased (4.313267 --&gt; 4.285990).  Saving model ...
Epoch: 53 	Training Loss: 4.208279 	Validation Loss: 4.386411
Epoch: 54 	Training Loss: 4.199060 	Validation Loss: 4.289718
Epoch: 55 	Training Loss: 4.186054 	Validation Loss: 4.288615
Epoch: 56 	Training Loss: 4.195400 	Validation Loss: 4.397564
Epoch: 57 	Training Loss: 4.174210 	Validation Loss: 4.348804
Epoch: 58 	Training Loss: 4.183610 	Validation Loss: 4.288554
Epoch: 59 	Training Loss: 4.172781 	Validation Loss: 4.266074
Validation loss decreased (4.285990 --&gt; 4.266074).  Saving model ...
Epoch: 60 	Training Loss: 4.138980 	Validation Loss: 4.301911
Epoch: 61 	Training Loss: 4.129215 	Validation Loss: 4.246749
Validation loss decreased (4.266074 --&gt; 4.246749).  Saving model ...
Epoch: 62 	Training Loss: 4.140269 	Validation Loss: 4.303463
Epoch: 63 	Training Loss: 4.101073 	Validation Loss: 4.237103
Validation loss decreased (4.246749 --&gt; 4.237103).  Saving model ...
Epoch: 64 	Training Loss: 4.103413 	Validation Loss: 4.350204
Epoch: 65 	Training Loss: 4.089620 	Validation Loss: 4.451767
Epoch: 66 	Training Loss: 4.078926 	Validation Loss: 4.230675
Validation loss decreased (4.237103 --&gt; 4.230675).  Saving model ...
Epoch: 67 	Training Loss: 4.071733 	Validation Loss: 4.239111
Epoch: 68 	Training Loss: 4.068038 	Validation Loss: 4.254570
Epoch: 69 	Training Loss: 4.044674 	Validation Loss: 4.164042
Validation loss decreased (4.230675 --&gt; 4.164042).  Saving model ...
Epoch: 70 	Training Loss: 4.050068 	Validation Loss: 4.201971
Epoch: 71 	Training Loss: 4.028053 	Validation Loss: 4.210199
Epoch: 72 	Training Loss: 4.016881 	Validation Loss: 4.224771
Epoch: 73 	Training Loss: 4.000968 	Validation Loss: 4.238995
Epoch: 74 	Training Loss: 4.011263 	Validation Loss: 4.206831
Epoch: 75 	Training Loss: 3.997978 	Validation Loss: 4.190726
Epoch: 76 	Training Loss: 3.989194 	Validation Loss: 4.191176
Epoch: 77 	Training Loss: 3.962977 	Validation Loss: 4.268704
Epoch: 78 	Training Loss: 3.979959 	Validation Loss: 4.134899
Validation loss decreased (4.164042 --&gt; 4.134899).  Saving model ...
Epoch: 79 	Training Loss: 3.974941 	Validation Loss: 4.235431
Epoch: 80 	Training Loss: 3.955604 	Validation Loss: 4.197137
Epoch: 81 	Training Loss: 3.938581 	Validation Loss: 4.229312
Epoch: 82 	Training Loss: 3.936051 	Validation Loss: 4.184020
Epoch: 83 	Training Loss: 3.912572 	Validation Loss: 4.167051
Epoch: 84 	Training Loss: 3.896236 	Validation Loss: 4.324294
Epoch: 85 	Training Loss: 3.914523 	Validation Loss: 4.193493
Epoch: 86 	Training Loss: 3.908394 	Validation Loss: 4.194742
Epoch: 87 	Training Loss: 3.879606 	Validation Loss: 4.134439
Validation loss decreased (4.134899 --&gt; 4.134439).  Saving model ...
Epoch: 88 	Training Loss: 3.882047 	Validation Loss: 4.107505
Validation loss decreased (4.134439 --&gt; 4.107505).  Saving model ...
Epoch: 89 	Training Loss: 3.864861 	Validation Loss: 4.142534
Epoch: 90 	Training Loss: 3.838241 	Validation Loss: 4.144615
Epoch: 91 	Training Loss: 3.839029 	Validation Loss: 4.156985
Epoch: 92 	Training Loss: 3.851252 	Validation Loss: 4.129681
Epoch: 93 	Training Loss: 3.829329 	Validation Loss: 4.172064
Epoch: 94 	Training Loss: 3.823297 	Validation Loss: 4.156325
Epoch: 95 	Training Loss: 3.829830 	Validation Loss: 4.176766
Epoch: 96 	Training Loss: 3.816242 	Validation Loss: 4.148245
Epoch: 97 	Training Loss: 3.832370 	Validation Loss: 4.094970
Validation loss decreased (4.107505 --&gt; 4.094970).  Saving model ...
Epoch: 98 	Training Loss: 3.802543 	Validation Loss: 4.204937
Epoch: 99 	Training Loss: 3.787935 	Validation Loss: 4.156454
Epoch: 100 	Training Loss: 3.788891 	Validation Loss: 4.059719
Validation loss decreased (4.094970 --&gt; 4.059719).  Saving model ...
Epoch: 101 	Training Loss: 3.764339 	Validation Loss: 4.122102
Epoch: 102 	Training Loss: 3.766723 	Validation Loss: 4.103717
Epoch: 103 	Training Loss: 3.745400 	Validation Loss: 4.076423
Epoch: 104 	Training Loss: 3.753478 	Validation Loss: 4.302872
Epoch: 105 	Training Loss: 3.730629 	Validation Loss: 4.033256
Validation loss decreased (4.059719 --&gt; 4.033256).  Saving model ...
Epoch: 106 	Training Loss: 3.717445 	Validation Loss: 4.130816
Epoch: 107 	Training Loss: 3.721087 	Validation Loss: 4.112819
Epoch: 108 	Training Loss: 3.723368 	Validation Loss: 4.083420
Epoch: 109 	Training Loss: 3.683190 	Validation Loss: 4.080729
Epoch: 110 	Training Loss: 3.686573 	Validation Loss: 4.045589
Epoch: 111 	Training Loss: 3.669239 	Validation Loss: 4.095308
Epoch: 112 	Training Loss: 3.657576 	Validation Loss: 4.138210
Epoch: 113 	Training Loss: 3.659616 	Validation Loss: 4.131644
Epoch: 114 	Training Loss: 3.673894 	Validation Loss: 4.041183
Epoch: 115 	Training Loss: 3.635828 	Validation Loss: 4.098026
Epoch: 116 	Training Loss: 3.655855 	Validation Loss: 4.100633
Epoch: 117 	Training Loss: 3.648139 	Validation Loss: 4.117939
Epoch: 118 	Training Loss: 3.651876 	Validation Loss: 4.091963
Epoch: 119 	Training Loss: 3.633613 	Validation Loss: 4.105678
Epoch: 120 	Training Loss: 3.591188 	Validation Loss: 4.237745
Epoch: 121 	Training Loss: 3.621310 	Validation Loss: 4.127603
Epoch: 122 	Training Loss: 3.573935 	Validation Loss: 4.113775
Epoch: 123 	Training Loss: 3.577533 	Validation Loss: 4.151799
Epoch: 124 	Training Loss: 3.572874 	Validation Loss: 4.148496
Epoch: 125 	Training Loss: 3.569172 	Validation Loss: 4.117895
Epoch: 126 	Training Loss: 3.580592 	Validation Loss: 4.083546
Epoch: 127 	Training Loss: 3.571531 	Validation Loss: 4.079367
Epoch: 128 	Training Loss: 3.555960 	Validation Loss: 4.012221
Validation loss decreased (4.033256 --&gt; 4.012221).  Saving model ...
Epoch: 129 	Training Loss: 3.543710 	Validation Loss: 4.077880
Epoch: 130 	Training Loss: 3.534067 	Validation Loss: 4.102357
Epoch: 131 	Training Loss: 3.536793 	Validation Loss: 4.051124
Epoch: 132 	Training Loss: 3.526774 	Validation Loss: 4.090284
Epoch: 133 	Training Loss: 3.513945 	Validation Loss: 4.076032
Epoch: 134 	Training Loss: 3.500629 	Validation Loss: 4.063320
Epoch: 135 	Training Loss: 3.526125 	Validation Loss: 4.072418
Epoch: 136 	Training Loss: 3.482297 	Validation Loss: 4.026421
Epoch: 137 	Training Loss: 3.475632 	Validation Loss: 4.034180
Epoch: 138 	Training Loss: 3.468718 	Validation Loss: 4.053487
Epoch: 139 	Training Loss: 3.481152 	Validation Loss: 4.033788
Epoch: 140 	Training Loss: 3.467103 	Validation Loss: 4.099402
Epoch: 141 	Training Loss: 3.438830 	Validation Loss: 4.004573
Validation loss decreased (4.012221 --&gt; 4.004573).  Saving model ...
Epoch: 142 	Training Loss: 3.479976 	Validation Loss: 4.170321
Epoch: 143 	Training Loss: 3.445631 	Validation Loss: 3.968201
Validation loss decreased (4.004573 --&gt; 3.968201).  Saving model ...
Epoch: 144 	Training Loss: 3.462523 	Validation Loss: 4.080863
Epoch: 145 	Training Loss: 3.451648 	Validation Loss: 4.017190
Epoch: 146 	Training Loss: 3.420381 	Validation Loss: 4.111290
Epoch: 147 	Training Loss: 3.431769 	Validation Loss: 4.067592
Epoch: 148 	Training Loss: 3.459422 	Validation Loss: 4.043458
Epoch: 149 	Training Loss: 3.423268 	Validation Loss: 4.078815
Epoch: 150 	Training Loss: 3.397910 	Validation Loss: 3.978523
Epoch: 151 	Training Loss: 3.411642 	Validation Loss: 3.964236
Validation loss decreased (3.968201 --&gt; 3.964236).  Saving model ...
Epoch: 152 	Training Loss: 3.396429 	Validation Loss: 4.073243
Epoch: 153 	Training Loss: 3.386742 	Validation Loss: 4.089198
Epoch: 154 	Training Loss: 3.391864 	Validation Loss: 4.140378
Epoch: 155 	Training Loss: 3.381126 	Validation Loss: 4.023870
Epoch: 156 	Training Loss: 3.408147 	Validation Loss: 4.052525
Epoch: 157 	Training Loss: 3.352920 	Validation Loss: 4.027601
Epoch: 158 	Training Loss: 3.391403 	Validation Loss: 4.032993
Epoch: 159 	Training Loss: 3.372732 	Validation Loss: 4.037481
Epoch: 160 	Training Loss: 3.322752 	Validation Loss: 4.143700
Epoch: 161 	Training Loss: 3.327201 	Validation Loss: 4.026756
Epoch: 162 	Training Loss: 3.360904 	Validation Loss: 4.165314
Epoch: 163 	Training Loss: 3.348274 	Validation Loss: 4.077931
Epoch: 164 	Training Loss: 3.347078 	Validation Loss: 4.051050
Epoch: 165 	Training Loss: 3.307517 	Validation Loss: 4.050797
Epoch: 166 	Training Loss: 3.317667 	Validation Loss: 4.047609
Epoch: 167 	Training Loss: 3.318123 	Validation Loss: 4.011051
Epoch: 168 	Training Loss: 3.301238 	Validation Loss: 4.046231
Epoch: 169 	Training Loss: 3.299343 	Validation Loss: 4.040300
Epoch: 170 	Training Loss: 3.303644 	Validation Loss: 4.109426
Epoch: 171 	Training Loss: 3.301054 	Validation Loss: 4.074551
Epoch: 172 	Training Loss: 3.322803 	Validation Loss: 4.252660
Epoch: 173 	Training Loss: 3.280382 	Validation Loss: 4.015139
Epoch: 174 	Training Loss: 3.274717 	Validation Loss: 3.928114
Validation loss decreased (3.964236 --&gt; 3.928114).  Saving model ...
Epoch: 175 	Training Loss: 3.275307 	Validation Loss: 4.143526
Epoch: 176 	Training Loss: 3.262664 	Validation Loss: 4.074123
Epoch: 177 	Training Loss: 3.278778 	Validation Loss: 4.093615
Epoch: 178 	Training Loss: 3.270026 	Validation Loss: 3.980084
Epoch: 179 	Training Loss: 3.246498 	Validation Loss: 4.081097
Epoch: 180 	Training Loss: 3.248898 	Validation Loss: 3.953602
Epoch: 181 	Training Loss: 3.260961 	Validation Loss: 4.103785
Epoch: 182 	Training Loss: 3.227532 	Validation Loss: 4.051767
Epoch: 183 	Training Loss: 3.236335 	Validation Loss: 3.998849
Epoch: 184 	Training Loss: 3.213779 	Validation Loss: 3.986136
Epoch: 185 	Training Loss: 3.233288 	Validation Loss: 4.080288
Epoch: 186 	Training Loss: 3.239949 	Validation Loss: 4.074043
Epoch: 187 	Training Loss: 3.219545 	Validation Loss: 4.031874
Epoch: 188 	Training Loss: 3.205016 	Validation Loss: 4.128262
Epoch: 189 	Training Loss: 3.195826 	Validation Loss: 4.012141
Epoch: 190 	Training Loss: 3.217094 	Validation Loss: 4.168865
Epoch: 191 	Training Loss: 3.187944 	Validation Loss: 4.134752
Epoch: 192 	Training Loss: 3.158413 	Validation Loss: 4.113627
Epoch: 193 	Training Loss: 3.194490 	Validation Loss: 4.017583
Epoch: 194 	Training Loss: 3.146890 	Validation Loss: 4.096797
Epoch: 195 	Training Loss: 3.215317 	Validation Loss: 4.020546
Epoch: 196 	Training Loss: 3.193280 	Validation Loss: 4.027123
Epoch: 197 	Training Loss: 3.193122 	Validation Loss: 4.041057
Epoch: 198 	Training Loss: 3.163487 	Validation Loss: 4.050038
Epoch: 199 	Training Loss: 3.192426 	Validation Loss: 4.083682
Epoch: 200 	Training Loss: 3.189723 	Validation Loss: 3.988435
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<h1 id="Training-Notes">Training Notes<a class="anchor-link" href="#Training-Notes">&#182;</a></h1><h3 id="First-run:--+10-Epochs">First run:  +10 Epochs<a class="anchor-link" href="#First-run:--+10-Epochs">&#182;</a></h3><p>Ran training for 10 epochs and saw loss go from:</p>
<ul>
<li>Epoch: 1  Training Loss: 4.890159     Validation Loss: 4.888753</li>
<li>.......</li>
<li>Epoch: 10     Training Loss: 4.867283     Validation Loss: 4.868416</li>
</ul>
<p>Given that it successfully made it through training, though performance was poor,
I'm going to increase the learning rate and number of epochs... then go to lunch.</p>
<hr>
<h3 id="Second-run:-+50-epochs">Second run: +50 epochs<a class="anchor-link" href="#Second-run:-+50-epochs">&#182;</a></h3><p>Ran training for 50 epochs and saw loss go from:</p>
<ul>
<li>Epoch: 11     Training Loss: 4.864773     Validation Loss: 4.866790</li>
<li>......</li>
<li>Epoch: 50     Training Loss: 4.238782     Validation Loss: 4.352975</li>
</ul>
<p>Now at a total training of 60 epochs, the model has had a ~ 11% decrease in validation loss an a 420% increase in test accuracy. I'm going to leave the model running now for a longer training period, hoping that is will achieve the specified 10% performance threshold.</p>
<hr>
<h3 id="Third-run:-+100-epochs">Third run: +100 epochs<a class="anchor-link" href="#Third-run:-+100-epochs">&#182;</a></h3><p>Ran training for 100 epochs and saw loss go from:</p>
<ul>
<li>Epoch: 1  Training Loss: 4.253037     Validation Loss: 4.326618</li>
<li>......</li>
<li>Epoch: 100    Training Loss: 3.441581     Validation Loss: 4.048419</li>
</ul>
<p>Around epoch 10 (70 in total) I first began to see a noticable divergence between the training and validation loss, which continued to amplify all the way to epoch 100. Both losses continued to lessen, however, I'm concerned that it is somewhat over fitting as the gap between the two began to average ~0.5 wheras it has started at ~0.01.</p>
<hr>
<h3 id="RE-TRAIN-from-scratch:-+200-epochs">RE-TRAIN from scratch: +200 epochs<a class="anchor-link" href="#RE-TRAIN-from-scratch:-+200-epochs">&#182;</a></h3><p>Re-trained model for 100 epochs and saw loss go from:</p>
<ul>
<li>Epoch: 1  Training Loss: 4.880341     Validation Loss: 4.872962</li>
<li>......</li>
<li>Epoch: 200    Training Loss: 3.189723     Validation Loss: 3.988435</li>
</ul>
<p>While working with the transfer_model I accidently overwrote the saved progress of the scratch_model and reset all training progress. I decided to let the model train overnight for 200 epochs with all parameters consistengt, which brought it back to a similary loss performance. My initial worry about the divergence of training and validation loss only worsened as the model approached the 200th epoch, suggesting that it was indeed starting to over fit around the 70-100 epoch.</p>

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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.  Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 10%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">loaders</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">):</span>

    <span class="c1"># monitor test loss and accuracy</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="mf">0.</span>
    <span class="n">correct</span> <span class="o">=</span> <span class="mf">0.</span>
    <span class="n">total</span> <span class="o">=</span> <span class="mf">0.</span>

    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">loaders</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]):</span>
        <span class="c1"># move to GPU</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="c1"># forward pass: compute predicted outputs by passing inputs to the model</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
        <span class="c1"># calculate the loss</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
        <span class="c1"># update average test loss </span>
        <span class="n">test_loss</span> <span class="o">=</span> <span class="n">test_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">test_loss</span><span class="p">))</span>
        <span class="c1"># convert output probabilities to predicted class</span>
        <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
        <span class="c1"># compare predictions to true label</span>
        <span class="n">correct</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">squeeze</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">)))</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
        <span class="n">total</span> <span class="o">+=</span> <span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss: </span><span class="si">{:.6f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Test Accuracy: </span><span class="si">%2d%%</span><span class="s1"> (</span><span class="si">%2d</span><span class="s1">/</span><span class="si">%2d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span>
        <span class="mf">100.</span> <span class="o">*</span> <span class="n">correct</span> <span class="o">/</span> <span class="n">total</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="n">total</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># call test function    </span>
<span class="n">test</span><span class="p">(</span><span class="n">loaders_scratch</span><span class="p">,</span> <span class="n">model_scratch</span><span class="p">,</span> <span class="n">criterion_scratch</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
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<pre>Test Loss: 3.910456


Test Accuracy: 11% (100/836)
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<h1 id="Test-Log">Test Log<a class="anchor-link" href="#Test-Log">&#182;</a></h1><h3 id="First-test-run:-+10-epochs">First test run: +10 epochs<a class="anchor-link" href="#First-test-run:-+10-epochs">&#182;</a></h3><ul>
<li>Test Loss: 4.866813</li>
<li>Test Accuracy:  1% (10/836)</li>
</ul>
<p>After the first 10 epochs of training, the model tested at 1% accuracy (10/836). This is practically guessing, however it gave me confidence on further training the same model with an increased learning rate.</p>
<hr>
<h3 id="Second-test-run:-+50-epochs">Second test run: +50 epochs<a class="anchor-link" href="#Second-test-run:-+50-epochs">&#182;</a></h3><ul>
<li>Test Loss: 4.367391</li>
<li>Test Accuracy:  5% (42/836)</li>
</ul>
<p>Test accuracy now at 5% (42/836). I'm very happy with this improvement! Keeping all parameters the same, I'm going to let the model run for a full 100 epochs, and hopefully pass the 10% threshold for this challenge.</p>
<hr>
<h3 id="Third-test-run:-+100-epochs">Third test run: +100 epochs<a class="anchor-link" href="#Third-test-run:-+100-epochs">&#182;</a></h3><ul>
<li>Test Loss: 4.073663</li>
<li>Test Accuracy: 11% (94/836)</li>
</ul>
<p>By a hair did this model pass the threshold of 10% accuracy. I was hoping that it would perform closer to ~15%, however, while I feel that longer training could sqeeze a few more points towards accuracy.</p>
<hr>
<h3 id="RE-TRAINED-MODEL:-First-test-run-+-200-epochs">RE-TRAINED MODEL: First test run + 200 epochs<a class="anchor-link" href="#RE-TRAINED-MODEL:-First-test-run-+-200-epochs">&#182;</a></h3><ul>
<li>Test Loss: 3.910456</li>
<li>Test Accuracy: 11% (100/836)</li>
</ul>
<p>Only 1 image away from 12% accuracy! I'm sure that if I kept running the test method I'd clear that hurdle by chance, however, I'm going to leave it here. The model is definately gaining it's ability to correctly classify the breed images. I would be curious to see if the is a natural bias being developed to which breeds it's correctly classifying (small dogs, long hair, etc...). After submitting the project I'll likely try to dig into that a bit more.</p>

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<p><a id='step4'></a></p>
<h2 id="Step-4:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-4:-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>
<h3 id="(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">&#182;</a></h3><p>Use the code cell below to write three separate <a href="http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader">data loaders</a> for the training, validation, and test datasets of dog images (located at <code>dogImages/train</code>, <code>dogImages/valid</code>, and <code>dogImages/test</code>, respectively).</p>
<p>If you like, <strong>you are welcome to use the same data loaders from the previous step</strong>, when you created a CNN from scratch.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## TODO: Specify data loaders</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;data_images/&#39;</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># Handle transforms using torchvision, preparing images for VGG19</span>
<span class="c1"># pretrained model input.</span>
<span class="n">image_transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> 
                                <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                     <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>

<span class="c1"># Load in data directories from each relevant folder and transform</span>
<span class="n">data_transfer</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/train&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">),</span>
    <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/valid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">),</span>
    <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/test&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">image_transform</span><span class="p">)</span>
<span class="p">}</span>

<span class="c1"># Prepare test/train/valid data loaders</span>
<span class="n">loaders_transfer</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_transfer</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_transfer</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">data_transfer</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">}</span>

<span class="c1"># Determine number of dog breed classes</span>
<span class="n">classes</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;dogs/train&#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>Use transfer learning to create a CNN to classify dog breed.  Use the code cell below, and save your initialized model as the variable <code>model_transfer</code>.</p>

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

<span class="c1"># Load pretrained vgg19 model from pytorch</span>
<span class="n">model_transfer</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg19</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># Freeze training for all &quot;features&quot; layers in the VGG16 model</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
    <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>
    
<span class="c1"># Map last layer of VGG19 model to new linear layer</span>
<span class="c1"># with output of number of dog breed classes.</span>
<span class="n">n_inputs</span> <span class="o">=</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">.</span><span class="n">in_features</span>
<span class="n">model_transfer</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span>

<span class="c1"># after completing your model, if GPU is available, move the model to GPU</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Using cuda&#39;</span><span class="p">)</span>
    <span class="n">model_transfer</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<pre>Using cuda
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<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>

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<p><strong>Answer:</strong> After loading in the pretrained VGG19 model, I froze it to prevent any further training by setting each feature param to not use gradient descent (back-propogration).  I then mapped a new fully connected layer's (FCL) with the same number of outputs as found in our breeds classes set and inputs as the current final layer. Doing this allows for our FCL to output predictions for each of the breed classes and be inserted seamlessly into the pretrained model.</p>
<p>The VGG19 model uses a small kernal size which works well for detailed feature extraction and differentiation (breed coat patterns/hair). Also, the model extracts features from a given image up to a depth of 512 (dims), in the 4th and 5th convolutional layer of the model, allowing it to better learn finely detailed and complex features of a given image.</p>

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<h3 id="(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">(IMPLEMENTATION) Specify Loss Function and Optimizer<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">&#182;</a></h3><p>Use the next code cell to specify a <a href="http://pytorch.org/docs/master/nn.html#loss-functions">loss function</a> and <a href="http://pytorch.org/docs/master/optim.html">optimizer</a>.  Save the chosen loss function as <code>criterion_transfer</code>, and the optimizer as <code>optimizer_transfer</code> below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a criterion (NNLoss and Softmax) and an optimizer. Using these</span>
<span class="c1"># options based on previous lectures and notebook excersizes.</span>

<span class="c1"># specify loss function (categorical cross-entropy)</span>
<span class="n">criterion_transfer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>

<span class="c1"># specify optimizer (stochastic gradient descent) and learning rate = 0.0025</span>
<span class="n">optimizer_transfer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model_transfer</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0025</span><span class="p">)</span>
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<h3 id="(IMPLEMENTATION)-Train-and-Validate-the-Model">(IMPLEMENTATION) Train and Validate the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-and-Validate-the-Model">&#182;</a></h3><p>Train and validate your model in the code cell below.  <a href="http://pytorch.org/docs/master/notes/serialization.html">Save the final model parameters</a> at filepath <code>'model_transfer.pt'</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># train the model</span>
<span class="n">model_transfer</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">loaders_transfer</span><span class="p">,</span> <span class="n">model_transfer</span><span class="p">,</span> <span class="n">optimizer_transfer</span><span class="p">,</span> <span class="n">criterion_transfer</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="s1">&#39;model_transfer.pt&#39;</span><span class="p">)</span>

<span class="c1"># load the model that got the best validation accuracy (uncomment the line below)</span>
<span class="n">model_transfer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;model_transfer.pt&#39;</span><span class="p">))</span>
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<pre>Epoch: 1 	Training Loss: 2.058879 	Validation Loss: 1.254177
Validation loss decreased (inf --&gt; 1.254177).  Saving model ...
Epoch: 2 	Training Loss: 1.272203 	Validation Loss: 1.009339
Validation loss decreased (1.254177 --&gt; 1.009339).  Saving model ...
Epoch: 3 	Training Loss: 1.135368 	Validation Loss: 0.943359
Validation loss decreased (1.009339 --&gt; 0.943359).  Saving model ...
Epoch: 4 	Training Loss: 1.041458 	Validation Loss: 0.936071
Validation loss decreased (0.943359 --&gt; 0.936071).  Saving model ...
Epoch: 5 	Training Loss: 0.951038 	Validation Loss: 0.887724
Validation loss decreased (0.936071 --&gt; 0.887724).  Saving model ...
Epoch: 6 	Training Loss: 0.903120 	Validation Loss: 0.859338
Validation loss decreased (0.887724 --&gt; 0.859338).  Saving model ...
Epoch: 7 	Training Loss: 0.894745 	Validation Loss: 0.809760
Validation loss decreased (0.859338 --&gt; 0.809760).  Saving model ...
Epoch: 8 	Training Loss: 0.836879 	Validation Loss: 0.899775
Epoch: 9 	Training Loss: 0.844444 	Validation Loss: 0.916908
Epoch: 10 	Training Loss: 0.827263 	Validation Loss: 0.820414
Epoch: 11 	Training Loss: 0.777345 	Validation Loss: 0.832883
Epoch: 12 	Training Loss: 0.787402 	Validation Loss: 0.806496
Validation loss decreased (0.809760 --&gt; 0.806496).  Saving model ...
Epoch: 13 	Training Loss: 0.746386 	Validation Loss: 0.880260
Epoch: 14 	Training Loss: 0.731148 	Validation Loss: 0.779687
Validation loss decreased (0.806496 --&gt; 0.779687).  Saving model ...
Epoch: 15 	Training Loss: 0.744613 	Validation Loss: 0.789804
Epoch: 16 	Training Loss: 0.708807 	Validation Loss: 0.732276
Validation loss decreased (0.779687 --&gt; 0.732276).  Saving model ...
Epoch: 17 	Training Loss: 0.678995 	Validation Loss: 0.768041
Epoch: 18 	Training Loss: 0.720818 	Validation Loss: 0.771726
Epoch: 19 	Training Loss: 0.695403 	Validation Loss: 0.808574
Epoch: 20 	Training Loss: 0.688880 	Validation Loss: 0.758507
Epoch: 21 	Training Loss: 0.688428 	Validation Loss: 0.846015
Epoch: 22 	Training Loss: 0.683548 	Validation Loss: 0.766458
Epoch: 23 	Training Loss: 0.650265 	Validation Loss: 0.712527
Validation loss decreased (0.732276 --&gt; 0.712527).  Saving model ...
Epoch: 24 	Training Loss: 0.648041 	Validation Loss: 0.808573
Epoch: 25 	Training Loss: 0.659139 	Validation Loss: 0.818131
Epoch: 26 	Training Loss: 0.647058 	Validation Loss: 0.763152
Epoch: 27 	Training Loss: 0.665725 	Validation Loss: 0.777877
Epoch: 28 	Training Loss: 0.642673 	Validation Loss: 0.755337
Epoch: 29 	Training Loss: 0.641316 	Validation Loss: 0.719336
Epoch: 30 	Training Loss: 0.622205 	Validation Loss: 0.827246
Epoch: 31 	Training Loss: 0.625022 	Validation Loss: 0.717912
Epoch: 32 	Training Loss: 0.600347 	Validation Loss: 0.793140
Epoch: 33 	Training Loss: 0.627620 	Validation Loss: 0.697601
Validation loss decreased (0.712527 --&gt; 0.697601).  Saving model ...
Epoch: 34 	Training Loss: 0.616134 	Validation Loss: 0.744844
Epoch: 35 	Training Loss: 0.603486 	Validation Loss: 0.820955
Epoch: 36 	Training Loss: 0.578588 	Validation Loss: 0.859830
Epoch: 37 	Training Loss: 0.558180 	Validation Loss: 0.841548
Epoch: 38 	Training Loss: 0.583007 	Validation Loss: 0.749793
Epoch: 39 	Training Loss: 0.563773 	Validation Loss: 0.782859
Epoch: 40 	Training Loss: 0.581598 	Validation Loss: 0.783673
Epoch: 41 	Training Loss: 0.563157 	Validation Loss: 0.797869
Epoch: 42 	Training Loss: 0.553717 	Validation Loss: 0.718943
Epoch: 43 	Training Loss: 0.573964 	Validation Loss: 0.728775
Epoch: 44 	Training Loss: 0.548663 	Validation Loss: 0.750029
Epoch: 45 	Training Loss: 0.561827 	Validation Loss: 0.824757
Epoch: 46 	Training Loss: 0.550700 	Validation Loss: 0.775790
Epoch: 47 	Training Loss: 0.572863 	Validation Loss: 0.768775
Epoch: 48 	Training Loss: 0.544405 	Validation Loss: 0.721394
Epoch: 49 	Training Loss: 0.530218 	Validation Loss: 0.721164
Epoch: 50 	Training Loss: 0.543619 	Validation Loss: 0.763713
Epoch: 51 	Training Loss: 0.527515 	Validation Loss: 0.777701
Epoch: 52 	Training Loss: 0.541559 	Validation Loss: 0.724036
Epoch: 53 	Training Loss: 0.537427 	Validation Loss: 0.831114
Epoch: 54 	Training Loss: 0.523978 	Validation Loss: 0.739289
Epoch: 55 	Training Loss: 0.527521 	Validation Loss: 0.737306
Epoch: 56 	Training Loss: 0.512520 	Validation Loss: 0.859902
Epoch: 57 	Training Loss: 0.539111 	Validation Loss: 0.891805
Epoch: 58 	Training Loss: 0.524583 	Validation Loss: 0.831124
Epoch: 59 	Training Loss: 0.501639 	Validation Loss: 0.739503
Epoch: 60 	Training Loss: 0.526326 	Validation Loss: 0.761517
Epoch: 61 	Training Loss: 0.511507 	Validation Loss: 0.682651
Validation loss decreased (0.697601 --&gt; 0.682651).  Saving model ...
Epoch: 62 	Training Loss: 0.501643 	Validation Loss: 0.788981
Epoch: 63 	Training Loss: 0.517928 	Validation Loss: 0.810908
Epoch: 64 	Training Loss: 0.505348 	Validation Loss: 0.763284
Epoch: 65 	Training Loss: 0.517869 	Validation Loss: 0.720285
Epoch: 66 	Training Loss: 0.500823 	Validation Loss: 0.806762
Epoch: 67 	Training Loss: 0.503160 	Validation Loss: 0.701153
Epoch: 68 	Training Loss: 0.504553 	Validation Loss: 0.799154
Epoch: 69 	Training Loss: 0.482317 	Validation Loss: 0.707783
Epoch: 70 	Training Loss: 0.499152 	Validation Loss: 0.792099
Epoch: 71 	Training Loss: 0.477714 	Validation Loss: 0.758449
Epoch: 72 	Training Loss: 0.479538 	Validation Loss: 0.807600
Epoch: 73 	Training Loss: 0.495850 	Validation Loss: 0.760261
Epoch: 74 	Training Loss: 0.483958 	Validation Loss: 0.800952
Epoch: 75 	Training Loss: 0.498477 	Validation Loss: 0.763333
Epoch: 76 	Training Loss: 0.505204 	Validation Loss: 0.794010
Epoch: 77 	Training Loss: 0.489825 	Validation Loss: 0.724724
Epoch: 78 	Training Loss: 0.474058 	Validation Loss: 0.686943
Epoch: 79 	Training Loss: 0.461105 	Validation Loss: 0.713961
Epoch: 80 	Training Loss: 0.466145 	Validation Loss: 0.806983
Epoch: 81 	Training Loss: 0.497100 	Validation Loss: 0.758113
Epoch: 82 	Training Loss: 0.491648 	Validation Loss: 0.743453
Epoch: 83 	Training Loss: 0.456397 	Validation Loss: 0.803961
Epoch: 84 	Training Loss: 0.462953 	Validation Loss: 0.780921
Epoch: 85 	Training Loss: 0.473632 	Validation Loss: 0.801905
Epoch: 86 	Training Loss: 0.462184 	Validation Loss: 0.765969
Epoch: 87 	Training Loss: 0.453579 	Validation Loss: 0.816039
Epoch: 88 	Training Loss: 0.471410 	Validation Loss: 0.694653
Epoch: 89 	Training Loss: 0.447870 	Validation Loss: 0.716144
Epoch: 90 	Training Loss: 0.455548 	Validation Loss: 0.815429
Epoch: 91 	Training Loss: 0.457286 	Validation Loss: 0.729398
Epoch: 92 	Training Loss: 0.439323 	Validation Loss: 0.802749
Epoch: 93 	Training Loss: 0.463945 	Validation Loss: 0.736609
Epoch: 94 	Training Loss: 0.457786 	Validation Loss: 0.829427
Epoch: 95 	Training Loss: 0.423502 	Validation Loss: 0.689614
Epoch: 96 	Training Loss: 0.438303 	Validation Loss: 0.784257
Epoch: 97 	Training Loss: 0.451775 	Validation Loss: 0.746457
Epoch: 98 	Training Loss: 0.457576 	Validation Loss: 0.746602
Epoch: 99 	Training Loss: 0.448984 	Validation Loss: 0.802481
Epoch: 100 	Training Loss: 0.446848 	Validation Loss: 0.730253
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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. Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 60%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">(</span><span class="n">loaders_transfer</span><span class="p">,</span> <span class="n">model_transfer</span><span class="p">,</span> <span class="n">criterion_transfer</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
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<pre>Test Loss: 0.808622


Test Accuracy: 76% (643/836)
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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>

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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">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Variable</span>

<span class="c1"># list of class names by index, i.e. a name can be accessed like class_names[0]</span>
<span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">4</span><span class="p">:]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">data_transfer</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">classes</span><span class="p">]</span>

<span class="k">def</span> <span class="nf">predict_breed_transfer</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># Handle image transforms    </span>
    <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> 
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                    <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
                                                         <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])])</span>
    <span class="c1"># Load image and run through transform      </span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="c1"># Add dimension to tensor for number of images</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="c1"># Prediction placeholder</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="c1"># Depending on whether cuda is being used, make sure the passed tensor</span>
    <span class="c1"># is being moved between the cpu and GPU pre/post calculation.</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># We can now make our prediction by passing the image_variable through the</span>
        <span class="c1"># VGG16 network to get a prediction.</span>
        <span class="n">prediction</span> <span class="o">=</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>

    <span class="c1"># To access the index of the highest predicted class value, we access</span>
    <span class="c1"># the data of the prediction and find the index of it&#39;s highest propbablity</span>
    <span class="k">return</span> <span class="n">class_names</span><span class="p">[</span><span class="n">prediction</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">()]</span>
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<h3 id="Test-dog-breed-identifier-methood.">Test dog breed identifier methood.<a class="anchor-link" href="#Test-dog-breed-identifier-methood.">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predict_breed_transfer</span><span class="p">(</span><span class="s1">&#39;data_images/dogs/train/120.Pharaoh_hound/Pharaoh_hound_07749.jpg&#39;</span><span class="p">)</span>
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<pre>&#39;Pharaoh hound&#39;</pre>
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<p><a id='step5'></a></p>
<h2 id="Step-5:-Write-your-Algorithm">Step 5: Write your Algorithm<a class="anchor-link" href="#Step-5:-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>human_detector</code> functions developed above.  You are <strong>required</strong> to use your CNN from Step 4 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">run_app</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1">## handle cases for a human face, dog, and neither</span>
    <span class="n">template</span> <span class="o">=</span> <span class="kc">False</span>
    
    <span class="c1">## Depending on whether a dog or a human face is</span>
    <span class="c1">## detected, define a relevant template.</span>
    <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
        <span class="n">template</span> <span class="o">=</span> <span class="s1">&#39;This person looks like a </span><span class="si">{}</span><span class="se">\n\n</span><span class="s1">&#39;</span>
    <span class="k">elif</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
        <span class="n">template</span> <span class="o">=</span> <span class="s1">&#39;This dog looks like a </span><span class="si">{}</span><span class="se">\n\n</span><span class="s1">&#39;</span>
    
    <span class="c1">## If a template was defined, use it to format</span>
    <span class="c1">## the predected breed. If not, print error.</span>
    <span class="k">if</span> <span class="n">template</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">template</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">predict_breed_transfer</span><span class="p">(</span><span class="n">img_path</span><span class="p">)))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;This image does not contain humans or dogs: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
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<p><a id='step6'></a></p>
<h2 id="Step-6:-Test-Your-Algorithm">Step 6: Test Your Algorithm<a class="anchor-link" href="#Step-6:-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 <em>you</em> 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>

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<p><strong>Answer:</strong> (Three possible points for improvement)</p>

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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="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span> <span class="c1"># to display images</span>

<span class="n">test_image_dir</span> <span class="o">=</span> <span class="s1">&#39;test_images/&#39;</span>
<span class="n">test_image_paths</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">test_image_dir</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing run_app on </span><span class="si">{}</span><span class="s1"> images: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test_image_paths</span><span class="p">)))</span>

<span class="c1">## suggested code, below</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">test_image_paths</span><span class="p">:</span>
    <span class="n">file_path</span> <span class="o">=</span> <span class="n">test_image_dir</span> <span class="o">+</span> <span class="n">file</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">Image</span><span class="o">.</span><span class="n">ANTIALIAS</span><span class="p">))</span>
    <span class="n">run_app</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
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<pre>Testing run_app on 8 images: 
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
"
>
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<pre>This person looks like a German pinscher


</pre>
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<img 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
"
>
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<pre>This dog looks like a German shepherd dog


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<img 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>
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<pre>This person looks like a Xoloitzcuintli


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<img 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<pre>This person looks like a Parson russell terrier


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
"
>
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<pre>This dog looks like a French bulldog


</pre>
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<img 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
"
>
</div>

</div>

<div class="output_area">

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<div class="output_subarea output_stream output_stdout output_text">
<pre>This image does not contain humans or dogs: test_images/pumpkin1.jpg
</pre>
</div>
</div>

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<img 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
"
>
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<pre>This person looks like a Dachshund


</pre>
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<img 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
"
>
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<div class="output_subarea output_stream output_stdout output_text">
<pre>This image does not contain humans or dogs: test_images/watermelon1.jpg
</pre>
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<h3 id="Question-6-Performance-answer:">Question 6 Performance answer:<a class="anchor-link" href="#Question-6-Performance-answer:">&#182;</a></h3><p>The algorithim performed better that I expected. I actually assumed that it would be thrown off by the halloween pumpkin head. Then again... if it's differentiating between dog breeds, pumkin vs human should be an easy battle! It's really fascinating to me, and apparent from this project, the power of transfer learning. Almost immediately the VGG19 model achieving 60%+ accuracy.</p>
<h5 id="3-Points-of-improvement:">3 Points of improvement:<a class="anchor-link" href="#3-Points-of-improvement:">&#182;</a></h5><p>1) Add additional fully connected layers at the end of the VGG19 model to allow for more detailed differentiation between the given dog breed classes.</p>
<p>2) In this project I used a standard transform when loading my images. Using data augmentation I could have potential better trained the model by giving it more image variants from the training set (further cropped, rotate, flipped, etc...)</p>
<p>3) Further training once the new layers are added. Looking at the current training log, the validation loss hovered at ~0.75 from epoch 15 onward. I believe the the added layers wil significantly help keeping a stronger correlation between the training and validation losses over a longer training period.</p>

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