report.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="Artificial-Intelligence-Nanodegree">Artificial Intelligence Nanodegree<a class="anchor-link" href="#Artificial-Intelligence-Nanodegree">&#182;</a></h1><h2 id="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">&#182;</a></h2><h2 id="Project:-Write-an-Algorithm-for-a-Dog-Identification-App">Project: Write an Algorithm for a Dog Identification App<a class="anchor-link" href="#Project:-Write-an-Algorithm-for-a-Dog-Identification-App">&#182;</a></h2><hr>
<p>In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'(IMPLEMENTATION)'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
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<p>The rubric contains <em>optional</em> "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.</p>
<hr>
<h3 id="Why-We're-Here">Why We're Here<a class="anchor-link" href="#Why-We're-Here">&#182;</a></h3><p>In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).</p>
<p><img src="images/sample_dog_output.png" alt="Sample Dog Output"></p>
<p>In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!</p>
<h3 id="The-Road-Ahead">The Road Ahead<a class="anchor-link" href="#The-Road-Ahead">&#182;</a></h3><p>We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.</p>
<ul>
<li><a href="#step0">Step 0</a>: Import Datasets</li>
<li><a href="#step1">Step 1</a>: Detect Humans</li>
<li><a href="#step2">Step 2</a>: Detect Dogs</li>
<li><a href="#step3">Step 3</a>: Create a CNN to Classify Dog Breeds (from Scratch)</li>
<li><a href="#step4">Step 4</a>: Use a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step5">Step 5</a>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step6">Step 6</a>: Write your Algorithm</li>
<li><a href="#step7">Step 7</a>: Test Your Algorithm</li>
</ul>
<hr>
<p><a id='step0'></a></p>
<h2 id="Step-0:-Import-Datasets">Step 0: Import Datasets<a class="anchor-link" href="#Step-0:-Import-Datasets">&#182;</a></h2><h3 id="Import-Dog-Dataset">Import Dog Dataset<a class="anchor-link" href="#Import-Dog-Dataset">&#182;</a></h3><p>In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the <code>load_files</code> function from the scikit-learn library:</p>
<ul>
<li><code>train_files</code>, <code>valid_files</code>, <code>test_files</code> - numpy arrays containing file paths to images</li>
<li><code>train_targets</code>, <code>valid_targets</code>, <code>test_targets</code> - numpy arrays containing onehot-encoded classification labels </li>
<li><code>dog_names</code> - list of string-valued dog breed names for translating labels</li>
</ul>

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

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

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

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

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

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

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

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

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

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

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

<span class="c1"># load color (BGR) image</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">human_files</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="c1"># convert BGR image to grayscale</span>
<span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>

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

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

<span class="c1"># get bounding box for each detected face</span>
<span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">w</span><span class="p">,</span><span class="n">h</span><span class="p">)</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span>
    <span class="c1"># add bounding box to color image</span>
    <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">img</span><span class="p">,(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">),(</span><span class="n">x</span><span class="o">+</span><span class="n">w</span><span class="p">,</span><span class="n">y</span><span class="o">+</span><span class="n">h</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
    
<span class="c1"># convert BGR image to RGB for plotting</span>
<span class="n">cv_rgb</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span>

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

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># returns &quot;True&quot; if face is detected in image stored at img_path</span>
<span class="k">def</span> <span class="nf">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
    <span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
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<h3 id="(IMPLEMENTATION)-Assess-the-Human-Face-Detector">(IMPLEMENTATION) Assess the Human Face Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Human-Face-Detector">&#182;</a></h3><p><strong>Question 1:</strong> Use the code cell below to test the performance of the <code>face_detector</code> function.</p>
<ul>
<li>What percentage of the first 100 images in <code>human_files</code> have a detected human face?  </li>
<li>What percentage of the first 100 images in <code>dog_files</code> have a detected human face? </li>
</ul>
<p>Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays <code>human_files_short</code> and <code>dog_files_short</code>.</p>
<p><strong>Answer:</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">train_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="c1"># Do NOT modify the code above this line.</span>

<span class="c1">## TODO: Test the performance of the face_detector algorithm </span>
<span class="c1">## on the images in human_files_short and dog_files_short.</span>
<span class="k">def</span> <span class="nf">test_face_detector_performance</span><span class="p">():</span>    
    <span class="n">detected_human_faces</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">human_files_short</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
            <span class="n">detected_human_faces</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detected human faces: &quot;</span><span class="p">,</span><span class="n">detected_human_faces</span><span class="p">,</span><span class="s2">&quot;/ 100&quot;</span><span class="p">)</span>

    <span class="n">detected_human_faces</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">dog_files_short</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
            <span class="n">detected_human_faces</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detected human faces out of dog faces: &quot;</span><span class="p">,</span><span class="n">detected_human_faces</span><span class="p">,</span> <span class="s2">&quot;/ 100&quot;</span><span class="p">)</span>
    
<span class="n">test_face_detector_performance</span><span class="p">()</span>
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<pre>Detected human faces:  99 / 100
Detected human faces out of dog faces:  12 / 100
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<p><strong>Question 2:</strong> This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?</p>
<p><strong>Answer:</strong> <strong>In special cases it is reasonable - for example logging in with face scanning on a device. In general if it is not possible to force the user to pose on camera, a CNN, which is trained with lots of images of humans, could do a good job of detecting people in various poses.</strong></p>
<p>We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this <em>optional</em> task, report performance on each of the datasets.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># copied these functions from Step 2 here because I need them for the optional assignment</span>

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

<span class="k">def</span> <span class="nf">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># loads RGB image as PIL.Image.Image type</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
    <span class="c1"># convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="c1"># convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">paths_to_tensor</span><span class="p">(</span><span class="n">img_paths</span><span class="p">):</span>
    <span class="n">list_of_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="k">for</span> <span class="n">img_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">img_paths</span><span class="p">)]</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">list_of_tensors</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## (Optional) TODO: Report the performance of another  </span>
<span class="c1">## face detection algorithm on the LFW dataset</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>

<span class="kn">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="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;dogImages/*/*/*&quot;</span><span class="p">))</span>

<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">human_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span><span class="p">,</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">dog_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span><span class="p">]),</span>
                                                    <span class="n">np_utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">human_files</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dog_files</span><span class="p">))]),</span><span class="mi">2</span><span class="p">),</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>100%|████████████████████████████████████████████████████████████████████████████| 13233/13233 [02:42&lt;00:00, 81.63it/s]
100%|██████████████████████████████████████████████████████████████████████████████| 8351/8351 [02:25&lt;00:00, 57.45it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> 
                        <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

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

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 8)       224       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 112, 112, 8)       0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 16)      1168      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 56, 56, 16)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 32)        4640      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 28, 28, 32)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 64)        18496     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 14, 14, 128)       73856     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 7, 7, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 6272)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 500)               3136500   
_________________________________________________________________
dropout_2 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 250)               125250    
_________________________________________________________________
dropout_3 (Dropout)          (None, 250)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 2)                 502       
=================================================================
Total params: 3,360,636
Trainable params: 3,360,636
Non-trainable params: 0
_________________________________________________________________
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>  

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.dog_vs_human.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Train on 13813 samples, validate on 3454 samples
Epoch 1/20
Epoch 00000: val_loss improved from inf to 0.52029, saving model to saved_models/weights.best.dog_vs_human.hdf5
65s - loss: 0.6945 - acc: 0.6191 - val_loss: 0.5203 - val_acc: 0.6740
Epoch 2/20
Epoch 00001: val_loss improved from 0.52029 to 0.15092, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.5063 - acc: 0.8105 - val_loss: 0.1509 - val_acc: 0.9473
Epoch 3/20
Epoch 00002: val_loss improved from 0.15092 to 0.08657, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.2010 - acc: 0.9188 - val_loss: 0.0866 - val_acc: 0.9696
Epoch 4/20
Epoch 00003: val_loss did not improve
19s - loss: 0.1427 - acc: 0.9495 - val_loss: 0.1576 - val_acc: 0.9461
Epoch 5/20
Epoch 00004: val_loss improved from 0.08657 to 0.06396, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.1056 - acc: 0.9658 - val_loss: 0.0640 - val_acc: 0.9820
Epoch 6/20
Epoch 00005: val_loss improved from 0.06396 to 0.04597, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.1289 - acc: 0.9615 - val_loss: 0.0460 - val_acc: 0.9838
Epoch 7/20
Epoch 00006: val_loss improved from 0.04597 to 0.03764, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.0661 - acc: 0.9773 - val_loss: 0.0376 - val_acc: 0.9867
Epoch 8/20
Epoch 00007: val_loss did not improve
19s - loss: 0.0445 - acc: 0.9848 - val_loss: 0.0570 - val_acc: 0.9818
Epoch 9/20
Epoch 00008: val_loss improved from 0.03764 to 0.03622, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.0775 - acc: 0.9754 - val_loss: 0.0362 - val_acc: 0.9881
Epoch 10/20
Epoch 00009: val_loss did not improve
19s - loss: 0.0340 - acc: 0.9875 - val_loss: 0.2656 - val_acc: 0.9195
Epoch 11/20
Epoch 00010: val_loss improved from 0.03622 to 0.02401, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.0526 - acc: 0.9824 - val_loss: 0.0240 - val_acc: 0.9907
Epoch 12/20
Epoch 00011: val_loss improved from 0.02401 to 0.01371, saving model to saved_models/weights.best.dog_vs_human.hdf5
19s - loss: 0.0702 - acc: 0.9702 - val_loss: 0.0137 - val_acc: 0.9945
Epoch 13/20
Epoch 00012: val_loss did not improve
18s - loss: 0.0207 - acc: 0.9930 - val_loss: 0.0170 - val_acc: 0.9936
Epoch 14/20
Epoch 00013: val_loss improved from 0.01371 to 0.01344, saving model to saved_models/weights.best.dog_vs_human.hdf5
18s - loss: 0.0543 - acc: 0.9805 - val_loss: 0.0134 - val_acc: 0.9959
Epoch 15/20
Epoch 00014: val_loss improved from 0.01344 to 0.01275, saving model to saved_models/weights.best.dog_vs_human.hdf5
18s - loss: 0.0199 - acc: 0.9934 - val_loss: 0.0128 - val_acc: 0.9962
Epoch 16/20
Epoch 00015: val_loss improved from 0.01275 to 0.01259, saving model to saved_models/weights.best.dog_vs_human.hdf5
18s - loss: 0.0310 - acc: 0.9894 - val_loss: 0.0126 - val_acc: 0.9965
Epoch 17/20
Epoch 00016: val_loss improved from 0.01259 to 0.01140, saving model to saved_models/weights.best.dog_vs_human.hdf5
18s - loss: 0.0067 - acc: 0.9978 - val_loss: 0.0114 - val_acc: 0.9959
Epoch 18/20
Epoch 00017: val_loss did not improve
18s - loss: 0.2287 - acc: 0.9704 - val_loss: 0.0166 - val_acc: 0.9942
Epoch 19/20
Epoch 00018: val_loss improved from 0.01140 to 0.01119, saving model to saved_models/weights.best.dog_vs_human.hdf5
18s - loss: 0.0068 - acc: 0.9971 - val_loss: 0.0112 - val_acc: 0.9980
Epoch 20/20
Epoch 00019: val_loss did not improve
18s - loss: 0.0278 - acc: 0.9916 - val_loss: 0.0123 - val_acc: 0.9974
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">X_test</span><span class="p">]</span>

<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">model_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">model_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 99.6062%
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>

<span class="k">def</span> <span class="nf">dog_vs_human_cnn_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> 
    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">)))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="k">return</span> <span class="s2">&quot;human&quot;</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="s2">&quot;dog&quot;</span>     
   
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">dog_files</span><span class="p">,</span> <span class="n">human_files</span><span class="p">]),</span> <span class="mi">10</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">dog_vs_human_cnn_detector</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; detected for file: &quot;</span> <span class="o">+</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file</span><span class="p">))</span>
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<pre>human detected for file: Richard_Gephardt_0001.jpg
dog detected for file: French_bulldog_04824.jpg
dog detected for file: Komondor_06380.jpg
human detected for file: Julie_Gerberding_0011.jpg
dog detected for file: Kerry_blue_terrier_06285.jpg
dog detected for file: Elvis_Costello_0001.jpg
dog detected for file: Norfolk_terrier_07050.jpg
dog detected for file: Boykin_spaniel_02468.jpg
human detected for file: Nick_Nolte_0004.jpg
human detected for file: Fernando_Henrique_Cardoso_0008.jpg
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<p><a id='step2'></a></p>
<h2 id="Step-2:-Detect-Dogs">Step 2: Detect Dogs<a class="anchor-link" href="#Step-2:-Detect-Dogs">&#182;</a></h2><p>In this section, we use a pre-trained <a href="http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006">ResNet-50</a> model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on <a href="http://www.image-net.org/">ImageNet</a>, a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">1000 categories</a>.  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.</p>

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

<span class="c1"># define ResNet50 model</span>
<span class="n">ResNet50_model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">)</span>
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<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape</p>
$$
(\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}),
$$<p>where <code>nb_samples</code> corresponds to the total number of images (or samples), and <code>rows</code>, <code>columns</code>, and <code>channels</code> correspond to the number of rows, columns, and channels for each image, respectively.</p>
<p>The <code>path_to_tensor</code> function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape</p>
$$
(1, 224, 224, 3).
$$<p>The <code>paths_to_tensor</code> function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape</p>
$$
(\text{nb_samples}, 224, 224, 3).
$$<p>Here, <code>nb_samples</code> is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of <code>nb_samples</code> as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!</p>

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

<span class="k">def</span> <span class="nf">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># loads RGB image as PIL.Image.Image type</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
    <span class="c1"># convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="c1"># convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

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

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

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

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

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">detected_human_faces</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">human_files_short</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
        <span class="n">detected_human_faces</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detected dogs out of human faces: &quot;</span><span class="p">,</span><span class="n">detected_human_faces</span><span class="p">,</span><span class="s2">&quot;/ 100&quot;</span><span class="p">)</span>

<span class="n">detected_human_faces</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">dog_files_short</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
        <span class="n">detected_human_faces</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detected dog faces out of dog faces: &quot;</span><span class="p">,</span><span class="n">detected_human_faces</span><span class="p">,</span> <span class="s2">&quot;/ 100&quot;</span><span class="p">)</span>
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<pre>Detected dogs out of human faces:  1 / 100
Detected dog faces out of dog faces:  100 / 100
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<p><a id='step3'></a></p>
<h2 id="Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">Step 3: Create a CNN to Classify Dog Breeds (from Scratch)<a class="anchor-link" href="#Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">&#182;</a></h2><p>Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN <em>from scratch</em> (so, you can't use transfer learning <em>yet</em>!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</p>
<p>Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.</p>
<p>We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that <em>even a human</em> would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.</p>
<table>
<thead><tr>
<th>Brittany</th>
<th>Welsh Springer Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Brittany_02625.jpg" width="100"></td>
<td><img src="images/Welsh_springer_spaniel_08203.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).</p>
<table>
<thead><tr>
<th>Curly-Coated Retriever</th>
<th>American Water Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Curly-coated_retriever_03896.jpg" width="200"></td>
<td><img src="images/American_water_spaniel_00648.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.</p>
<table>
<thead><tr>
<th>Yellow Labrador</th>
<th>Chocolate Labrador</th>
<th>Black Labrador</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Labrador_retriever_06457.jpg" width="150"></td>
<td><img src="images/Labrador_retriever_06455.jpg" width="240"></td>
<td><img src="images/Labrador_retriever_06449.jpg" width="220"></td>
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<p>We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.</p>
<p>Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun!</p>
<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>We rescale the images by dividing every pixel in every image by 255.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># pre-process the data for Keras</span>
<span class="n">train_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">train_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
<span class="n">valid_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">valid_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
<span class="n">test_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">test_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
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<pre>100%|██████████████████████████████████████████████████████████████████████████████| 6680/6680 [02:26&lt;00:00, 45.48it/s]
100%|████████████████████████████████████████████████████████████████████████████████| 835/835 [00:13&lt;00:00, 76.58it/s]
100%|████████████████████████████████████████████████████████████████████████████████| 836/836 [00:15&lt;00:00, 55.10it/s]
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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

<pre><code>    model.summary()

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<p>We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains &gt;1% test accuracy in 5 epochs:</p>
<p><img src="images/sample_cnn.png" alt="Sample CNN"></p>
<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.</p>
<p><strong>Answer:</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>


<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> 
                        <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>


<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 224, 224, 8)       224       
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 112, 112, 8)       0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 112, 112, 16)      1168      
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 56, 56, 16)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 56, 56, 32)        4640      
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 28, 28, 32)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 28, 28, 64)        18496     
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 14, 14, 128)       73856     
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 7, 7, 128)         0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 7, 7, 128)         0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 6272)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 500)               3136500   
_________________________________________________________________
dropout_5 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 250)               125250    
_________________________________________________________________
dropout_6 (Dropout)          (None, 250)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 133)               33383     
=================================================================
Total params: 3,393,517
Trainable params: 3,393,517
Non-trainable params: 0
_________________________________________________________________
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<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

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

<span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span> 

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">epochs</span> <span class="o">=</span> <span class="mi">300</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">133</span>

<span class="n">datagen_train</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
    <span class="n">width_shift_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>  
    <span class="n">height_shift_range</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>  
    <span class="n">horizontal_flip</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> 

<span class="n">datagen_train</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">datagen_train</span><span class="o">.</span><span class="n">flow</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">),</span>
                    <span class="n">steps_per_epoch</span><span class="o">=</span><span class="n">train_tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="n">batch_size</span><span class="p">,</span>
                    <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span>
                    <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
                    <span class="n">validation_steps</span><span class="o">=</span><span class="n">valid_tensors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
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<pre>Epoch 1/300
Epoch 00000: val_loss improved from inf to 4.87746, saving model to saved_models/weights.best.from_scratch.hdf5
15s - loss: 4.8889 - acc: 0.0083 - val_loss: 4.8775 - val_acc: 0.0108
Epoch 2/300
Epoch 00001: val_loss improved from 4.87746 to 4.83848, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.8669 - acc: 0.0121 - val_loss: 4.8385 - val_acc: 0.0168
Epoch 3/300
Epoch 00002: val_loss improved from 4.83848 to 4.70525, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.8015 - acc: 0.0146 - val_loss: 4.7053 - val_acc: 0.0263
Epoch 4/300
Epoch 00003: val_loss improved from 4.70525 to 4.59775, saving model to saved_models/weights.best.from_scratch.hdf5
13s - loss: 4.7093 - acc: 0.0206 - val_loss: 4.5977 - val_acc: 0.0287
Epoch 5/300
Epoch 00004: val_loss improved from 4.59775 to 4.59175, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.6313 - acc: 0.0301 - val_loss: 4.5917 - val_acc: 0.0347
Epoch 6/300
Epoch 00005: val_loss improved from 4.59175 to 4.35887, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.5185 - acc: 0.0367 - val_loss: 4.3589 - val_acc: 0.0527
Epoch 7/300
Epoch 00006: val_loss improved from 4.35887 to 4.31871, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.4339 - acc: 0.0392 - val_loss: 4.3187 - val_acc: 0.0527
Epoch 8/300
Epoch 00007: val_loss improved from 4.31871 to 4.17725, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.3463 - acc: 0.0483 - val_loss: 4.1772 - val_acc: 0.0659
Epoch 9/300
Epoch 00008: val_loss improved from 4.17725 to 4.09643, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.2818 - acc: 0.0455 - val_loss: 4.0964 - val_acc: 0.0754
Epoch 10/300
Epoch 00009: val_loss improved from 4.09643 to 4.03980, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.2224 - acc: 0.0572 - val_loss: 4.0398 - val_acc: 0.0707
Epoch 11/300
Epoch 00010: val_loss improved from 4.03980 to 4.02604, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 4.1524 - acc: 0.0587 - val_loss: 4.0260 - val_acc: 0.0671
Epoch 12/300
Epoch 00011: val_loss did not improve
11s - loss: 4.1299 - acc: 0.0620 - val_loss: 4.1446 - val_acc: 0.0731
Epoch 13/300
Epoch 00012: val_loss did not improve
12s - loss: 4.0865 - acc: 0.0711 - val_loss: 4.0407 - val_acc: 0.0778
Epoch 14/300
Epoch 00013: val_loss improved from 4.02604 to 3.90518, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 4.0394 - acc: 0.0767 - val_loss: 3.9052 - val_acc: 0.0994
Epoch 15/300
Epoch 00014: val_loss improved from 3.90518 to 3.84857, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.9779 - acc: 0.0827 - val_loss: 3.8486 - val_acc: 0.1126
Epoch 16/300
Epoch 00015: val_loss improved from 3.84857 to 3.80213, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.9502 - acc: 0.0849 - val_loss: 3.8021 - val_acc: 0.1006
Epoch 17/300
Epoch 00016: val_loss improved from 3.80213 to 3.79205, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 3.8613 - acc: 0.1018 - val_loss: 3.7921 - val_acc: 0.0898
Epoch 18/300
Epoch 00017: val_loss improved from 3.79205 to 3.75463, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.8361 - acc: 0.0999 - val_loss: 3.7546 - val_acc: 0.0994
Epoch 19/300
Epoch 00018: val_loss did not improve
11s - loss: 3.7896 - acc: 0.1073 - val_loss: 3.7764 - val_acc: 0.1102
Epoch 20/300
Epoch 00019: val_loss improved from 3.75463 to 3.73335, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.7504 - acc: 0.1163 - val_loss: 3.7333 - val_acc: 0.1138
Epoch 21/300
Epoch 00020: val_loss did not improve
11s - loss: 3.7132 - acc: 0.1179 - val_loss: 3.7790 - val_acc: 0.1018
Epoch 22/300
Epoch 00021: val_loss did not improve
11s - loss: 3.7044 - acc: 0.1213 - val_loss: 3.8302 - val_acc: 0.1030
Epoch 23/300
Epoch 00022: val_loss improved from 3.73335 to 3.70565, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.6526 - acc: 0.1271 - val_loss: 3.7057 - val_acc: 0.1269
Epoch 24/300
Epoch 00023: val_loss improved from 3.70565 to 3.68426, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.5936 - acc: 0.1317 - val_loss: 3.6843 - val_acc: 0.1234
Epoch 25/300
Epoch 00024: val_loss did not improve
11s - loss: 3.5654 - acc: 0.1378 - val_loss: 3.6900 - val_acc: 0.1138
Epoch 26/300
Epoch 00025: val_loss improved from 3.68426 to 3.64056, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.5747 - acc: 0.1368 - val_loss: 3.6406 - val_acc: 0.1138
Epoch 27/300
Epoch 00026: val_loss did not improve
11s - loss: 3.4920 - acc: 0.1507 - val_loss: 3.8128 - val_acc: 0.1066
Epoch 28/300
Epoch 00027: val_loss improved from 3.64056 to 3.60636, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 3.4808 - acc: 0.1473 - val_loss: 3.6064 - val_acc: 0.1377
Epoch 29/300
Epoch 00028: val_loss improved from 3.60636 to 3.55491, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.4449 - acc: 0.1552 - val_loss: 3.5549 - val_acc: 0.1437
Epoch 30/300
Epoch 00029: val_loss improved from 3.55491 to 3.52320, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 3.4235 - acc: 0.1565 - val_loss: 3.5232 - val_acc: 0.1401
Epoch 31/300
Epoch 00030: val_loss did not improve
11s - loss: 3.3834 - acc: 0.1705 - val_loss: 3.5316 - val_acc: 0.1425
Epoch 32/300
Epoch 00031: val_loss improved from 3.52320 to 3.47871, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 3.3650 - acc: 0.1706 - val_loss: 3.4787 - val_acc: 0.1677
Epoch 33/300
Epoch 00032: val_loss improved from 3.47871 to 3.42157, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.3143 - acc: 0.1762 - val_loss: 3.4216 - val_acc: 0.1725
Epoch 34/300
Epoch 00033: val_loss did not improve
11s - loss: 3.3141 - acc: 0.1791 - val_loss: 3.5056 - val_acc: 0.1533
Epoch 35/300
Epoch 00034: val_loss did not improve
11s - loss: 3.2773 - acc: 0.1879 - val_loss: 3.7731 - val_acc: 0.1257
Epoch 36/300
Epoch 00035: val_loss improved from 3.42157 to 3.36759, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.2362 - acc: 0.1941 - val_loss: 3.3676 - val_acc: 0.1737
Epoch 37/300
Epoch 00036: val_loss did not improve
11s - loss: 3.1664 - acc: 0.2045 - val_loss: 3.3724 - val_acc: 0.1665
Epoch 38/300
Epoch 00037: val_loss did not improve
11s - loss: 3.1648 - acc: 0.2073 - val_loss: 3.3747 - val_acc: 0.1689
Epoch 39/300
Epoch 00038: val_loss improved from 3.36759 to 3.33095, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.1321 - acc: 0.2129 - val_loss: 3.3309 - val_acc: 0.1868
Epoch 40/300
Epoch 00039: val_loss did not improve
11s - loss: 3.1305 - acc: 0.2126 - val_loss: 3.4341 - val_acc: 0.1796
Epoch 41/300
Epoch 00040: val_loss did not improve
11s - loss: 3.0895 - acc: 0.2230 - val_loss: 3.3449 - val_acc: 0.1749
Epoch 42/300
Epoch 00041: val_loss improved from 3.33095 to 3.28650, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.0747 - acc: 0.2158 - val_loss: 3.2865 - val_acc: 0.2024
Epoch 43/300
Epoch 00042: val_loss improved from 3.28650 to 3.25121, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 3.0171 - acc: 0.2280 - val_loss: 3.2512 - val_acc: 0.1976
Epoch 44/300
Epoch 00043: val_loss did not improve
11s - loss: 3.0020 - acc: 0.2331 - val_loss: 3.2639 - val_acc: 0.1772
Epoch 45/300
Epoch 00044: val_loss improved from 3.25121 to 3.23972, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.9596 - acc: 0.2399 - val_loss: 3.2397 - val_acc: 0.2120
Epoch 46/300
Epoch 00045: val_loss did not improve
11s - loss: 2.9586 - acc: 0.2324 - val_loss: 3.2969 - val_acc: 0.1940
Epoch 47/300
Epoch 00046: val_loss did not improve
11s - loss: 2.9205 - acc: 0.2491 - val_loss: 3.3441 - val_acc: 0.2060
Epoch 48/300
Epoch 00047: val_loss improved from 3.23972 to 3.15641, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.8984 - acc: 0.2509 - val_loss: 3.1564 - val_acc: 0.2192
Epoch 49/300
Epoch 00048: val_loss improved from 3.15641 to 3.13637, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.8948 - acc: 0.2579 - val_loss: 3.1364 - val_acc: 0.1988
Epoch 50/300
Epoch 00049: val_loss improved from 3.13637 to 3.11002, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.8594 - acc: 0.2567 - val_loss: 3.1100 - val_acc: 0.2204
Epoch 51/300
Epoch 00050: val_loss improved from 3.11002 to 3.07705, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.8590 - acc: 0.2555 - val_loss: 3.0771 - val_acc: 0.2204
Epoch 52/300
Epoch 00051: val_loss did not improve
11s - loss: 2.8069 - acc: 0.2797 - val_loss: 3.1971 - val_acc: 0.2323
Epoch 53/300
Epoch 00052: val_loss did not improve
12s - loss: 2.7812 - acc: 0.2730 - val_loss: 3.1511 - val_acc: 0.2299
Epoch 54/300
Epoch 00053: val_loss did not improve
11s - loss: 2.7442 - acc: 0.2848 - val_loss: 3.1505 - val_acc: 0.2144
Epoch 55/300
Epoch 00054: val_loss did not improve
11s - loss: 2.7355 - acc: 0.2912 - val_loss: 3.1947 - val_acc: 0.2192
Epoch 56/300
Epoch 00055: val_loss did not improve
12s - loss: 2.6858 - acc: 0.2919 - val_loss: 3.5388 - val_acc: 0.1808
Epoch 57/300
Epoch 00056: val_loss did not improve
10s - loss: 2.7145 - acc: 0.2950 - val_loss: 3.1989 - val_acc: 0.2204
Epoch 58/300
Epoch 00057: val_loss did not improve
11s - loss: 2.6391 - acc: 0.3088 - val_loss: 3.1205 - val_acc: 0.2204
Epoch 59/300
Epoch 00058: val_loss improved from 3.07705 to 3.05982, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 2.6405 - acc: 0.3018 - val_loss: 3.0598 - val_acc: 0.2419
Epoch 60/300
Epoch 00059: val_loss improved from 3.05982 to 3.04865, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.5776 - acc: 0.3163 - val_loss: 3.0487 - val_acc: 0.2491
Epoch 61/300
Epoch 00060: val_loss improved from 3.04865 to 3.01281, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 2.6076 - acc: 0.3041 - val_loss: 3.0128 - val_acc: 0.2431
Epoch 62/300
Epoch 00061: val_loss did not improve
11s - loss: 2.5776 - acc: 0.3109 - val_loss: 3.0702 - val_acc: 0.2335
Epoch 63/300
Epoch 00062: val_loss did not improve
11s - loss: 2.5571 - acc: 0.3222 - val_loss: 3.1113 - val_acc: 0.2383
Epoch 64/300
Epoch 00063: val_loss did not improve
11s - loss: 2.5339 - acc: 0.3207 - val_loss: 3.0219 - val_acc: 0.2611
Epoch 65/300
Epoch 00064: val_loss improved from 3.01281 to 2.96998, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 2.5469 - acc: 0.3273 - val_loss: 2.9700 - val_acc: 0.2671
Epoch 66/300
Epoch 00065: val_loss did not improve
11s - loss: 2.4781 - acc: 0.3339 - val_loss: 3.1126 - val_acc: 0.2479
Epoch 67/300
Epoch 00066: val_loss did not improve
11s - loss: 2.4812 - acc: 0.3370 - val_loss: 3.0817 - val_acc: 0.2527
Epoch 68/300
Epoch 00067: val_loss did not improve
11s - loss: 2.4293 - acc: 0.3534 - val_loss: 3.0590 - val_acc: 0.2731
Epoch 69/300
Epoch 00068: val_loss did not improve
12s - loss: 2.4664 - acc: 0.3479 - val_loss: 3.0228 - val_acc: 0.2503
Epoch 70/300
Epoch 00069: val_loss improved from 2.96998 to 2.93885, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 2.4255 - acc: 0.3464 - val_loss: 2.9389 - val_acc: 0.2719
Epoch 71/300
Epoch 00070: val_loss did not improve
11s - loss: 2.4093 - acc: 0.3470 - val_loss: 2.9904 - val_acc: 0.2659
Epoch 72/300
Epoch 00071: val_loss did not improve
11s - loss: 2.3551 - acc: 0.3613 - val_loss: 3.0234 - val_acc: 0.2683
Epoch 73/300
Epoch 00072: val_loss did not improve
12s - loss: 2.3498 - acc: 0.3658 - val_loss: 2.9599 - val_acc: 0.2683
Epoch 74/300
Epoch 00073: val_loss did not improve
11s - loss: 2.3521 - acc: 0.3717 - val_loss: 3.1662 - val_acc: 0.2443
Epoch 75/300
Epoch 00074: val_loss did not improve
11s - loss: 2.3414 - acc: 0.3647 - val_loss: 2.9761 - val_acc: 0.2599
Epoch 76/300
Epoch 00075: val_loss did not improve
11s - loss: 2.2436 - acc: 0.3808 - val_loss: 3.0421 - val_acc: 0.2575
Epoch 77/300
Epoch 00076: val_loss did not improve
11s - loss: 2.3224 - acc: 0.3730 - val_loss: 2.9432 - val_acc: 0.2766
Epoch 78/300
Epoch 00077: val_loss did not improve
11s - loss: 2.2521 - acc: 0.3790 - val_loss: 3.0224 - val_acc: 0.2695
Epoch 79/300
Epoch 00078: val_loss did not improve
11s - loss: 2.2211 - acc: 0.3842 - val_loss: 3.1181 - val_acc: 0.2575
Epoch 80/300
Epoch 00079: val_loss did not improve
11s - loss: 2.2600 - acc: 0.3794 - val_loss: 3.0292 - val_acc: 0.2599
Epoch 81/300
Epoch 00080: val_loss did not improve
11s - loss: 2.1732 - acc: 0.4027 - val_loss: 3.0492 - val_acc: 0.2838
Epoch 82/300
Epoch 00081: val_loss did not improve
12s - loss: 2.2249 - acc: 0.3787 - val_loss: 2.9392 - val_acc: 0.2659
Epoch 83/300
Epoch 00082: val_loss did not improve
12s - loss: 2.1914 - acc: 0.4040 - val_loss: 2.9947 - val_acc: 0.2587
Epoch 84/300
Epoch 00083: val_loss did not improve
12s - loss: 2.1660 - acc: 0.4106 - val_loss: 2.9404 - val_acc: 0.2707
Epoch 85/300
Epoch 00084: val_loss did not improve
11s - loss: 2.1673 - acc: 0.4096 - val_loss: 2.9592 - val_acc: 0.2874
Epoch 86/300
Epoch 00085: val_loss did not improve
12s - loss: 2.1383 - acc: 0.4079 - val_loss: 3.0558 - val_acc: 0.2707
Epoch 87/300
Epoch 00086: val_loss did not improve
11s - loss: 2.1344 - acc: 0.4170 - val_loss: 3.0128 - val_acc: 0.2754
Epoch 88/300
Epoch 00087: val_loss did not improve
11s - loss: 2.1201 - acc: 0.4175 - val_loss: 2.9905 - val_acc: 0.2790
Epoch 89/300
Epoch 00088: val_loss did not improve
12s - loss: 2.1032 - acc: 0.4166 - val_loss: 2.9828 - val_acc: 0.2647
Epoch 90/300
Epoch 00089: val_loss did not improve
11s - loss: 2.1018 - acc: 0.4191 - val_loss: 3.1734 - val_acc: 0.2647
Epoch 91/300
Epoch 00090: val_loss did not improve
11s - loss: 2.0899 - acc: 0.4267 - val_loss: 2.9699 - val_acc: 0.2958
Epoch 92/300
Epoch 00091: val_loss improved from 2.93885 to 2.88557, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 2.0850 - acc: 0.4253 - val_loss: 2.8856 - val_acc: 0.2970
Epoch 93/300
Epoch 00092: val_loss did not improve
11s - loss: 2.0780 - acc: 0.4245 - val_loss: 3.0390 - val_acc: 0.2922
Epoch 94/300
Epoch 00093: val_loss did not improve
11s - loss: 2.0319 - acc: 0.4380 - val_loss: 2.9079 - val_acc: 0.2958
Epoch 95/300
Epoch 00094: val_loss did not improve
11s - loss: 1.9961 - acc: 0.4437 - val_loss: 3.0161 - val_acc: 0.3006
Epoch 96/300
Epoch 00095: val_loss did not improve
11s - loss: 2.0448 - acc: 0.4324 - val_loss: 2.9079 - val_acc: 0.3030
Epoch 97/300
Epoch 00096: val_loss did not improve
11s - loss: 1.9692 - acc: 0.4383 - val_loss: 2.9987 - val_acc: 0.3042
Epoch 98/300
Epoch 00097: val_loss did not improve
11s - loss: 2.0321 - acc: 0.4381 - val_loss: 2.9631 - val_acc: 0.2790
Epoch 99/300
Epoch 00098: val_loss improved from 2.88557 to 2.86309, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 1.9800 - acc: 0.4476 - val_loss: 2.8631 - val_acc: 0.3174
Epoch 100/300
Epoch 00099: val_loss did not improve
11s - loss: 1.9702 - acc: 0.4503 - val_loss: 3.0314 - val_acc: 0.2934
Epoch 101/300
Epoch 00100: val_loss improved from 2.86309 to 2.82439, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 1.9687 - acc: 0.4540 - val_loss: 2.8244 - val_acc: 0.2994
Epoch 102/300
Epoch 00101: val_loss did not improve
12s - loss: 1.9312 - acc: 0.4645 - val_loss: 2.9398 - val_acc: 0.2994
Epoch 103/300
Epoch 00102: val_loss did not improve
11s - loss: 1.9533 - acc: 0.4524 - val_loss: 2.8724 - val_acc: 0.3054
Epoch 104/300
Epoch 00103: val_loss did not improve
11s - loss: 1.9517 - acc: 0.4564 - val_loss: 2.9423 - val_acc: 0.2982
Epoch 105/300
Epoch 00104: val_loss did not improve
11s - loss: 1.9094 - acc: 0.4680 - val_loss: 2.9088 - val_acc: 0.3030
Epoch 106/300
Epoch 00105: val_loss did not improve
13s - loss: 1.8986 - acc: 0.4738 - val_loss: 2.9848 - val_acc: 0.3090
Epoch 107/300
Epoch 00106: val_loss did not improve
10s - loss: 1.8789 - acc: 0.4703 - val_loss: 3.1448 - val_acc: 0.2659
Epoch 108/300
Epoch 00107: val_loss did not improve
13s - loss: 1.8382 - acc: 0.4786 - val_loss: 3.4564 - val_acc: 0.2287
Epoch 109/300
Epoch 00108: val_loss did not improve
10s - loss: 1.8686 - acc: 0.4795 - val_loss: 2.9177 - val_acc: 0.3090
Epoch 110/300
Epoch 00109: val_loss did not improve
11s - loss: 1.8559 - acc: 0.4868 - val_loss: 2.9547 - val_acc: 0.3066
Epoch 111/300
Epoch 00110: val_loss did not improve
12s - loss: 1.8614 - acc: 0.4727 - val_loss: 2.8264 - val_acc: 0.3090
Epoch 112/300
Epoch 00111: val_loss did not improve
11s - loss: 1.8410 - acc: 0.4828 - val_loss: 2.9354 - val_acc: 0.3018
Epoch 113/300
Epoch 00112: val_loss did not improve
11s - loss: 1.8344 - acc: 0.4856 - val_loss: 2.9519 - val_acc: 0.3030
Epoch 114/300
Epoch 00113: val_loss did not improve
11s - loss: 1.8094 - acc: 0.4855 - val_loss: 2.9131 - val_acc: 0.3078
Epoch 115/300
Epoch 00114: val_loss did not improve
11s - loss: 1.7642 - acc: 0.5014 - val_loss: 3.0036 - val_acc: 0.2850
Epoch 116/300
Epoch 00115: val_loss did not improve
12s - loss: 1.8013 - acc: 0.4875 - val_loss: 2.9510 - val_acc: 0.3174
Epoch 117/300
Epoch 00116: val_loss did not improve
12s - loss: 1.7737 - acc: 0.4951 - val_loss: 2.8719 - val_acc: 0.3174
Epoch 118/300
Epoch 00117: val_loss did not improve
11s - loss: 1.7998 - acc: 0.4925 - val_loss: 2.9070 - val_acc: 0.3030
Epoch 119/300
Epoch 00118: val_loss did not improve
11s - loss: 1.8160 - acc: 0.4898 - val_loss: 2.9080 - val_acc: 0.3066
Epoch 120/300
Epoch 00119: val_loss did not improve
12s - loss: 1.7380 - acc: 0.5167 - val_loss: 2.9544 - val_acc: 0.3030
Epoch 121/300
Epoch 00120: val_loss did not improve
11s - loss: 1.7641 - acc: 0.4956 - val_loss: 2.9764 - val_acc: 0.3006
Epoch 122/300
Epoch 00121: val_loss did not improve
11s - loss: 1.7470 - acc: 0.4979 - val_loss: 2.9280 - val_acc: 0.3102
Epoch 123/300
Epoch 00122: val_loss did not improve
11s - loss: 1.7359 - acc: 0.5141 - val_loss: 3.0759 - val_acc: 0.3162
Epoch 124/300
Epoch 00123: val_loss did not improve
11s - loss: 1.7201 - acc: 0.5111 - val_loss: 3.0610 - val_acc: 0.3246
Epoch 125/300
Epoch 00124: val_loss did not improve
11s - loss: 1.6890 - acc: 0.5152 - val_loss: 2.8995 - val_acc: 0.3269
Epoch 126/300
Epoch 00125: val_loss did not improve
12s - loss: 1.7197 - acc: 0.5078 - val_loss: 2.9599 - val_acc: 0.3257
Epoch 127/300
Epoch 00126: val_loss did not improve
12s - loss: 1.6980 - acc: 0.5139 - val_loss: 2.9216 - val_acc: 0.3365
Epoch 128/300
Epoch 00127: val_loss did not improve
11s - loss: 1.7127 - acc: 0.5107 - val_loss: 2.8624 - val_acc: 0.3054
Epoch 129/300
Epoch 00128: val_loss did not improve
11s - loss: 1.6635 - acc: 0.5271 - val_loss: 2.8734 - val_acc: 0.3222
Epoch 130/300
Epoch 00129: val_loss improved from 2.82439 to 2.79630, saving model to saved_models/weights.best.from_scratch.hdf5
12s - loss: 1.6844 - acc: 0.5100 - val_loss: 2.7963 - val_acc: 0.3329
Epoch 131/300
Epoch 00130: val_loss did not improve
11s - loss: 1.6400 - acc: 0.5305 - val_loss: 3.0681 - val_acc: 0.3162
Epoch 132/300
Epoch 00131: val_loss did not improve
11s - loss: 1.6598 - acc: 0.5220 - val_loss: 3.2083 - val_acc: 0.2982
Epoch 133/300
Epoch 00132: val_loss did not improve
12s - loss: 1.6557 - acc: 0.5216 - val_loss: 2.8290 - val_acc: 0.3305
Epoch 134/300
Epoch 00133: val_loss did not improve
11s - loss: 1.6225 - acc: 0.5307 - val_loss: 2.8842 - val_acc: 0.3257
Epoch 135/300
Epoch 00134: val_loss did not improve
12s - loss: 1.6329 - acc: 0.5312 - val_loss: 3.0573 - val_acc: 0.3150
Epoch 136/300
Epoch 00135: val_loss did not improve
11s - loss: 1.6418 - acc: 0.5245 - val_loss: 2.9872 - val_acc: 0.3222
Epoch 137/300
Epoch 00136: val_loss did not improve
12s - loss: 1.6259 - acc: 0.5398 - val_loss: 3.0346 - val_acc: 0.3090
Epoch 138/300
Epoch 00137: val_loss did not improve
11s - loss: 1.5872 - acc: 0.5411 - val_loss: 2.8599 - val_acc: 0.3114
Epoch 139/300
Epoch 00138: val_loss did not improve
12s - loss: 1.6117 - acc: 0.5378 - val_loss: 2.8117 - val_acc: 0.3557
Epoch 140/300
Epoch 00139: val_loss did not improve
12s - loss: 1.6266 - acc: 0.5373 - val_loss: 2.8731 - val_acc: 0.3317
Epoch 141/300
Epoch 00140: val_loss did not improve
11s - loss: 1.6305 - acc: 0.5340 - val_loss: 2.9457 - val_acc: 0.3162
Epoch 142/300
Epoch 00141: val_loss did not improve
11s - loss: 1.6004 - acc: 0.5428 - val_loss: 3.0593 - val_acc: 0.3281
Epoch 143/300
Epoch 00142: val_loss did not improve
12s - loss: 1.5857 - acc: 0.5550 - val_loss: 2.8849 - val_acc: 0.3293
Epoch 144/300
Epoch 00143: val_loss did not improve
12s - loss: 1.5761 - acc: 0.5458 - val_loss: 2.8377 - val_acc: 0.3305
Epoch 145/300
Epoch 00144: val_loss did not improve
11s - loss: 1.5621 - acc: 0.5542 - val_loss: 3.0052 - val_acc: 0.3222
Epoch 146/300
Epoch 00145: val_loss did not improve
11s - loss: 1.5669 - acc: 0.5567 - val_loss: 2.9556 - val_acc: 0.3485
Epoch 147/300
Epoch 00146: val_loss did not improve
11s - loss: 1.5807 - acc: 0.5505 - val_loss: 2.9554 - val_acc: 0.3222
Epoch 148/300
Epoch 00147: val_loss did not improve
12s - loss: 1.5947 - acc: 0.5501 - val_loss: 2.8796 - val_acc: 0.3365
Epoch 149/300
Epoch 00148: val_loss improved from 2.79630 to 2.76853, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 1.5432 - acc: 0.5579 - val_loss: 2.7685 - val_acc: 0.3521
Epoch 150/300
Epoch 00149: val_loss did not improve
11s - loss: 1.5226 - acc: 0.5658 - val_loss: 2.8500 - val_acc: 0.3437
Epoch 151/300
Epoch 00150: val_loss did not improve
12s - loss: 1.5754 - acc: 0.5498 - val_loss: 2.8755 - val_acc: 0.3437
Epoch 152/300
Epoch 00151: val_loss did not improve
11s - loss: 1.5340 - acc: 0.5615 - val_loss: 2.9018 - val_acc: 0.3473
Epoch 153/300
Epoch 00152: val_loss did not improve
11s - loss: 1.5355 - acc: 0.5563 - val_loss: 2.7819 - val_acc: 0.3413
Epoch 154/300
Epoch 00153: val_loss did not improve
12s - loss: 1.5219 - acc: 0.5730 - val_loss: 2.8435 - val_acc: 0.3497
Epoch 155/300
Epoch 00154: val_loss improved from 2.76853 to 2.73296, saving model to saved_models/weights.best.from_scratch.hdf5
11s - loss: 1.5037 - acc: 0.5629 - val_loss: 2.7330 - val_acc: 0.3401
Epoch 156/300
Epoch 00155: val_loss did not improve
11s - loss: 1.5013 - acc: 0.5643 - val_loss: 2.9647 - val_acc: 0.3509
Epoch 157/300
Epoch 00156: val_loss did not improve
11s - loss: 1.5323 - acc: 0.5698 - val_loss: 2.7779 - val_acc: 0.3413
Epoch 158/300
Epoch 00157: val_loss did not improve
12s - loss: 1.5166 - acc: 0.5663 - val_loss: 2.8470 - val_acc: 0.3509
Epoch 159/300
Epoch 00158: val_loss did not improve
13s - loss: 1.4715 - acc: 0.5760 - val_loss: 3.2862 - val_acc: 0.3126
Epoch 160/300
Epoch 00159: val_loss did not improve
10s - loss: 1.4985 - acc: 0.5743 - val_loss: 2.8283 - val_acc: 0.3629
Epoch 161/300
Epoch 00160: val_loss did not improve
12s - loss: 1.4913 - acc: 0.5743 - val_loss: 3.0030 - val_acc: 0.3485
Epoch 162/300
Epoch 00161: val_loss did not improve
11s - loss: 1.4623 - acc: 0.5779 - val_loss: 2.8050 - val_acc: 0.3545
Epoch 163/300
Epoch 00162: val_loss did not improve
11s - loss: 1.5186 - acc: 0.5577 - val_loss: 2.8405 - val_acc: 0.3497
Epoch 164/300
Epoch 00163: val_loss did not improve
11s - loss: 1.4184 - acc: 0.5988 - val_loss: 2.9190 - val_acc: 0.3437
Epoch 165/300
Epoch 00164: val_loss did not improve
11s - loss: 1.4398 - acc: 0.5827 - val_loss: 3.0055 - val_acc: 0.3329
Epoch 166/300
Epoch 00165: val_loss did not improve
11s - loss: 1.4500 - acc: 0.5859 - val_loss: 2.9803 - val_acc: 0.3222
Epoch 167/300
Epoch 00166: val_loss did not improve
11s - loss: 1.4705 - acc: 0.5795 - val_loss: 2.9600 - val_acc: 0.3222
Epoch 168/300
Epoch 00167: val_loss did not improve
11s - loss: 1.4585 - acc: 0.5755 - val_loss: 3.0323 - val_acc: 0.3246
Epoch 169/300
Epoch 00168: val_loss did not improve
11s - loss: 1.4507 - acc: 0.5803 - val_loss: 2.8564 - val_acc: 0.3365
Epoch 170/300
Epoch 00169: val_loss did not improve
11s - loss: 1.4485 - acc: 0.5786 - val_loss: 2.9321 - val_acc: 0.3234
Epoch 171/300
Epoch 00170: val_loss did not improve
11s - loss: 1.4340 - acc: 0.5869 - val_loss: 2.9944 - val_acc: 0.3246
Epoch 172/300
Epoch 00171: val_loss did not improve
11s - loss: 1.4267 - acc: 0.5800 - val_loss: 2.8640 - val_acc: 0.3401
Epoch 173/300
Epoch 00172: val_loss did not improve
12s - loss: 1.4336 - acc: 0.5884 - val_loss: 2.9200 - val_acc: 0.3449
Epoch 174/300
Epoch 00173: val_loss did not improve
11s - loss: 1.3972 - acc: 0.5990 - val_loss: 2.9069 - val_acc: 0.3796
Epoch 175/300
Epoch 00174: val_loss did not improve
11s - loss: 1.4077 - acc: 0.5849 - val_loss: 2.9460 - val_acc: 0.3365
Epoch 176/300
Epoch 00175: val_loss did not improve
11s - loss: 1.4243 - acc: 0.5947 - val_loss: 3.0203 - val_acc: 0.3569
Epoch 177/300
Epoch 00176: val_loss did not improve
11s - loss: 1.4412 - acc: 0.5883 - val_loss: 2.9233 - val_acc: 0.3269
Epoch 178/300
Epoch 00177: val_loss did not improve
11s - loss: 1.3901 - acc: 0.5989 - val_loss: 2.8448 - val_acc: 0.3485
Epoch 179/300
Epoch 00178: val_loss did not improve
11s - loss: 1.3803 - acc: 0.6012 - val_loss: 2.8139 - val_acc: 0.3425
Epoch 180/300
Epoch 00179: val_loss did not improve
11s - loss: 1.3729 - acc: 0.6048 - val_loss: 3.0264 - val_acc: 0.3257
Epoch 181/300
Epoch 00180: val_loss did not improve
11s - loss: 1.4159 - acc: 0.5952 - val_loss: 2.8442 - val_acc: 0.3557
Epoch 182/300
Epoch 00181: val_loss did not improve
11s - loss: 1.3894 - acc: 0.6039 - val_loss: 2.9161 - val_acc: 0.3449
Epoch 183/300
Epoch 00182: val_loss did not improve
11s - loss: 1.3698 - acc: 0.6054 - val_loss: 2.9121 - val_acc: 0.3437
Epoch 184/300
Epoch 00183: val_loss did not improve
11s - loss: 1.3705 - acc: 0.6046 - val_loss: 3.0893 - val_acc: 0.3545
Epoch 185/300
Epoch 00184: val_loss did not improve
11s - loss: 1.4029 - acc: 0.5943 - val_loss: 2.8820 - val_acc: 0.3557
Epoch 186/300
Epoch 00185: val_loss did not improve
11s - loss: 1.3607 - acc: 0.6081 - val_loss: 2.9413 - val_acc: 0.3389
Epoch 187/300
Epoch 00186: val_loss did not improve
11s - loss: 1.3634 - acc: 0.6062 - val_loss: 2.9395 - val_acc: 0.3545
Epoch 188/300
Epoch 00187: val_loss did not improve
11s - loss: 1.3407 - acc: 0.6206 - val_loss: 2.9443 - val_acc: 0.3497
Epoch 189/300
Epoch 00188: val_loss did not improve
11s - loss: 1.3884 - acc: 0.6062 - val_loss: 2.8903 - val_acc: 0.3677
Epoch 190/300
Epoch 00189: val_loss did not improve
12s - loss: 1.3550 - acc: 0.6088 - val_loss: 3.0943 - val_acc: 0.3473
Epoch 191/300
Epoch 00190: val_loss did not improve
11s - loss: 1.3702 - acc: 0.6057 - val_loss: 2.9284 - val_acc: 0.3701
Epoch 192/300
Epoch 00191: val_loss did not improve
11s - loss: 1.3430 - acc: 0.6117 - val_loss: 2.8568 - val_acc: 0.3545
Epoch 193/300
Epoch 00192: val_loss did not improve
11s - loss: 1.2960 - acc: 0.6226 - val_loss: 3.2051 - val_acc: 0.3317
Epoch 194/300
Epoch 00193: val_loss did not improve
11s - loss: 1.3582 - acc: 0.6130 - val_loss: 2.9888 - val_acc: 0.3317
Epoch 195/300
Epoch 00194: val_loss did not improve
11s - loss: 1.3157 - acc: 0.6229 - val_loss: 2.8824 - val_acc: 0.3473
Epoch 196/300
Epoch 00195: val_loss did not improve
11s - loss: 1.3487 - acc: 0.6063 - val_loss: 2.8852 - val_acc: 0.3581
Epoch 197/300
Epoch 00196: val_loss did not improve
12s - loss: 1.3362 - acc: 0.6168 - val_loss: 2.8357 - val_acc: 0.3617
Epoch 198/300
Epoch 00197: val_loss did not improve
12s - loss: 1.3350 - acc: 0.6154 - val_loss: 2.9432 - val_acc: 0.3413
Epoch 199/300
Epoch 00198: val_loss did not improve
11s - loss: 1.3656 - acc: 0.6060 - val_loss: 2.8695 - val_acc: 0.3521
Epoch 200/300
Epoch 00199: val_loss did not improve
12s - loss: 1.2900 - acc: 0.6252 - val_loss: 2.9444 - val_acc: 0.3521
Epoch 201/300
Epoch 00200: val_loss did not improve
12s - loss: 1.3191 - acc: 0.6170 - val_loss: 2.8538 - val_acc: 0.3353
Epoch 202/300
Epoch 00201: val_loss did not improve
12s - loss: 1.3336 - acc: 0.6196 - val_loss: 2.9269 - val_acc: 0.3413
Epoch 203/300
Epoch 00202: val_loss did not improve
12s - loss: 1.3191 - acc: 0.6172 - val_loss: 3.0610 - val_acc: 0.3353
Epoch 204/300
Epoch 00203: val_loss did not improve
12s - loss: 1.3244 - acc: 0.6231 - val_loss: 3.0007 - val_acc: 0.3246
Epoch 205/300
Epoch 00204: val_loss did not improve
11s - loss: 1.3062 - acc: 0.6249 - val_loss: 2.8975 - val_acc: 0.3605
Epoch 206/300
Epoch 00205: val_loss did not improve
11s - loss: 1.3145 - acc: 0.6194 - val_loss: 3.1334 - val_acc: 0.3317
Epoch 207/300
Epoch 00206: val_loss did not improve
11s - loss: 1.3269 - acc: 0.6161 - val_loss: 2.8352 - val_acc: 0.3653
Epoch 208/300
Epoch 00207: val_loss did not improve
11s - loss: 1.3072 - acc: 0.6249 - val_loss: 3.2398 - val_acc: 0.2982
Epoch 209/300
Epoch 00208: val_loss did not improve
13s - loss: 1.2787 - acc: 0.6367 - val_loss: 3.0344 - val_acc: 0.3437
Epoch 210/300
Epoch 00209: val_loss did not improve
12s - loss: 1.2991 - acc: 0.6305 - val_loss: 3.1774 - val_acc: 0.3377
Epoch 211/300
Epoch 00210: val_loss did not improve
11s - loss: 1.2817 - acc: 0.6333 - val_loss: 3.3676 - val_acc: 0.3006
Epoch 212/300
Epoch 00211: val_loss did not improve
11s - loss: 1.2727 - acc: 0.6311 - val_loss: 2.9631 - val_acc: 0.3653
Epoch 213/300
Epoch 00212: val_loss did not improve
11s - loss: 1.2681 - acc: 0.6364 - val_loss: 3.1535 - val_acc: 0.3425
Epoch 214/300
Epoch 00213: val_loss did not improve
11s - loss: 1.2870 - acc: 0.6248 - val_loss: 2.8875 - val_acc: 0.3617
Epoch 215/300
Epoch 00214: val_loss did not improve
11s - loss: 1.2867 - acc: 0.6286 - val_loss: 3.0790 - val_acc: 0.3401
Epoch 216/300
Epoch 00215: val_loss did not improve
11s - loss: 1.2954 - acc: 0.6312 - val_loss: 3.0811 - val_acc: 0.3269
Epoch 217/300
Epoch 00216: val_loss did not improve
12s - loss: 1.2863 - acc: 0.6341 - val_loss: 3.0641 - val_acc: 0.3772
Epoch 218/300
Epoch 00217: val_loss did not improve
11s - loss: 1.3036 - acc: 0.6295 - val_loss: 2.8905 - val_acc: 0.3737
Epoch 219/300
Epoch 00218: val_loss did not improve
12s - loss: 1.2727 - acc: 0.6379 - val_loss: 2.9694 - val_acc: 0.3401
Epoch 220/300
Epoch 00219: val_loss did not improve
12s - loss: 1.2779 - acc: 0.6247 - val_loss: 2.9694 - val_acc: 0.3497
Epoch 221/300
Epoch 00220: val_loss did not improve
11s - loss: 1.2773 - acc: 0.6278 - val_loss: 3.0046 - val_acc: 0.3713
Epoch 222/300
Epoch 00221: val_loss did not improve
11s - loss: 1.2604 - acc: 0.6336 - val_loss: 2.9840 - val_acc: 0.3569
Epoch 223/300
Epoch 00222: val_loss did not improve
11s - loss: 1.2379 - acc: 0.6548 - val_loss: 3.1106 - val_acc: 0.3569
Epoch 224/300
Epoch 00223: val_loss did not improve
11s - loss: 1.2618 - acc: 0.6326 - val_loss: 2.9814 - val_acc: 0.3737
Epoch 225/300
Epoch 00224: val_loss did not improve
11s - loss: 1.2641 - acc: 0.6313 - val_loss: 3.1144 - val_acc: 0.3485
Epoch 226/300
Epoch 00225: val_loss did not improve
11s - loss: 1.2733 - acc: 0.6403 - val_loss: 3.0626 - val_acc: 0.3545
Epoch 227/300
Epoch 00226: val_loss did not improve
11s - loss: 1.2518 - acc: 0.6417 - val_loss: 2.9064 - val_acc: 0.3629
Epoch 228/300
Epoch 00227: val_loss did not improve
11s - loss: 1.2195 - acc: 0.6506 - val_loss: 3.0410 - val_acc: 0.3593
Epoch 229/300
Epoch 00228: val_loss did not improve
11s - loss: 1.2307 - acc: 0.6538 - val_loss: 3.1107 - val_acc: 0.3485
Epoch 230/300
Epoch 00229: val_loss did not improve
11s - loss: 1.2794 - acc: 0.6400 - val_loss: 2.8490 - val_acc: 0.3737
Epoch 231/300
Epoch 00230: val_loss did not improve
11s - loss: 1.2438 - acc: 0.6428 - val_loss: 3.0929 - val_acc: 0.3557
Epoch 232/300
Epoch 00231: val_loss did not improve
11s - loss: 1.2248 - acc: 0.6448 - val_loss: 2.9718 - val_acc: 0.3557
Epoch 233/300
Epoch 00232: val_loss did not improve
11s - loss: 1.2603 - acc: 0.6389 - val_loss: 2.9906 - val_acc: 0.3701
Epoch 234/300
Epoch 00233: val_loss did not improve
11s - loss: 1.2387 - acc: 0.6503 - val_loss: 2.9487 - val_acc: 0.3569
Epoch 235/300
Epoch 00234: val_loss did not improve
12s - loss: 1.2263 - acc: 0.6507 - val_loss: 3.1784 - val_acc: 0.3605
Epoch 236/300
Epoch 00235: val_loss did not improve
11s - loss: 1.2682 - acc: 0.6413 - val_loss: 3.0690 - val_acc: 0.3629
Epoch 237/300
Epoch 00236: val_loss did not improve
11s - loss: 1.2581 - acc: 0.6415 - val_loss: 3.1214 - val_acc: 0.3377
Epoch 238/300
Epoch 00237: val_loss did not improve
11s - loss: 1.2321 - acc: 0.6415 - val_loss: 3.1017 - val_acc: 0.3760
Epoch 239/300
Epoch 00238: val_loss did not improve
11s - loss: 1.2429 - acc: 0.6509 - val_loss: 2.9358 - val_acc: 0.3629
Epoch 240/300
Epoch 00239: val_loss did not improve
11s - loss: 1.2145 - acc: 0.6534 - val_loss: 3.3173 - val_acc: 0.3186
Epoch 241/300
Epoch 00240: val_loss did not improve
11s - loss: 1.2595 - acc: 0.6354 - val_loss: 2.9766 - val_acc: 0.3629
Epoch 242/300
Epoch 00241: val_loss did not improve
11s - loss: 1.2608 - acc: 0.6409 - val_loss: 3.0806 - val_acc: 0.3581
Epoch 243/300
Epoch 00242: val_loss did not improve
11s - loss: 1.2225 - acc: 0.6512 - val_loss: 2.9621 - val_acc: 0.3389
Epoch 244/300
Epoch 00243: val_loss did not improve
11s - loss: 1.2436 - acc: 0.6489 - val_loss: 3.0556 - val_acc: 0.3593
Epoch 245/300
Epoch 00244: val_loss did not improve
11s - loss: 1.2356 - acc: 0.6472 - val_loss: 3.1083 - val_acc: 0.3569
Epoch 246/300
Epoch 00245: val_loss did not improve
11s - loss: 1.2236 - acc: 0.6478 - val_loss: 3.0649 - val_acc: 0.3713
Epoch 247/300
Epoch 00246: val_loss did not improve
11s - loss: 1.1890 - acc: 0.6527 - val_loss: 2.9779 - val_acc: 0.3713
Epoch 248/300
Epoch 00247: val_loss did not improve
11s - loss: 1.2498 - acc: 0.6361 - val_loss: 3.0612 - val_acc: 0.3641
Epoch 249/300
Epoch 00248: val_loss did not improve
11s - loss: 1.2281 - acc: 0.6400 - val_loss: 3.2058 - val_acc: 0.3150
Epoch 250/300
Epoch 00249: val_loss did not improve
11s - loss: 1.2282 - acc: 0.6521 - val_loss: 2.9815 - val_acc: 0.3641
Epoch 251/300
Epoch 00250: val_loss did not improve
11s - loss: 1.1934 - acc: 0.6586 - val_loss: 3.1255 - val_acc: 0.3533
Epoch 252/300
Epoch 00251: val_loss did not improve
11s - loss: 1.2041 - acc: 0.6578 - val_loss: 2.9817 - val_acc: 0.3425
Epoch 253/300
Epoch 00252: val_loss did not improve
12s - loss: 1.1772 - acc: 0.6611 - val_loss: 3.0573 - val_acc: 0.3760
Epoch 254/300
Epoch 00253: val_loss did not improve
11s - loss: 1.1912 - acc: 0.6534 - val_loss: 3.0399 - val_acc: 0.3425
Epoch 255/300
Epoch 00254: val_loss did not improve
11s - loss: 1.1988 - acc: 0.6567 - val_loss: 2.9954 - val_acc: 0.3689
Epoch 256/300
Epoch 00255: val_loss did not improve
12s - loss: 1.1786 - acc: 0.6710 - val_loss: 3.0673 - val_acc: 0.3784
Epoch 257/300
Epoch 00256: val_loss did not improve
11s - loss: 1.1557 - acc: 0.6636 - val_loss: 3.1177 - val_acc: 0.3509
Epoch 258/300
Epoch 00257: val_loss did not improve
12s - loss: 1.1996 - acc: 0.6586 - val_loss: 3.1095 - val_acc: 0.3449
Epoch 259/300
Epoch 00258: val_loss did not improve
11s - loss: 1.2113 - acc: 0.6543 - val_loss: 3.0455 - val_acc: 0.3509
Epoch 260/300
Epoch 00259: val_loss did not improve
12s - loss: 1.1787 - acc: 0.6586 - val_loss: 2.9808 - val_acc: 0.3485
Epoch 261/300
Epoch 00260: val_loss did not improve
13s - loss: 1.1950 - acc: 0.6520 - val_loss: 3.1806 - val_acc: 0.3198
Epoch 262/300
Epoch 00261: val_loss did not improve
11s - loss: 1.1730 - acc: 0.6618 - val_loss: 3.1900 - val_acc: 0.3760
Epoch 263/300
Epoch 00262: val_loss did not improve
10s - loss: 1.1597 - acc: 0.6690 - val_loss: 3.1922 - val_acc: 0.3545
Epoch 264/300
Epoch 00263: val_loss did not improve
12s - loss: 1.1756 - acc: 0.6635 - val_loss: 3.0415 - val_acc: 0.3653
Epoch 265/300
Epoch 00264: val_loss did not improve
11s - loss: 1.1377 - acc: 0.6710 - val_loss: 3.2926 - val_acc: 0.3497
Epoch 266/300
Epoch 00265: val_loss did not improve
11s - loss: 1.2089 - acc: 0.6607 - val_loss: 3.0818 - val_acc: 0.3701
Epoch 267/300
Epoch 00266: val_loss did not improve
11s - loss: 1.1712 - acc: 0.6719 - val_loss: 2.9997 - val_acc: 0.3461
Epoch 268/300
Epoch 00267: val_loss did not improve
11s - loss: 1.1739 - acc: 0.6605 - val_loss: 3.0360 - val_acc: 0.3629
Epoch 269/300
Epoch 00268: val_loss did not improve
11s - loss: 1.1872 - acc: 0.6574 - val_loss: 3.2070 - val_acc: 0.3425
Epoch 270/300
Epoch 00269: val_loss did not improve
11s - loss: 1.1385 - acc: 0.6753 - val_loss: 3.3015 - val_acc: 0.3677
Epoch 271/300
Epoch 00270: val_loss did not improve
11s - loss: 1.1858 - acc: 0.6598 - val_loss: 3.2386 - val_acc: 0.3617
Epoch 272/300
Epoch 00271: val_loss did not improve
11s - loss: 1.1895 - acc: 0.6628 - val_loss: 3.0579 - val_acc: 0.3269
Epoch 273/300
Epoch 00272: val_loss did not improve
11s - loss: 1.1644 - acc: 0.6661 - val_loss: 3.2318 - val_acc: 0.3617
Epoch 274/300
Epoch 00273: val_loss did not improve
11s - loss: 1.1793 - acc: 0.6638 - val_loss: 3.0692 - val_acc: 0.3485
Epoch 275/300
Epoch 00274: val_loss did not improve
11s - loss: 1.1830 - acc: 0.6594 - val_loss: 3.1016 - val_acc: 0.3533
Epoch 276/300
Epoch 00275: val_loss did not improve
11s - loss: 1.1554 - acc: 0.6662 - val_loss: 3.2181 - val_acc: 0.3629
Epoch 277/300
Epoch 00276: val_loss did not improve
11s - loss: 1.1732 - acc: 0.6638 - val_loss: 3.0275 - val_acc: 0.3581
Epoch 278/300
Epoch 00277: val_loss did not improve
11s - loss: 1.1262 - acc: 0.6692 - val_loss: 3.3129 - val_acc: 0.3617
Epoch 279/300
Epoch 00278: val_loss did not improve
11s - loss: 1.1689 - acc: 0.6637 - val_loss: 3.1307 - val_acc: 0.3617
Epoch 280/300
Epoch 00279: val_loss did not improve
11s - loss: 1.1733 - acc: 0.6700 - val_loss: 3.3585 - val_acc: 0.3569
Epoch 281/300
Epoch 00280: val_loss did not improve
12s - loss: 1.1459 - acc: 0.6773 - val_loss: 3.3610 - val_acc: 0.3425
Epoch 282/300
Epoch 00281: val_loss did not improve
11s - loss: 1.1339 - acc: 0.6775 - val_loss: 3.0743 - val_acc: 0.3653
Epoch 283/300
Epoch 00282: val_loss did not improve
11s - loss: 1.1902 - acc: 0.6639 - val_loss: 3.0227 - val_acc: 0.3281
Epoch 284/300
Epoch 00283: val_loss did not improve
11s - loss: 1.1691 - acc: 0.6636 - val_loss: 3.5012 - val_acc: 0.3473
Epoch 285/300
Epoch 00284: val_loss did not improve
11s - loss: 1.1791 - acc: 0.6707 - val_loss: 3.3248 - val_acc: 0.3365
Epoch 286/300
Epoch 00285: val_loss did not improve
11s - loss: 1.1619 - acc: 0.6695 - val_loss: 3.2358 - val_acc: 0.3401
Epoch 287/300
Epoch 00286: val_loss did not improve
11s - loss: 1.1379 - acc: 0.6762 - val_loss: 2.9375 - val_acc: 0.3689
Epoch 288/300
Epoch 00287: val_loss did not improve
11s - loss: 1.1924 - acc: 0.6539 - val_loss: 2.9666 - val_acc: 0.3701
Epoch 289/300
Epoch 00288: val_loss did not improve
12s - loss: 1.1567 - acc: 0.6748 - val_loss: 3.0876 - val_acc: 0.3509
Epoch 290/300
Epoch 00289: val_loss did not improve
11s - loss: 1.1602 - acc: 0.6710 - val_loss: 3.1491 - val_acc: 0.3485
Epoch 291/300
Epoch 00290: val_loss did not improve
12s - loss: 1.1285 - acc: 0.6730 - val_loss: 3.1089 - val_acc: 0.3509
Epoch 292/300
Epoch 00291: val_loss did not improve
11s - loss: 1.1221 - acc: 0.6777 - val_loss: 3.0216 - val_acc: 0.3473
Epoch 293/300
Epoch 00292: val_loss did not improve
11s - loss: 1.1100 - acc: 0.6800 - val_loss: 3.1042 - val_acc: 0.3557
Epoch 294/300
Epoch 00293: val_loss did not improve
11s - loss: 1.1449 - acc: 0.6724 - val_loss: 3.0695 - val_acc: 0.3605
Epoch 295/300
Epoch 00294: val_loss did not improve
11s - loss: 1.1583 - acc: 0.6693 - val_loss: 2.8360 - val_acc: 0.3413
Epoch 296/300
Epoch 00295: val_loss did not improve
11s - loss: 1.1706 - acc: 0.6668 - val_loss: 3.0029 - val_acc: 0.3329
Epoch 297/300
Epoch 00296: val_loss did not improve
11s - loss: 1.1503 - acc: 0.6717 - val_loss: 3.1345 - val_acc: 0.3617
Epoch 298/300
Epoch 00297: val_loss did not improve
11s - loss: 1.1946 - acc: 0.6660 - val_loss: 3.1828 - val_acc: 0.3485
Epoch 299/300
Epoch 00298: val_loss did not improve
11s - loss: 1.1495 - acc: 0.6806 - val_loss: 3.1585 - val_acc: 0.3485
Epoch 300/300
Epoch 00299: val_loss did not improve
11s - loss: 1.1550 - acc: 0.6743 - val_loss: 3.1233 - val_acc: 0.3377
</pre>
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<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">)</span>
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<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dog_breed_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">test_tensors</span><span class="p">]</span>

<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;acc&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_acc&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;model accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;epoch&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;model loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;epoch&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Test accuracy: 33.6124%
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<p><a id='step4'></a></p>
<h2 id="Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">Step 4: Use a CNN to Classify Dog Breeds<a class="anchor-link" href="#Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">&#182;</a></h2><p>To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.</p>
<h3 id="Obtain-Bottleneck-Features">Obtain Bottleneck Features<a class="anchor-link" href="#Obtain-Bottleneck-Features">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;bottleneck_features/DogVGG16Data.npz&#39;</span><span class="p">)</span>
<span class="n">train_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
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<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3><p>The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG16</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.3</span><span class="p">))</span>
<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

<span class="n">VGG16_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dropout_7 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_7 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________
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<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<h3 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>  

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">VGG16_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_VGG16</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_VGG16</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">133</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/100
Epoch 00000: val_loss improved from inf to 12.32647, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 14.3285 - acc: 0.0370 - val_loss: 12.3265 - val_acc: 0.1030
Epoch 2/100
Epoch 00001: val_loss improved from 12.32647 to 10.48043, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 12.5295 - acc: 0.1015 - val_loss: 10.4804 - val_acc: 0.1904
Epoch 3/100
Epoch 00002: val_loss improved from 10.48043 to 9.42599, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 11.1839 - acc: 0.1644 - val_loss: 9.4260 - val_acc: 0.2587
Epoch 4/100
Epoch 00003: val_loss improved from 9.42599 to 8.82049, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 10.1821 - acc: 0.2290 - val_loss: 8.8205 - val_acc: 0.3162
Epoch 5/100
Epoch 00004: val_loss improved from 8.82049 to 8.50695, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 9.5129 - acc: 0.2687 - val_loss: 8.5069 - val_acc: 0.3629
Epoch 6/100
Epoch 00005: val_loss improved from 8.50695 to 8.33651, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 9.1585 - acc: 0.3030 - val_loss: 8.3365 - val_acc: 0.3665
Epoch 7/100
Epoch 00006: val_loss improved from 8.33651 to 8.05239, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 8.6996 - acc: 0.3449 - val_loss: 8.0524 - val_acc: 0.3988
Epoch 8/100
Epoch 00007: val_loss improved from 8.05239 to 7.93696, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 8.4459 - acc: 0.3608 - val_loss: 7.9370 - val_acc: 0.4024
Epoch 9/100
Epoch 00008: val_loss improved from 7.93696 to 7.82658, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 8.1326 - acc: 0.3925 - val_loss: 7.8266 - val_acc: 0.4072
Epoch 10/100
Epoch 00009: val_loss improved from 7.82658 to 7.63858, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.9583 - acc: 0.4036 - val_loss: 7.6386 - val_acc: 0.4263
Epoch 11/100
Epoch 00010: val_loss improved from 7.63858 to 7.56212, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.7774 - acc: 0.4189 - val_loss: 7.5621 - val_acc: 0.4299
Epoch 12/100
Epoch 00011: val_loss improved from 7.56212 to 7.42660, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.5795 - acc: 0.4374 - val_loss: 7.4266 - val_acc: 0.4515
Epoch 13/100
Epoch 00012: val_loss did not improve
0s - loss: 7.4190 - acc: 0.4555 - val_loss: 7.4406 - val_acc: 0.4431
Epoch 14/100
Epoch 00013: val_loss improved from 7.42660 to 7.38458, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.3627 - acc: 0.4659 - val_loss: 7.3846 - val_acc: 0.4539
Epoch 15/100
Epoch 00014: val_loss improved from 7.38458 to 7.36753, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.2867 - acc: 0.4723 - val_loss: 7.3675 - val_acc: 0.4419
Epoch 16/100
Epoch 00015: val_loss improved from 7.36753 to 7.27132, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.1945 - acc: 0.4811 - val_loss: 7.2713 - val_acc: 0.4515
Epoch 17/100
Epoch 00016: val_loss improved from 7.27132 to 7.20519, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.0654 - acc: 0.4921 - val_loss: 7.2052 - val_acc: 0.4527
Epoch 18/100
Epoch 00017: val_loss improved from 7.20519 to 7.14654, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 7.0090 - acc: 0.4961 - val_loss: 7.1465 - val_acc: 0.4647
Epoch 19/100
Epoch 00018: val_loss did not improve
0s - loss: 6.9238 - acc: 0.5135 - val_loss: 7.1830 - val_acc: 0.4647
Epoch 20/100
Epoch 00019: val_loss improved from 7.14654 to 7.13518, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.9033 - acc: 0.5139 - val_loss: 7.1352 - val_acc: 0.4599
Epoch 21/100
Epoch 00020: val_loss improved from 7.13518 to 7.09920, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.8864 - acc: 0.5168 - val_loss: 7.0992 - val_acc: 0.4754
Epoch 22/100
Epoch 00021: val_loss did not improve
0s - loss: 6.8617 - acc: 0.5232 - val_loss: 7.1509 - val_acc: 0.4814
Epoch 23/100
Epoch 00022: val_loss did not improve
0s - loss: 6.8493 - acc: 0.5223 - val_loss: 7.1688 - val_acc: 0.4719
Epoch 24/100
Epoch 00023: val_loss did not improve
0s - loss: 6.8186 - acc: 0.5316 - val_loss: 7.1214 - val_acc: 0.4790
Epoch 25/100
Epoch 00024: val_loss did not improve
0s - loss: 6.8007 - acc: 0.5352 - val_loss: 7.1695 - val_acc: 0.4587
Epoch 26/100
Epoch 00025: val_loss did not improve
0s - loss: 6.7690 - acc: 0.5382 - val_loss: 7.1388 - val_acc: 0.4778
Epoch 27/100
Epoch 00026: val_loss improved from 7.09920 to 7.09746, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.7860 - acc: 0.5376 - val_loss: 7.0975 - val_acc: 0.4754
Epoch 28/100
Epoch 00027: val_loss did not improve
0s - loss: 6.7462 - acc: 0.5397 - val_loss: 7.1540 - val_acc: 0.4695
Epoch 29/100
Epoch 00028: val_loss did not improve
0s - loss: 6.7646 - acc: 0.5395 - val_loss: 7.1193 - val_acc: 0.4790
Epoch 30/100
Epoch 00029: val_loss did not improve
0s - loss: 6.7196 - acc: 0.5442 - val_loss: 7.1727 - val_acc: 0.4707
Epoch 31/100
Epoch 00030: val_loss did not improve
0s - loss: 6.7321 - acc: 0.5418 - val_loss: 7.1229 - val_acc: 0.4814
Epoch 32/100
Epoch 00031: val_loss improved from 7.09746 to 7.07457, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.6785 - acc: 0.5481 - val_loss: 7.0746 - val_acc: 0.4778
Epoch 33/100
Epoch 00032: val_loss improved from 7.07457 to 7.06739, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.6657 - acc: 0.5430 - val_loss: 7.0674 - val_acc: 0.4802
Epoch 34/100
Epoch 00033: val_loss did not improve
0s - loss: 6.6229 - acc: 0.5481 - val_loss: 7.1051 - val_acc: 0.4790
Epoch 35/100
Epoch 00034: val_loss improved from 7.06739 to 7.01716, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.5763 - acc: 0.5504 - val_loss: 7.0172 - val_acc: 0.4826
Epoch 36/100
Epoch 00035: val_loss improved from 7.01716 to 6.98150, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.5265 - acc: 0.5572 - val_loss: 6.9815 - val_acc: 0.4731
Epoch 37/100
Epoch 00036: val_loss improved from 6.98150 to 6.93946, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.4776 - acc: 0.5572 - val_loss: 6.9395 - val_acc: 0.4850
Epoch 38/100
Epoch 00037: val_loss improved from 6.93946 to 6.87119, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.4565 - acc: 0.5593 - val_loss: 6.8712 - val_acc: 0.4886
Epoch 39/100
Epoch 00038: val_loss improved from 6.87119 to 6.77000, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.3931 - acc: 0.5606 - val_loss: 6.7700 - val_acc: 0.4922
Epoch 40/100
Epoch 00039: val_loss did not improve
0s - loss: 6.3360 - acc: 0.5675 - val_loss: 6.7782 - val_acc: 0.4874
Epoch 41/100
Epoch 00040: val_loss improved from 6.77000 to 6.73774, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.3113 - acc: 0.5699 - val_loss: 6.7377 - val_acc: 0.5006
Epoch 42/100
Epoch 00041: val_loss did not improve
0s - loss: 6.2704 - acc: 0.5756 - val_loss: 6.8090 - val_acc: 0.4934
Epoch 43/100
Epoch 00042: val_loss did not improve
0s - loss: 6.2589 - acc: 0.5816 - val_loss: 6.7807 - val_acc: 0.4922
Epoch 44/100
Epoch 00043: val_loss did not improve
0s - loss: 6.2622 - acc: 0.5814 - val_loss: 6.7964 - val_acc: 0.5030
Epoch 45/100
Epoch 00044: val_loss did not improve
0s - loss: 6.2621 - acc: 0.5771 - val_loss: 6.8284 - val_acc: 0.4910
Epoch 46/100
Epoch 00045: val_loss did not improve
0s - loss: 6.2489 - acc: 0.5801 - val_loss: 6.7809 - val_acc: 0.4826
Epoch 47/100
Epoch 00046: val_loss did not improve
0s - loss: 6.2346 - acc: 0.5829 - val_loss: 6.7627 - val_acc: 0.5078
Epoch 48/100
Epoch 00047: val_loss did not improve
0s - loss: 6.2115 - acc: 0.5831 - val_loss: 6.7844 - val_acc: 0.4910
Epoch 49/100
Epoch 00048: val_loss did not improve
0s - loss: 6.2016 - acc: 0.5837 - val_loss: 6.7870 - val_acc: 0.5030
Epoch 50/100
Epoch 00049: val_loss did not improve
0s - loss: 6.1908 - acc: 0.5831 - val_loss: 6.7755 - val_acc: 0.4934
Epoch 51/100
Epoch 00050: val_loss did not improve
0s - loss: 6.1854 - acc: 0.5819 - val_loss: 6.7407 - val_acc: 0.4934
Epoch 52/100
Epoch 00051: val_loss improved from 6.73774 to 6.68742, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.1805 - acc: 0.5843 - val_loss: 6.6874 - val_acc: 0.5066
Epoch 53/100
Epoch 00052: val_loss did not improve
0s - loss: 6.1699 - acc: 0.5865 - val_loss: 6.7676 - val_acc: 0.4994
Epoch 54/100
Epoch 00053: val_loss improved from 6.68742 to 6.67464, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.1595 - acc: 0.5907 - val_loss: 6.6746 - val_acc: 0.5042
Epoch 55/100
Epoch 00054: val_loss did not improve
0s - loss: 6.1538 - acc: 0.5891 - val_loss: 6.7375 - val_acc: 0.4934
Epoch 56/100
Epoch 00055: val_loss did not improve
0s - loss: 6.1549 - acc: 0.5892 - val_loss: 6.6920 - val_acc: 0.5078
Epoch 57/100
Epoch 00056: val_loss did not improve
0s - loss: 6.1349 - acc: 0.5931 - val_loss: 6.6820 - val_acc: 0.5018
Epoch 58/100
Epoch 00057: val_loss did not improve
0s - loss: 6.1501 - acc: 0.5892 - val_loss: 6.7170 - val_acc: 0.5030
Epoch 59/100
Epoch 00058: val_loss did not improve
0s - loss: 6.1239 - acc: 0.5937 - val_loss: 6.6965 - val_acc: 0.5042
Epoch 60/100
Epoch 00059: val_loss did not improve
0s - loss: 6.1250 - acc: 0.5930 - val_loss: 6.7326 - val_acc: 0.5078
Epoch 61/100
Epoch 00060: val_loss did not improve
0s - loss: 6.1007 - acc: 0.5978 - val_loss: 6.7463 - val_acc: 0.4946
Epoch 62/100
Epoch 00061: val_loss did not improve
0s - loss: 6.1174 - acc: 0.5946 - val_loss: 6.7253 - val_acc: 0.4994
Epoch 63/100
Epoch 00062: val_loss did not improve
0s - loss: 6.0936 - acc: 0.5918 - val_loss: 6.7273 - val_acc: 0.4946
Epoch 64/100
Epoch 00063: val_loss improved from 6.67464 to 6.66130, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.0476 - acc: 0.5913 - val_loss: 6.6613 - val_acc: 0.5054
Epoch 65/100
Epoch 00064: val_loss improved from 6.66130 to 6.61851, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 6.0092 - acc: 0.6012 - val_loss: 6.6185 - val_acc: 0.5102
Epoch 66/100
Epoch 00065: val_loss improved from 6.61851 to 6.58232, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.9942 - acc: 0.6027 - val_loss: 6.5823 - val_acc: 0.5150
Epoch 67/100
Epoch 00066: val_loss improved from 6.58232 to 6.56200, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.9848 - acc: 0.6078 - val_loss: 6.5620 - val_acc: 0.5042
Epoch 68/100
Epoch 00067: val_loss improved from 6.56200 to 6.56137, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.9934 - acc: 0.6015 - val_loss: 6.5614 - val_acc: 0.5090
Epoch 69/100
Epoch 00068: val_loss did not improve
0s - loss: 5.9760 - acc: 0.6072 - val_loss: 6.6183 - val_acc: 0.4994
Epoch 70/100
Epoch 00069: val_loss did not improve
0s - loss: 5.9718 - acc: 0.6066 - val_loss: 6.5707 - val_acc: 0.5126
Epoch 71/100
Epoch 00070: val_loss did not improve
0s - loss: 5.9810 - acc: 0.6045 - val_loss: 6.6335 - val_acc: 0.5102
Epoch 72/100
Epoch 00071: val_loss did not improve
0s - loss: 5.9770 - acc: 0.6064 - val_loss: 6.5943 - val_acc: 0.5054
Epoch 73/100
Epoch 00072: val_loss did not improve
0s - loss: 5.9755 - acc: 0.6063 - val_loss: 6.6650 - val_acc: 0.5006
Epoch 74/100
Epoch 00073: val_loss improved from 6.56137 to 6.54824, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.9598 - acc: 0.6082 - val_loss: 6.5482 - val_acc: 0.5186
Epoch 75/100
Epoch 00074: val_loss did not improve
0s - loss: 5.9668 - acc: 0.6069 - val_loss: 6.6230 - val_acc: 0.5090
Epoch 76/100
Epoch 00075: val_loss did not improve
0s - loss: 5.9552 - acc: 0.6097 - val_loss: 6.5887 - val_acc: 0.5126
Epoch 77/100
Epoch 00076: val_loss did not improve
0s - loss: 5.9657 - acc: 0.6060 - val_loss: 6.6431 - val_acc: 0.5042
Epoch 78/100
Epoch 00077: val_loss did not improve
0s - loss: 5.9615 - acc: 0.6093 - val_loss: 6.6239 - val_acc: 0.5150
Epoch 79/100
Epoch 00078: val_loss did not improve
0s - loss: 5.9595 - acc: 0.6093 - val_loss: 6.6224 - val_acc: 0.5114
Epoch 80/100
Epoch 00079: val_loss did not improve
0s - loss: 5.9586 - acc: 0.6097 - val_loss: 6.6079 - val_acc: 0.5066
Epoch 81/100
Epoch 00080: val_loss did not improve
0s - loss: 5.9461 - acc: 0.6111 - val_loss: 6.5952 - val_acc: 0.5138
Epoch 82/100
Epoch 00081: val_loss did not improve
0s - loss: 5.9629 - acc: 0.6096 - val_loss: 6.5987 - val_acc: 0.5138
Epoch 83/100
Epoch 00082: val_loss did not improve
0s - loss: 5.9633 - acc: 0.6075 - val_loss: 6.6319 - val_acc: 0.5054
Epoch 84/100
Epoch 00083: val_loss did not improve
0s - loss: 5.9383 - acc: 0.6138 - val_loss: 6.5928 - val_acc: 0.5126
Epoch 85/100
Epoch 00084: val_loss did not improve
0s - loss: 5.9461 - acc: 0.6123 - val_loss: 6.5805 - val_acc: 0.5114
Epoch 86/100
Epoch 00085: val_loss did not improve
0s - loss: 5.9443 - acc: 0.6115 - val_loss: 6.6547 - val_acc: 0.5006
Epoch 87/100
Epoch 00086: val_loss did not improve
0s - loss: 5.9379 - acc: 0.6123 - val_loss: 6.5830 - val_acc: 0.5102
Epoch 88/100
Epoch 00087: val_loss did not improve
0s - loss: 5.9640 - acc: 0.6102 - val_loss: 6.6257 - val_acc: 0.5018
Epoch 89/100
Epoch 00088: val_loss did not improve
0s - loss: 5.9393 - acc: 0.6121 - val_loss: 6.6893 - val_acc: 0.5054
Epoch 90/100
Epoch 00089: val_loss did not improve
0s - loss: 5.9422 - acc: 0.6118 - val_loss: 6.6256 - val_acc: 0.5102
Epoch 91/100
Epoch 00090: val_loss did not improve
0s - loss: 5.9258 - acc: 0.6069 - val_loss: 6.5516 - val_acc: 0.5090
Epoch 92/100
Epoch 00091: val_loss did not improve
0s - loss: 5.8383 - acc: 0.6055 - val_loss: 6.5854 - val_acc: 0.5006
Epoch 93/100
Epoch 00092: val_loss improved from 6.54824 to 6.53443, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.7825 - acc: 0.6150 - val_loss: 6.5344 - val_acc: 0.4982
Epoch 94/100
Epoch 00093: val_loss improved from 6.53443 to 6.44561, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.7587 - acc: 0.6108 - val_loss: 6.4456 - val_acc: 0.5102
Epoch 95/100
Epoch 00094: val_loss did not improve
0s - loss: 5.6984 - acc: 0.6180 - val_loss: 6.4666 - val_acc: 0.5042
Epoch 96/100
Epoch 00095: val_loss improved from 6.44561 to 6.43260, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.6785 - acc: 0.6162 - val_loss: 6.4326 - val_acc: 0.5066
Epoch 97/100
Epoch 00096: val_loss improved from 6.43260 to 6.39903, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.6585 - acc: 0.6207 - val_loss: 6.3990 - val_acc: 0.5114
Epoch 98/100
Epoch 00097: val_loss improved from 6.39903 to 6.39233, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.6175 - acc: 0.6263 - val_loss: 6.3923 - val_acc: 0.5126
Epoch 99/100
Epoch 00098: val_loss improved from 6.39233 to 6.36614, saving model to saved_models/weights.best.VGG16.hdf5
0s - loss: 5.6182 - acc: 0.6256 - val_loss: 6.3661 - val_acc: 0.5198
Epoch 100/100
Epoch 00099: val_loss did not improve
0s - loss: 5.5811 - acc: 0.6272 - val_loss: 6.4000 - val_acc: 0.5114
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<pre>&lt;keras.callbacks.History at 0x161b38069e8&gt;</pre>
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<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">)</span>
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<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_VGG16</span><span class="p">]</span>

<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 52.0335%
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<h3 id="Predict-Dog-Breed-with-the-Model">Predict Dog Breed with the Model<a class="anchor-link" href="#Predict-Dog-Breed-with-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span>

<span class="k">def</span> <span class="nf">VGG16_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_VGG16</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>

<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">dog_files</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>    
    <span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">VGG16_predict_breed</span><span class="p">(</span><span class="n">file</span><span class="p">))</span>
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<pre>Australian_shepherd_00888.jpg Pembroke_welsh_corgi
Norwegian_elkhound_07150.jpg Norwegian_elkhound
Golden_retriever_05243.jpg Golden_retriever
Irish_wolfhound_06077.jpg Irish_wolfhound
Norwich_terrier_07234.jpg Norwich_terrier
Afghan_hound_00148.jpg Newfoundland
Collie_03815.jpg Pembroke_welsh_corgi
Smooth_fox_terrier_08119.jpg Smooth_fox_terrier
Australian_shepherd_00808.jpg Welsh_springer_spaniel
Glen_of_imaal_terrier_05160.jpg Glen_of_imaal_terrier
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<p><a id='step5'></a></p>
<h2 id="Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">&#182;</a></h2><p>You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.</p>
<p>In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:</p>
<ul>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz">VGG-19</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz">ResNet-50</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz">Inception</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz">Xception</a> bottleneck features</li>
</ul>
<p>The files are encoded as such:</p>

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

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

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

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;bottleneck_features/DogInceptionV3Data.npz&#39;</span><span class="p">)</span>
<span class="n">train_InceptionV3</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_InceptionV3</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_InceptionV3</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

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

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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>
<p><strong>Answer:</strong> <strong><em>I chose the Inception network to join to a fully connected neural network. The output data from Inception network gets transformed by a global average pooling to a format, which can be pushed into the fully connected network. I was experimenting with adding more and more dense hidden layers but after a point, accuracy was not improved, so I sticked to 3 hidden layers: two of them have relu activation function and the last one operates with softmax, which is exceptinally good with categorization. Adding dropout layers also improved accuracy.</em></strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">InceptionV3_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_InceptionV3</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">))</span>
<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

<span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dropout_8 (Dropout)          (None, 2048)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 500)               1024500   
_________________________________________________________________
dropout_9 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_9 (Dense)              (None, 250)               125250    
_________________________________________________________________
dropout_10 (Dropout)         (None, 250)               0         
_________________________________________________________________
dense_10 (Dense)             (None, 133)               33383     
=================================================================
Total params: 1,183,133
Trainable params: 1,183,133
Non-trainable params: 0
_________________________________________________________________
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<h3 id="(IMPLEMENTATION)-Compile-the-Model">(IMPLEMENTATION) Compile the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Compile-the-Model">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

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

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.InceptionV3x.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_InceptionV3</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_InceptionV3</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">133</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/100
Epoch 00000: val_loss improved from inf to 1.46835, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 3.6501 - acc: 0.2400 - val_loss: 1.4684 - val_acc: 0.6719
Epoch 2/100
Epoch 00001: val_loss improved from 1.46835 to 0.81036, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 1.7371 - acc: 0.5624 - val_loss: 0.8104 - val_acc: 0.7557
Epoch 3/100
Epoch 00002: val_loss improved from 0.81036 to 0.70792, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 1.2814 - acc: 0.6525 - val_loss: 0.7079 - val_acc: 0.7820
Epoch 4/100
Epoch 00003: val_loss improved from 0.70792 to 0.58309, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 1.0495 - acc: 0.7018 - val_loss: 0.5831 - val_acc: 0.8084
Epoch 5/100
Epoch 00004: val_loss improved from 0.58309 to 0.56733, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 0.9269 - acc: 0.7344 - val_loss: 0.5673 - val_acc: 0.8216
Epoch 6/100
Epoch 00005: val_loss did not improve
1s - loss: 0.8505 - acc: 0.7542 - val_loss: 0.5740 - val_acc: 0.8323
Epoch 7/100
Epoch 00006: val_loss did not improve
1s - loss: 0.7847 - acc: 0.7743 - val_loss: 0.6063 - val_acc: 0.8251
Epoch 8/100
Epoch 00007: val_loss did not improve
1s - loss: 0.7136 - acc: 0.7897 - val_loss: 0.5887 - val_acc: 0.8216
Epoch 9/100
Epoch 00008: val_loss did not improve
1s - loss: 0.6826 - acc: 0.7903 - val_loss: 0.5836 - val_acc: 0.8132
Epoch 10/100
Epoch 00009: val_loss improved from 0.56733 to 0.56609, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 0.6417 - acc: 0.8067 - val_loss: 0.5661 - val_acc: 0.8156
Epoch 11/100
Epoch 00010: val_loss did not improve
1s - loss: 0.5933 - acc: 0.8198 - val_loss: 0.5865 - val_acc: 0.8168
Epoch 12/100
Epoch 00011: val_loss improved from 0.56609 to 0.54996, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 0.5662 - acc: 0.8223 - val_loss: 0.5500 - val_acc: 0.8383
Epoch 13/100
Epoch 00012: val_loss improved from 0.54996 to 0.54003, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 0.5458 - acc: 0.8271 - val_loss: 0.5400 - val_acc: 0.8359
Epoch 14/100
Epoch 00013: val_loss did not improve
1s - loss: 0.5279 - acc: 0.8329 - val_loss: 0.5643 - val_acc: 0.8371
Epoch 15/100
Epoch 00014: val_loss did not improve
1s - loss: 0.5200 - acc: 0.8337 - val_loss: 0.5557 - val_acc: 0.8419
Epoch 16/100
Epoch 00015: val_loss improved from 0.54003 to 0.51491, saving model to saved_models/weights.best.InceptionV3x.hdf5
1s - loss: 0.4928 - acc: 0.8440 - val_loss: 0.5149 - val_acc: 0.8359
Epoch 17/100
Epoch 00016: val_loss did not improve
1s - loss: 0.4808 - acc: 0.8452 - val_loss: 0.5387 - val_acc: 0.8491
Epoch 18/100
Epoch 00017: val_loss did not improve
1s - loss: 0.4655 - acc: 0.8522 - val_loss: 0.5871 - val_acc: 0.8335
Epoch 19/100
Epoch 00018: val_loss did not improve
1s - loss: 0.4321 - acc: 0.8653 - val_loss: 0.5954 - val_acc: 0.8383
Epoch 20/100
Epoch 00019: val_loss did not improve
1s - loss: 0.4142 - acc: 0.8671 - val_loss: 0.5808 - val_acc: 0.8407
Epoch 21/100
Epoch 00020: val_loss did not improve
1s - loss: 0.4175 - acc: 0.8695 - val_loss: 0.5818 - val_acc: 0.8407
Epoch 22/100
Epoch 00021: val_loss did not improve
1s - loss: 0.4025 - acc: 0.8719 - val_loss: 0.5574 - val_acc: 0.8479
Epoch 23/100
Epoch 00022: val_loss did not improve
1s - loss: 0.4016 - acc: 0.8743 - val_loss: 0.6157 - val_acc: 0.8455
Epoch 24/100
Epoch 00023: val_loss did not improve
1s - loss: 0.3882 - acc: 0.8754 - val_loss: 0.6231 - val_acc: 0.8479
Epoch 25/100
Epoch 00024: val_loss did not improve
1s - loss: 0.3769 - acc: 0.8817 - val_loss: 0.6085 - val_acc: 0.8539
Epoch 26/100
Epoch 00025: val_loss did not improve
1s - loss: 0.3547 - acc: 0.8891 - val_loss: 0.6070 - val_acc: 0.8419
Epoch 27/100
Epoch 00026: val_loss did not improve
1s - loss: 0.3403 - acc: 0.8891 - val_loss: 0.6812 - val_acc: 0.8479
Epoch 28/100
Epoch 00027: val_loss did not improve
1s - loss: 0.3470 - acc: 0.8937 - val_loss: 0.6264 - val_acc: 0.8539
Epoch 29/100
Epoch 00028: val_loss did not improve
1s - loss: 0.3346 - acc: 0.8900 - val_loss: 0.6498 - val_acc: 0.8587
Epoch 30/100
Epoch 00029: val_loss did not improve
1s - loss: 0.3356 - acc: 0.8936 - val_loss: 0.6151 - val_acc: 0.8407
Epoch 31/100
Epoch 00030: val_loss did not improve
1s - loss: 0.3230 - acc: 0.8936 - val_loss: 0.6321 - val_acc: 0.8455
Epoch 32/100
Epoch 00031: val_loss did not improve
1s - loss: 0.3181 - acc: 0.8963 - val_loss: 0.6231 - val_acc: 0.8551
Epoch 33/100
Epoch 00032: val_loss did not improve
1s - loss: 0.3239 - acc: 0.8942 - val_loss: 0.6207 - val_acc: 0.8599
Epoch 34/100
Epoch 00033: val_loss did not improve
1s - loss: 0.3083 - acc: 0.9063 - val_loss: 0.6442 - val_acc: 0.8395
Epoch 35/100
Epoch 00034: val_loss did not improve
1s - loss: 0.3041 - acc: 0.9030 - val_loss: 0.6214 - val_acc: 0.8683
Epoch 36/100
Epoch 00035: val_loss did not improve
1s - loss: 0.3197 - acc: 0.8984 - val_loss: 0.6645 - val_acc: 0.8479
Epoch 37/100
Epoch 00036: val_loss did not improve
1s - loss: 0.3042 - acc: 0.9016 - val_loss: 0.6265 - val_acc: 0.8635
Epoch 38/100
Epoch 00037: val_loss did not improve
1s - loss: 0.3044 - acc: 0.9072 - val_loss: 0.6641 - val_acc: 0.8527
Epoch 39/100
Epoch 00038: val_loss did not improve
1s - loss: 0.2869 - acc: 0.9064 - val_loss: 0.6804 - val_acc: 0.8467
Epoch 40/100
Epoch 00039: val_loss did not improve
1s - loss: 0.2857 - acc: 0.9079 - val_loss: 0.6498 - val_acc: 0.8431
Epoch 41/100
Epoch 00040: val_loss did not improve
1s - loss: 0.2708 - acc: 0.9135 - val_loss: 0.6978 - val_acc: 0.8491
Epoch 42/100
Epoch 00041: val_loss did not improve
1s - loss: 0.2897 - acc: 0.9109 - val_loss: 0.7069 - val_acc: 0.8503
Epoch 43/100
Epoch 00042: val_loss did not improve
1s - loss: 0.2737 - acc: 0.9093 - val_loss: 0.7076 - val_acc: 0.8383
Epoch 44/100
Epoch 00043: val_loss did not improve
1s - loss: 0.2675 - acc: 0.9147 - val_loss: 0.6762 - val_acc: 0.8599
Epoch 45/100
Epoch 00044: val_loss did not improve
1s - loss: 0.2839 - acc: 0.9126 - val_loss: 0.7199 - val_acc: 0.8443
Epoch 46/100
Epoch 00045: val_loss did not improve
1s - loss: 0.2659 - acc: 0.9144 - val_loss: 0.7409 - val_acc: 0.8527
Epoch 47/100
Epoch 00046: val_loss did not improve
1s - loss: 0.2485 - acc: 0.9177 - val_loss: 0.6886 - val_acc: 0.8647
Epoch 48/100
Epoch 00047: val_loss did not improve
1s - loss: 0.2764 - acc: 0.9178 - val_loss: 0.6943 - val_acc: 0.8539
Epoch 49/100
Epoch 00048: val_loss did not improve
1s - loss: 0.2538 - acc: 0.9222 - val_loss: 0.7385 - val_acc: 0.8539
Epoch 50/100
Epoch 00049: val_loss did not improve
1s - loss: 0.2644 - acc: 0.9184 - val_loss: 0.7315 - val_acc: 0.8539
Epoch 51/100
Epoch 00050: val_loss did not improve
1s - loss: 0.2341 - acc: 0.9228 - val_loss: 0.7592 - val_acc: 0.8587
Epoch 52/100
Epoch 00051: val_loss did not improve
1s - loss: 0.2448 - acc: 0.9244 - val_loss: 0.7267 - val_acc: 0.8659
Epoch 53/100
Epoch 00052: val_loss did not improve
1s - loss: 0.2604 - acc: 0.9190 - val_loss: 0.7391 - val_acc: 0.8503
Epoch 54/100
Epoch 00053: val_loss did not improve
1s - loss: 0.2504 - acc: 0.9208 - val_loss: 0.7704 - val_acc: 0.8443
Epoch 55/100
Epoch 00054: val_loss did not improve
1s - loss: 0.2338 - acc: 0.9278 - val_loss: 0.7810 - val_acc: 0.8491
Epoch 56/100
Epoch 00055: val_loss did not improve
1s - loss: 0.2375 - acc: 0.9225 - val_loss: 0.7594 - val_acc: 0.8467
Epoch 57/100
Epoch 00056: val_loss did not improve
1s - loss: 0.2277 - acc: 0.9328 - val_loss: 0.8379 - val_acc: 0.8419
Epoch 58/100
Epoch 00057: val_loss did not improve
1s - loss: 0.2336 - acc: 0.9284 - val_loss: 0.7737 - val_acc: 0.8527
Epoch 59/100
Epoch 00058: val_loss did not improve
1s - loss: 0.2436 - acc: 0.9265 - val_loss: 0.7580 - val_acc: 0.8467
Epoch 60/100
Epoch 00059: val_loss did not improve
1s - loss: 0.2498 - acc: 0.9275 - val_loss: 0.7990 - val_acc: 0.8407
Epoch 61/100
Epoch 00060: val_loss did not improve
1s - loss: 0.2382 - acc: 0.9301 - val_loss: 0.7687 - val_acc: 0.8479
Epoch 62/100
Epoch 00061: val_loss did not improve
1s - loss: 0.2195 - acc: 0.9298 - val_loss: 0.8073 - val_acc: 0.8491
Epoch 63/100
Epoch 00062: val_loss did not improve
1s - loss: 0.2347 - acc: 0.9298 - val_loss: 0.7823 - val_acc: 0.8503
Epoch 64/100
Epoch 00063: val_loss did not improve
1s - loss: 0.2305 - acc: 0.9344 - val_loss: 0.8100 - val_acc: 0.8599
Epoch 65/100
Epoch 00064: val_loss did not improve
1s - loss: 0.2310 - acc: 0.9280 - val_loss: 0.8386 - val_acc: 0.8575
Epoch 66/100
Epoch 00065: val_loss did not improve
1s - loss: 0.2179 - acc: 0.9368 - val_loss: 0.8382 - val_acc: 0.8467
Epoch 67/100
Epoch 00066: val_loss did not improve
1s - loss: 0.2213 - acc: 0.9338 - val_loss: 0.8019 - val_acc: 0.8563
Epoch 68/100
Epoch 00067: val_loss did not improve
1s - loss: 0.2318 - acc: 0.9325 - val_loss: 0.8361 - val_acc: 0.8575
Epoch 69/100
Epoch 00068: val_loss did not improve
1s - loss: 0.2478 - acc: 0.9287 - val_loss: 0.8282 - val_acc: 0.8623
Epoch 70/100
Epoch 00069: val_loss did not improve
1s - loss: 0.2392 - acc: 0.9287 - val_loss: 0.7864 - val_acc: 0.8491
Epoch 71/100
Epoch 00070: val_loss did not improve
1s - loss: 0.2379 - acc: 0.9295 - val_loss: 0.7457 - val_acc: 0.8527
Epoch 72/100
Epoch 00071: val_loss did not improve
1s - loss: 0.2168 - acc: 0.9358 - val_loss: 0.8820 - val_acc: 0.8443
Epoch 73/100
Epoch 00072: val_loss did not improve
1s - loss: 0.2001 - acc: 0.9382 - val_loss: 0.8484 - val_acc: 0.8515
Epoch 74/100
Epoch 00073: val_loss did not improve
1s - loss: 0.2045 - acc: 0.9410 - val_loss: 0.8537 - val_acc: 0.8491
Epoch 75/100
Epoch 00074: val_loss did not improve
1s - loss: 0.2265 - acc: 0.9334 - val_loss: 0.8835 - val_acc: 0.8467
Epoch 76/100
Epoch 00075: val_loss did not improve
1s - loss: 0.2298 - acc: 0.9314 - val_loss: 0.8935 - val_acc: 0.8563
Epoch 77/100
Epoch 00076: val_loss did not improve
1s - loss: 0.2195 - acc: 0.9376 - val_loss: 0.8789 - val_acc: 0.8515
Epoch 78/100
Epoch 00077: val_loss did not improve
1s - loss: 0.2164 - acc: 0.9368 - val_loss: 0.8701 - val_acc: 0.8587
Epoch 79/100
Epoch 00078: val_loss did not improve
1s - loss: 0.2235 - acc: 0.9340 - val_loss: 0.8696 - val_acc: 0.8479
Epoch 80/100
Epoch 00079: val_loss did not improve
1s - loss: 0.2040 - acc: 0.9398 - val_loss: 0.8718 - val_acc: 0.8611
Epoch 81/100
Epoch 00080: val_loss did not improve
1s - loss: 0.2210 - acc: 0.9388 - val_loss: 0.8673 - val_acc: 0.8503
Epoch 82/100
Epoch 00081: val_loss did not improve
1s - loss: 0.2166 - acc: 0.9371 - val_loss: 0.8719 - val_acc: 0.8635
Epoch 83/100
Epoch 00082: val_loss did not improve
1s - loss: 0.2134 - acc: 0.9422 - val_loss: 0.9412 - val_acc: 0.8527
Epoch 84/100
Epoch 00083: val_loss did not improve
1s - loss: 0.2268 - acc: 0.9395 - val_loss: 0.8421 - val_acc: 0.8443
Epoch 85/100
Epoch 00084: val_loss did not improve
1s - loss: 0.1918 - acc: 0.9475 - val_loss: 0.9145 - val_acc: 0.8503
Epoch 86/100
Epoch 00085: val_loss did not improve
1s - loss: 0.2070 - acc: 0.9392 - val_loss: 0.9048 - val_acc: 0.8419
Epoch 87/100
Epoch 00086: val_loss did not improve
1s - loss: 0.2187 - acc: 0.9427 - val_loss: 0.8260 - val_acc: 0.8575
Epoch 88/100
Epoch 00087: val_loss did not improve
1s - loss: 0.2020 - acc: 0.9392 - val_loss: 0.9941 - val_acc: 0.8503
Epoch 89/100
Epoch 00088: val_loss did not improve
1s - loss: 0.2149 - acc: 0.9428 - val_loss: 0.8790 - val_acc: 0.8551
Epoch 90/100
Epoch 00089: val_loss did not improve
1s - loss: 0.2022 - acc: 0.9421 - val_loss: 0.8939 - val_acc: 0.8587
Epoch 91/100
Epoch 00090: val_loss did not improve
1s - loss: 0.1970 - acc: 0.9443 - val_loss: 0.8815 - val_acc: 0.8527
Epoch 92/100
Epoch 00091: val_loss did not improve
1s - loss: 0.2272 - acc: 0.9403 - val_loss: 0.9276 - val_acc: 0.8395
Epoch 93/100
Epoch 00092: val_loss did not improve
1s - loss: 0.1907 - acc: 0.9463 - val_loss: 0.9812 - val_acc: 0.8431
Epoch 94/100
Epoch 00093: val_loss did not improve
1s - loss: 0.2313 - acc: 0.9373 - val_loss: 1.0170 - val_acc: 0.8551
Epoch 95/100
Epoch 00094: val_loss did not improve
1s - loss: 0.2030 - acc: 0.9458 - val_loss: 0.9524 - val_acc: 0.8515
Epoch 96/100
Epoch 00095: val_loss did not improve
1s - loss: 0.2031 - acc: 0.9412 - val_loss: 0.9953 - val_acc: 0.8527
Epoch 97/100
Epoch 00096: val_loss did not improve
1s - loss: 0.2212 - acc: 0.9401 - val_loss: 0.9414 - val_acc: 0.8587
Epoch 98/100
Epoch 00097: val_loss did not improve
1s - loss: 0.2062 - acc: 0.9440 - val_loss: 1.0121 - val_acc: 0.8527
Epoch 99/100
Epoch 00098: val_loss did not improve
1s - loss: 0.2116 - acc: 0.9428 - val_loss: 0.9744 - val_acc: 0.8563
Epoch 100/100
Epoch 00099: val_loss did not improve
1s - loss: 0.2123 - acc: 0.9443 - val_loss: 0.8978 - val_acc: 0.8623
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<h3 id="(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">(IMPLEMENTATION) Load the Model with the Best Validation Loss<a class="anchor-link" href="#(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.InceptionV3x.hdf5&#39;</span><span class="p">)</span>
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<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">InceptionV3_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_InceptionV3</span><span class="p">]</span>

<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">InceptionV3_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">InceptionV3_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span>
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<pre>Test accuracy: 80.2632%
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<h3 id="(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">(IMPLEMENTATION) Predict Dog Breed with the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">&#182;</a></h3><p>Write a function that takes an image path as input and returns the dog breed (<code>Affenpinscher</code>, <code>Afghan_hound</code>, etc) that is predicted by your model.</p>
<p>Similar to the analogous function in Step 5, your function should have three steps:</p>
<ol>
<li>Extract the bottleneck features corresponding to the chosen CNN model.</li>
<li>Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.</li>
<li>Use the <code>dog_names</code> array defined in Step 0 of this notebook to return the corresponding breed.</li>
</ol>
<p>The functions to extract the bottleneck features can be found in <code>extract_bottleneck_features.py</code>, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function</p>

<pre><code>extract_{network}

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

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span>

<span class="k">def</span> <span class="nf">InceptionV3_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># extract bottleneck features</span>
    <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_InceptionV3</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="c1"># obtain predicted vector</span>
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">InceptionV3_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span>
    <span class="c1"># return dog breed that is predicted by the model</span>
    <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>

<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">dog_files</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>  
    <span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file</span><span class="p">),</span> <span class="n">InceptionV3_predict_breed</span><span class="p">(</span><span class="n">file</span><span class="p">))</span>
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<pre>Anatolian_shepherd_dog_00678.jpg Mastiff
Pharaoh_hound_07744.jpg Pharaoh_hound
English_toy_spaniel_04526.jpg English_toy_spaniel
Saint_bernard_08037.jpg Saint_bernard
Border_collie_02021.jpg Border_collie
Havanese_05597.jpg Havanese
Irish_setter_05828.jpg Irish_setter
Tibetan_mastiff_08163.jpg Tibetan_mastiff
Alaskan_malamute_00315.jpg Alaskan_malamute
Golden_retriever_05233.jpg Golden_retriever
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<p><a id='step6'></a></p>
<h2 id="Step-6:-Write-your-Algorithm">Step 6: Write your Algorithm<a class="anchor-link" href="#Step-6:-Write-your-Algorithm">&#182;</a></h2><p>Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,</p>
<ul>
<li>if a <strong>dog</strong> is detected in the image, return the predicted breed.</li>
<li>if a <strong>human</strong> is detected in the image, return the resembling dog breed.</li>
<li>if <strong>neither</strong> is detected in the image, provide output that indicates an error.</li>
</ul>
<p>You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the <code>face_detector</code> and <code>dog_detector</code> functions developed above.  You are <strong>required</strong> to use your CNN from Step 5 to predict dog breed.</p>
<p>Some sample output for our algorithm is provided below, but feel free to design your own user experience!</p>
<p><img src="images/sample_human_output.png" alt="Sample Human Output"></p>
<h3 id="(IMPLEMENTATION)-Write-your-Algorithm">(IMPLEMENTATION) Write your Algorithm<a class="anchor-link" href="#(IMPLEMENTATION)-Write-your-Algorithm">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
        <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
        <span class="k">return</span> <span class="s2">&quot;Dog, looks like: &quot;</span> <span class="o">+</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>
    <span class="k">elif</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
        <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
        <span class="k">return</span> <span class="s2">&quot;Human, looks like: &quot;</span> <span class="o">+</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="s2">&quot;Neither human nor dog&quot;</span>

<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">dog_files</span><span class="p">,</span> <span class="n">human_files</span><span class="p">]),</span> <span class="mi">10</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">detector</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; filename: &quot;</span> <span class="o">+</span>  <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">file</span><span class="p">))</span>
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<pre>Human, looks like: French_bulldog filename: Colin_Powell_0117.jpg
Human, looks like: French_bulldog filename: Andy_Rooney_0001.jpg
Human, looks like: Bull_terrier filename: Michelle_Branch_0002.jpg
Dog, looks like: Dalmatian filename: Icelandic_sheepdog_05708.jpg
Dog, looks like: Bull_terrier filename: Pekingese_07605.jpg
Human, looks like: Beauceron filename: Jayson_Williams_0001.jpg
Dog, looks like: Bull_terrier filename: Bullmastiff_02914.jpg
Human, looks like: Bull_terrier filename: Toni_Braxton_0002.jpg
Human, looks like: Curly-coated_retriever filename: Chawki_Armali_0001.jpg
Dog, looks like: Bull_terrier filename: Boston_terrier_02277.jpg
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<p><a id='step7'></a></p>
<h2 id="Step-7:-Test-Your-Algorithm">Step 7: Test Your Algorithm<a class="anchor-link" href="#Step-7:-Test-Your-Algorithm">&#182;</a></h2><p>In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that <strong>you</strong> look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?</p>
<h3 id="(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">(IMPLEMENTATION) Test Your Algorithm on Sample Images!<a class="anchor-link" href="#(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">&#182;</a></h3><p>Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.</p>
<p><strong>Question 6:</strong> Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.</p>
<p><strong>Answer:</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="k">import</span> <span class="n">imshow</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">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span>               

<span class="n">test_filenames</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="s2">&quot;test_images/*&quot;</span><span class="p">)</span>

<span class="n">test_images</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">:</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
    <span class="n">test_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
    
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
<span class="n">columns</span> <span class="o">=</span> <span class="mi">6</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">picture</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">test_images</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test_images</span><span class="p">)</span> <span class="o">/</span> <span class="n">columns</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">set_text</span><span class="p">(</span><span class="n">detector</span><span class="p">(</span><span class="n">test_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">picture</span><span class="p">)</span>
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<h2 id="Remarks">Remarks<a class="anchor-link" href="#Remarks">&#182;</a></h2><p>Provided that I found results in AIND forum with mostly 15-20% accuracy, I am quite happy about the results I have. These ideas could definitely improve the accuracy of the algorithm:</p>
<ul>
<li>There are many images in the dog dataset, which contain other objects and animals. A cleaner dataset could have provided better accuracy</li>
<li>State of the art CNNs are trained on millions of images while the size of the dog dataset is very small. It is obvious that a much bigger set of data would have given a better algorithm</li>
<li>I was trying to make the CNN more accurate with trying out different random hyperparameters. Searching for an optimal set of hyperparameters with an algorithm could have given an improved CNN</li>
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