PyTorch编程实验MNIST_Net.html

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<title>MNIST_Net</title>

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
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.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
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<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
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div#notebook {
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    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
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}
</style>

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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
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</pre></div>

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<h2 id="&#19979;&#36733;&#12289;&#35835;&#21462;MNIST&#25968;&#25454;&#38598;">&#19979;&#36733;&#12289;&#35835;&#21462;MNIST&#25968;&#25454;&#38598;<a class="anchor-link" href="#&#19979;&#36733;&#12289;&#35835;&#21462;MNIST&#25968;&#25454;&#38598;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transform</span>

<span class="c1"># 方法1 手动下载</span>
<span class="c1"># 从官网下载的mnist,将四个压缩文件解压并一起保存在dataset文件夹下</span>
<span class="c1"># 路径 E:/mnist/dataset/</span>
<span class="c1">#                       t10k-images.idx3-ubyte</span>
<span class="c1">#                       t10k-labels.idx1-ubyte</span>
<span class="c1">#                       train-images.idx3-ubyte</span>
<span class="c1">#                       train-labels.idx1-ubyte</span>
<span class="c1">#                       t10k-images.idx3-ubyte.gz</span>
<span class="c1">#                       t10k-labels.idx1-ubyte.gz</span>
<span class="c1">#                       train-images.idx3-ubyte.gz</span>
<span class="c1">#                       train-labels.idx1-ubyte.gz</span>

<span class="c1"># 读取训练集和测试集数据</span>
<span class="n">train_images_path</span> <span class="o">=</span> <span class="s2">&quot;E:/mnist/dataset/train-images.idx3-ubyte&quot;</span>
<span class="n">train_labels_path</span> <span class="o">=</span> <span class="s2">&quot;E:/mnist/dataset/train-labels.idx1-ubyte&quot;</span>
<span class="n">test_images_path</span> <span class="o">=</span> <span class="s2">&quot;E:/mnist/dataset/t10k-images.idx3-ubyte&quot;</span>
<span class="n">test_labels_path</span> <span class="o">=</span> <span class="s2">&quot;E:/mnist/dataset/t10k-labels.idx1-ubyte&quot;</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">train_images_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">train_labels_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">test_images_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">test_labels_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>

<span class="c1"># 将numpy数组转换为Tensor</span>
<span class="n">train_images</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_images</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">train_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_labels</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
<span class="n">test_images</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">test_images</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="o">/</span> <span class="mf">255.0</span>
<span class="n">test_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">test_labels</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
<span class="c1"># 不加.copy()的话有一个警告，大致内容就是：&lt;PyTorch库将一个NumPy数组转换为一个PyTorch张量。然而，在转换过程中，PyTorch库检测到输入的NumPy数组是不可写的（non-writeable），这意味着PyTorch张量不能直接对其进行写操作。因此，PyTorch库发出了一个警告（UserWarning），提醒用户要么将该数组复制一份以保护其数据，要么将其转换为可写的（writeable）数组，然后再将其转换为张量。&gt;</span>
<span class="c1"># 解决办法：a&lt;xxx.copy()&gt;; b 补充设定：xxx.flags.writeable = True</span>

<span class="c1"># 定义数据集对象</span>
<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">train_images</span><span class="p">,</span> <span class="n">train_labels</span><span class="p">)</span>
<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">test_images</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)</span>

<span class="c1"># 定义batch size和num_workers</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">num_workers</span> <span class="o">=</span> <span class="mi">1</span>

<span class="c1"># 创建DataLoader对象</span>
<span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span>
<span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">)</span>
<span class="c1"># 在上面的代码中，我们首先读取MNIST官网上下载的原始数据集，并将其转换为PyTorch的Tensor对象。</span>
<span class="c1"># 然后使用torch.utils.data.TensorDataset将Tensor对象转换为PyTorch的数据集对象，用于模型训练。</span>
<span class="c1"># 最后，我们使用torch.utils.data.DataLoader将数据集对象转换为PyTorch的DataLoader对象，用于数据的批量加载和并行处理。</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;MNIST load done&#39;</span><span class="p">)</span>
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<pre>MNIST load done
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<h2 id="&#35299;&#26512;MNIST">&#35299;&#26512;MNIST<a class="anchor-link" href="#&#35299;&#26512;MNIST">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 解析MNIST数据集</span>
<span class="c1"># 不同于我们常见到的数据集图片通过jpg这样的图片格式保存，标签通过txt、xml、json等常规文本文件保存。</span>
<span class="c1"># MNIST的图片和标签均通过二进制文件进行保存，也就是我们无法直接在Windows中查看手写数字的图片和标签，</span>
<span class="c1"># 必须要先解码。</span>
<span class="c1"># 从上面官方提供的编码格式可以看出，MNIST的图片和标签均采用二进制编码，图片二进制编码文件的前16个字节（Byte）</span>
<span class="c1"># 为描述性内容，我们可以不管，因为我们已经知道MNIST训练集60000张图片、测试集10000张图片，</span>
<span class="c1"># 所有图片分辨率为28*28，因此可以直接通过Python自带的gzip工具读取MNIST数据集的图片和标签二进制编码文件，</span>
<span class="c1"># 再通过numpy.frombuffer()方法解析读取到的二进制信息，解析MNIST二进制文件的Python脚本如下</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">通过gzip和numpy解析MNIST数据集的二进制文件</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>  <span class="c1"># 设置Python日志管理工具的消息格式和显示级别</span>


<span class="k">def</span> <span class="nf">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;解析MNIST二进制文件, 并返回解析结果</span>
<span class="sd">    输入参数:</span>
<span class="sd">        minst_file: MNIST数据集的文件地址. 类型: 字符串.</span>

<span class="sd">    返回值:</span>
<span class="sd">        解析后的numpy数组</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">minst_file_addr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">minst_file_name</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">minst_file_addr</span><span class="p">)</span>  <span class="c1"># 根据地址获取MNIST文件名字</span>
        <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">minst_file_addr</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">minst_file</span><span class="p">:</span>
            <span class="n">mnist_file_content</span> <span class="o">=</span> <span class="n">minst_file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
        <span class="k">if</span> <span class="s2">&quot;label&quot;</span> <span class="ow">in</span> <span class="n">minst_file_name</span><span class="p">:</span>  <span class="c1"># 传入的为标签二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>  <span class="c1"># MNIST标签文件的前8个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
        <span class="k">else</span><span class="p">:</span>  <span class="c1"># 传入的为图片二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>  <span class="c1"># MNIST图片文件的前16个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s2">&quot;请传入MNIST文件地址!&quot;</span><span class="p">)</span>

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


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="o">=</span><span class="s2">&quot;E:/mnist/dataset/t10k-labels-idx1-ubyte.gz&quot;</span><span class="p">)</span>  <span class="c1"># t10k-images-idx1-ubyte.gz文件应该和本脚本在同一个目录下，否则应该修改地址</span>
    <span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>  <span class="c1"># 10000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">])</span>  <span class="c1"># [7 2 1 0 4 1 4 9 5 9]</span>
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<pre>10000
[7 2 1 0 4 1 4 9 5 9]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>  <span class="c1"># 设置Python日志管理工具的消息格式和显示级别</span>


<span class="k">def</span> <span class="nf">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;解析MNIST二进制文件, 并返回解析结果</span>
<span class="sd">    输入参数:</span>
<span class="sd">        minst_file: MNIST数据集的文件地址. 类型: 字符串.</span>

<span class="sd">    返回值:</span>
<span class="sd">        解析后的numpy数组</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">minst_file_addr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">minst_file_name</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">minst_file_addr</span><span class="p">)</span>  <span class="c1"># 根据地址获取MNIST文件名字</span>
        <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">minst_file_addr</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">minst_file</span><span class="p">:</span>
            <span class="n">mnist_file_content</span> <span class="o">=</span> <span class="n">minst_file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
        <span class="k">if</span> <span class="s2">&quot;label&quot;</span> <span class="ow">in</span> <span class="n">minst_file_name</span><span class="p">:</span>  <span class="c1"># 传入的为标签二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>  <span class="c1"># MNIST标签文件的前8个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
        <span class="k">else</span><span class="p">:</span>  <span class="c1"># 传入的为图片二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>  <span class="c1"># MNIST图片文件的前16个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s2">&quot;请传入MNIST文件地址!&quot;</span><span class="p">)</span>

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


<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     data = parse_mnist(minst_file_addr=&quot;E:/mnist/dataset/t10k-labels-idx1-ubyte.gz&quot;)  # t10k-images-idx1-ubyte.gz文件应该和本脚本在同一个目录下，否则应该修改地址</span>
<span class="c1">#     print(len(data))  # 10000</span>
<span class="c1">#     print(data[0:10])  # [7 2 1 0 4 1 4 9 5 9]</span>

<span class="c1"># 显示MNIST数据集中训练集的前9章图片和标签</span>
<span class="c1"># 通过上一步解析后的MNIST数据已经是numpy.narray数据类型，可以直接通过pillow、opencv和matplotlib库直接可视化了。</span>
<span class="c1"># 此处通过matplotlib库可视化MNIST训练集的前9张图片及其标签，代码和效果如下：</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">通过gzip和numpy解析MNIST数据集的二进制文件, 并可视化训练集前10张图片和标签</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">logging</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>  <span class="c1"># 设置Python日志管理工具的消息格式和显示级别</span>

<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.sans-serif&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;SimSun&quot;</span>  <span class="c1"># 确保plt绘图正常显示中文</span>
<span class="c1"># plt.rcParams[&quot;font.sans-serif&quot;] = &quot;SimHei&quot;  # 确保plt绘图正常显示中文</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>  <span class="c1"># 设置plt绘图尺寸</span>

<span class="k">def</span> <span class="nf">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
    <span class="sd">&quot;&quot;&quot;解析MNIST二进制文件, 并返回解析结果</span>
<span class="sd">    输入参数:</span>
<span class="sd">        minst_file: MNIST数据集的文件地址. 类型: 字符串.</span>

<span class="sd">    返回值:</span>
<span class="sd">        解析后的numpy数组</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">minst_file_addr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">minst_file_name</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">minst_file_addr</span><span class="p">)</span>  <span class="c1"># 根据地址获取MNIST文件名字</span>
        <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">minst_file_addr</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">minst_file</span><span class="p">:</span>
            <span class="n">mnist_file_content</span> <span class="o">=</span> <span class="n">minst_file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
        <span class="k">if</span> <span class="s2">&quot;label&quot;</span> <span class="ow">in</span> <span class="n">minst_file_name</span><span class="p">:</span>  <span class="c1"># 传入的为标签二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span>
                                 <span class="n">offset</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>  <span class="c1"># MNIST标签文件的前8个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
        <span class="k">else</span><span class="p">:</span>  <span class="c1"># 传入的为图片二进制编码文件地址</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buffer</span><span class="o">=</span><span class="n">mnist_file_content</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span>
                                 <span class="n">offset</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>  <span class="c1"># MNIST图片文件的前16个字节为描述性内容，直接从第九个字节开始读取标签，并解析</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">msg</span><span class="o">=</span><span class="s2">&quot;请传入MNIST文件地址!&quot;</span><span class="p">)</span>

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


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="n">train_imgs</span> <span class="o">=</span> <span class="n">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="o">=</span><span class="s2">&quot;E:/mnist/dataset/train-images-idx3-ubyte.gz&quot;</span><span class="p">)</span>  <span class="c1"># 训练集图像</span>
    <span class="n">train_labels</span> <span class="o">=</span> <span class="n">parse_mnist</span><span class="p">(</span><span class="n">minst_file_addr</span><span class="o">=</span><span class="s2">&quot;E:/mnist/dataset/train-labels-idx1-ubyte.gz&quot;</span><span class="p">)</span>  <span class="c1"># 训练集标签</span>

    <span class="c1"># 可视化</span>
    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
    <span class="n">ax</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="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</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="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</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="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</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="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">train_imgs</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;标签为</span><span class="si">{</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="c1"># plt.rcParams = [&#39;Sans-serif&#39;]</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>  <span class="c1"># 显示绘图</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    《findfont: score(FontEntry(fname=&#39;C:...site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\STIXSizOneSymBol.ttf&#39;, name=&#39;STIXSizeOneSym&#39;, style=&#39;normal&#39;, variant=&#39;normal&#39;, weight=700, stretch=&#39;normal&#39;, size=&#39;scalable&#39;)) = 10.335》</span>
<span class="sd">    这段报错是来自于Matplotlib库，它的作用是绘制数据可视化图形，其中包含了一些字体匹配的信息。报错信息中包含了一些寻找字体的过程，它们是在尝试找到一个适合当前绘图环境的字体。在这个过程中，Matplotlib会尝试在系统中查找已经安装的字体文件，然后进行匹配。</span>
<span class="sd">    这段报错的含义是Matplotlib尝试查找适合绘图的字体，但没有找到匹配的字体，因此它会使用默认字体作为替代。</span>
<span class="sd">    &#39;&#39;&#39;</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
</pre></div>

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

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
"
>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>KeysView(RcParams({&#39;_internal.classic_mode&#39;: False,
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          &#39;animation.bitrate&#39;: -1,
          &#39;animation.codec&#39;: &#39;h264&#39;,
          &#39;animation.convert_args&#39;: [],
          &#39;animation.convert_path&#39;: &#39;convert&#39;,
          &#39;animation.embed_limit&#39;: 20.0,
          &#39;animation.ffmpeg_args&#39;: [],
          &#39;animation.ffmpeg_path&#39;: &#39;ffmpeg&#39;,
          &#39;animation.frame_format&#39;: &#39;png&#39;,
          &#39;animation.html&#39;: &#39;none&#39;,
          &#39;animation.writer&#39;: &#39;ffmpeg&#39;,
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          &#39;axes.axisbelow&#39;: &#39;line&#39;,
          &#39;axes.edgecolor&#39;: &#39;black&#39;,
          &#39;axes.facecolor&#39;: &#39;white&#39;,
          &#39;axes.formatter.limits&#39;: [-5, 6],
          &#39;axes.formatter.min_exponent&#39;: 0,
          &#39;axes.formatter.offset_threshold&#39;: 4,
          &#39;axes.formatter.use_locale&#39;: False,
          &#39;axes.formatter.use_mathtext&#39;: False,
          &#39;axes.formatter.useoffset&#39;: True,
          &#39;axes.grid&#39;: False,
          &#39;axes.grid.axis&#39;: &#39;both&#39;,
          &#39;axes.grid.which&#39;: &#39;major&#39;,
          &#39;axes.labelcolor&#39;: &#39;black&#39;,
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          &#39;axes.labelweight&#39;: &#39;normal&#39;,
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          &#39;axes.ymargin&#39;: 0.05,
          &#39;axes.zmargin&#39;: 0.05,
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          &#39;backend_fallback&#39;: True,
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          &#39;boxplot.flierprops.markeredgewidth&#39;: 1.0,
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<h2 id="torchvision.datasets.MNIST">torchvision.datasets.MNIST<a class="anchor-link" href="#torchvision.datasets.MNIST">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import torchvision</span>
<span class="c1"># # # 方法2，借助python自动下载</span>


<span class="c1"># 归一化处理    （mean, std）  --也可以尝试其他数据</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))])</span>

<span class="c1"># 下载数据集  </span>
<span class="n">train_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;./mnist&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>  
<span class="n">test_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;./mnist&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>  <span class="c1"># train=True训练集，=False测试集</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 实例化Dataset，然后用dataloader进行打包，载入数据。</span>
<span class="c1"># batch_size 就是批量大小</span>
<span class="c1"># shuffle = True 打乱数据</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>  <span class="c1"># 批量大小  可以自行设置</span>
<span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">test_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<h2 id="MNIST&#25968;&#25454;&#38598;&#20998;&#26512;">MNIST&#25968;&#25454;&#38598;&#20998;&#26512;<a class="anchor-link" href="#MNIST&#25968;&#25454;&#38598;&#20998;&#26512;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 1. 抽样展示 12个样本图片以及对应的标签</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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">train_dataset</span><span class="o">.</span><span class="n">train_data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;none&#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="s2">&quot;Labels: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">train_labels</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">xticks</span><span class="p">([])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">yticks</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="c1"># 接口版本的问题   标签  labels  targets</span>
</pre></div>

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<pre>D:\Anaconda3\anaconda3\lib\site-packages\torchvision\datasets\mnist.py:75: UserWarning: train_data has been renamed data
  warnings.warn(&#34;train_data has been renamed data&#34;)
D:\Anaconda3\anaconda3\lib\site-packages\torchvision\datasets\mnist.py:65: UserWarning: train_labels has been renamed targets
  warnings.warn(&#34;train_labels has been renamed targets&#34;)
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<img 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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 2 其他基本信息</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Train dataset:&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Numpy array shape:&#39;</span><span class="p">,</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Tensor shape:&#39;</span><span class="p">,</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Data type:&#39;</span><span class="p">,</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Label type:&#39;</span><span class="p">,</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">targets</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Number of classes:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">classes</span><span class="p">))</span>
<span class="nb">print</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test dataset:&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Numpy array shape:&#39;</span><span class="p">,</span> <span class="n">test_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Tensor shape:&#39;</span><span class="p">,</span> <span class="n">test_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Data type:&#39;</span><span class="p">,</span> <span class="n">test_dataset</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Label type:&#39;</span><span class="p">,</span> <span class="n">test_dataset</span><span class="o">.</span><span class="n">targets</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39; - Number of classes:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_dataset</span><span class="o">.</span><span class="n">classes</span><span class="p">))</span>
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<pre>Train dataset:
 - Numpy array shape: torch.Size([60000, 28, 28])
 - Tensor shape: torch.Size([60000, 28, 28])
 - Data type: torch.uint8
 - Label type: torch.int64
 - Number of classes: 10

Test dataset:
 - Numpy array shape: torch.Size([10000, 28, 28])
 - Tensor shape: torch.Size([10000, 28, 28])
 - Data type: torch.uint8
 - Label type: torch.int64
 - Number of classes: 10
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<h2 id="model">model<a class="anchor-link" href="#model">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>

<span class="c1"># LeNet</span>
<span class="k">class</span> <span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">LeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="c1"># 构造网络有两种方式一个是seqential还有一个是module,前者在后者中也可以使用，</span>
        <span class="c1"># 这里使用的是sequential方式，将网络结构按顺序添加即可</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>     <span class="c1">#input_size=(1*28*28)</span>
            <span class="c1"># 第一个卷积层，输入通道为1，输出通道为6，卷积核大小为5，步长为1，</span>
            <span class="c1"># 填充为2保证输入输出尺寸相同</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>  <span class="c1">#padding=2保证输入输出尺寸相同</span>
            <span class="c1"># 激活函数,两个网络层之间加入，引入非线性</span>

            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>      <span class="c1">#input_size=(6*28*28)</span>
            <span class="c1"># 池化层，大小为2步长为2</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>  <span class="c1"># output_size=(6*14*14)</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>      <span class="c1">#input_size=(16*10*10)</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>  <span class="c1">#output_size=(16*5*5)</span>
        <span class="p">)</span>
        <span class="c1"># 全连接层，输入是16*5*5特征图，神经元数目120</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">16</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="p">)</span>
        <span class="c1"># 全连接层神经元数目输入为上一层的120，输出为84</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="p">)</span>
        <span class="c1"># 最后一层全连接层神经元数目10，与上一个全连接层同理</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

    <span class="c1"># 定义前向传播过程，输入为x，也就是把前面定义的网络结构赋予了一个运行顺序</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="c1"># nn.Linear()的输入输出都是维度为一的值，所以要把多维度的tensor展平成一维</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>


<span class="c1"># AlexNet</span>
<span class="k">class</span> <span class="nc">AlexNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">AlexNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>  <span class="c1"># in_channels, out_channels, kernel_size, stride, padding</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>  <span class="c1"># kernel_size, stride</span>
            <span class="c1"># 减小卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
            <span class="c1"># 连续3个卷积层，且使用更小的卷积窗口。除了最后的卷积层外，进一步增大了输出通道数。</span>
            <span class="c1"># 前两个卷积层后不使用池化层来减小输入的高和宽</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">384</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">384</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="p">)</span>
        <span class="c1"># 这里全连接层的输出个数比LeNet中的大数倍。使用丢弃层来缓解过拟合</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">256</span><span class="o">*</span><span class="mi">5</span><span class="o">*</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4096</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">4096</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
            <span class="c1"># 输出层。由于这里使用Fashion-MNIST，所以用类别数为10，而非论文中的1000</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4096</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
        <span class="p">)</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</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="mi">1</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">x</span>


<span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</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="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</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="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">64</span> <span class="o">*</span> <span class="mi">7</span> <span class="o">*</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span> <span class="o">*</span> <span class="mi">7</span> <span class="o">*</span> <span class="mi">7</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>
    
<span class="c1"># MNIST_Net</span>
<span class="k">class</span> <span class="nc">MNIST_Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">MNIST_Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">64</span><span class="o">*</span><span class="mi">7</span><span class="o">*</span><span class="mi">7</span><span class="p">,</span> <span class="mi">160</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">160</span><span class="p">,</span> <span class="mi">80</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">80</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个六层卷积结构的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个最大池化层的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第二个十二层卷积结构的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第二个最大池化层的输出-展平处理前的shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个线性层输入-展平处理后的shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>
    <span class="c1"># 为什么定义forward而不定义backend   --  PyTorch内置了自动求导Autograd</span>
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<h2 id="&#23450;&#20041;&#36229;&#21442;&#25968;">&#23450;&#20041;&#36229;&#21442;&#25968;<a class="anchor-link" href="#&#23450;&#20041;&#36229;&#21442;&#25968;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 定义超参数</span>
<span class="n">num_epochs</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span>
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<h2 id="&#21021;&#22987;&#21270;&#27169;&#22411;">&#21021;&#22987;&#21270;&#27169;&#22411;<a class="anchor-link" href="#&#21021;&#22987;&#21270;&#27169;&#22411;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 模型选择</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MNIST_Net</span><span class="p">()</span>
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<h1 id="&#23450;&#20041;&#25439;&#22833;&#20989;&#25968;Loss&#21644;&#20248;&#21270;&#22120;Optimizer">&#23450;&#20041;&#25439;&#22833;&#20989;&#25968;Loss&#21644;&#20248;&#21270;&#22120;Optimizer<a class="anchor-link" href="#&#23450;&#20041;&#25439;&#22833;&#20989;&#25968;Loss&#21644;&#20248;&#21270;&#22120;Optimizer">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>

<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
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<h2 id="&#35757;&#32451;">&#35757;&#32451;<a class="anchor-link" href="#&#35757;&#32451;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 定义超参数</span>
<span class="n">num_epochs</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.01</span>

<span class="c1"># 初始化模型、损失函数和优化器</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>

<span class="c1"># 记录训练过程中的训练精度、测试精度、训练Loss和测试Loss</span>
<span class="n">train_acc_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">test_acc_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">train_loss_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">test_loss_list</span> <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># 训练模型</span>
<span class="n">best_test_acc</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
    <span class="n">train_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">train_correct</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">train_total</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">test_correct</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">test_total</span> <span class="o">=</span> <span class="mi">0</span>

    <span class="c1"># 训练模式</span>
    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span>
        <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
        <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span>

        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>

        <span class="n">train_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">train_total</span> <span class="o">+=</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

    <span class="c1"># 测试模式</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>
            <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span>

            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>

            <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">_</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">test_total</span> <span class="o">+=</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">test_correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

    <span class="c1"># 记录训练过程中的训练精度、测试精度、训练Loss和测试Loss</span>
    <span class="n">train_acc</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="n">train_correct</span> <span class="o">/</span> <span class="n">train_total</span>
    <span class="n">test_acc</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="n">test_correct</span> <span class="o">/</span> <span class="n">test_total</span>
    <span class="n">train_loss</span> <span class="o">/=</span> <span class="n">train_total</span>
    <span class="n">test_loss</span> <span class="o">/=</span> <span class="n">test_total</span>
    <span class="n">train_acc_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_acc</span><span class="p">)</span>
    <span class="n">test_acc_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">test_acc</span><span class="p">)</span>
    <span class="n">train_loss_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span>
    <span class="n">test_loss_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)</span>

    <span class="c1"># 输出训练过程中的训练精度、测试精度、训练Loss和测试Loss</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch [</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">], Train Loss: </span><span class="si">{:.4f}</span><span class="s1">, Train Acc: </span><span class="si">{:.2f}</span><span class="s1">%, Test Loss: </span><span class="si">{:.4f}</span><span class="s1">, Test Acc: </span><span class="si">{:.2f}</span><span class="s1">%&#39;</span>
          <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_epochs</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">train_acc</span><span class="p">,</span> <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="c1"># 保存精度最高的模型</span>
    <span class="k">if</span> <span class="n">test_acc</span> <span class="o">&gt;</span> <span class="n">best_test_acc</span><span class="p">:</span>
        <span class="n">best_test_acc</span> <span class="o">=</span> <span class="n">test_acc</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s1">&#39;best-net.pth&#39;</span><span class="p">)</span>

        <span class="c1"># 输出最高精度</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Best Test Accuracy: </span><span class="si">{:.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_test_acc</span><span class="p">))</span>
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<pre>Epoch [1/30], Train Loss: 0.1356, Train Acc: 95.78%, Test Loss: 0.0456, Test Acc: 98.45%
Epoch [2/30], Train Loss: 0.0403, Train Acc: 98.76%, Test Loss: 0.0309, Test Acc: 98.83%
Epoch [3/30], Train Loss: 0.0262, Train Acc: 99.22%, Test Loss: 0.0354, Test Acc: 98.74%
Epoch [4/30], Train Loss: 0.0189, Train Acc: 99.42%, Test Loss: 0.0280, Test Acc: 99.11%
Epoch [5/30], Train Loss: 0.0133, Train Acc: 99.58%, Test Loss: 0.0270, Test Acc: 99.20%
Epoch [6/30], Train Loss: 0.0098, Train Acc: 99.69%, Test Loss: 0.0210, Test Acc: 99.30%
Epoch [7/30], Train Loss: 0.0071, Train Acc: 99.77%, Test Loss: 0.0282, Test Acc: 99.10%
Epoch [8/30], Train Loss: 0.0057, Train Acc: 99.81%, Test Loss: 0.0288, Test Acc: 99.21%
Epoch [9/30], Train Loss: 0.0043, Train Acc: 99.87%, Test Loss: 0.0281, Test Acc: 99.13%
Epoch [10/30], Train Loss: 0.0037, Train Acc: 99.89%, Test Loss: 0.0270, Test Acc: 99.29%
Epoch [11/30], Train Loss: 0.0023, Train Acc: 99.92%, Test Loss: 0.0270, Test Acc: 99.32%
Epoch [12/30], Train Loss: 0.0014, Train Acc: 99.97%, Test Loss: 0.0280, Test Acc: 99.32%
Epoch [13/30], Train Loss: 0.0014, Train Acc: 99.96%, Test Loss: 0.0300, Test Acc: 99.17%
Epoch [14/30], Train Loss: 0.0010, Train Acc: 99.97%, Test Loss: 0.0297, Test Acc: 99.24%
Epoch [15/30], Train Loss: 0.0009, Train Acc: 99.97%, Test Loss: 0.0262, Test Acc: 99.34%
Epoch [16/30], Train Loss: 0.0001, Train Acc: 100.00%, Test Loss: 0.0265, Test Acc: 99.35%
Epoch [17/30], Train Loss: 0.0001, Train Acc: 100.00%, Test Loss: 0.0276, Test Acc: 99.34%
Epoch [18/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0279, Test Acc: 99.35%
Epoch [19/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0284, Test Acc: 99.34%
Epoch [20/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0289, Test Acc: 99.35%
Epoch [21/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0291, Test Acc: 99.34%
Epoch [22/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0294, Test Acc: 99.35%
Epoch [23/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0297, Test Acc: 99.35%
Epoch [24/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0299, Test Acc: 99.35%
Epoch [25/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0300, Test Acc: 99.35%
Epoch [26/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0302, Test Acc: 99.34%
Epoch [27/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0303, Test Acc: 99.34%
Epoch [28/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0306, Test Acc: 99.36%
Epoch [29/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0306, Test Acc: 99.34%
Epoch [30/30], Train Loss: 0.0000, Train Acc: 100.00%, Test Loss: 0.0307, Test Acc: 99.34%
Best Test Accuracy: 99.36%
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<h2 id="&#21487;&#35270;&#21270;&#35757;&#32451;&#32467;&#26524;">&#21487;&#35270;&#21270;&#35757;&#32451;&#32467;&#26524;<a class="anchor-link" href="#&#21487;&#35270;&#21270;&#35757;&#32451;&#32467;&#26524;">&#182;</a></h2><ol>
<li>figure1(训练精度Acc变化、测试精度Acc变化)</li>
<li>figure2（训练损失Loss变化、测试损失Loss变化）</li>
<li>模型的最高精度</li>
<li>保存best-net  </li>
<li>best-net在验证集上的表现（对于0123456789的识别情况 或者输出一个 混淆矩阵）</li>
<li>输入图片，加载best-net进行预测</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import matplotlib.pyplot as plt</span>

<span class="c1"># # 绘制训练精度和测试精度</span>
<span class="c1"># plt.plot(train_acc_list, label=&#39;Train Accuracy&#39;)</span>
<span class="c1"># plt.plot(test_acc_list, label=&#39;Test Accuracy&#39;)</span>
<span class="c1"># plt.legend()</span>

<span class="c1"># # 显示图形</span>
<span class="c1"># plt.show()</span>
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<h2 id="train_acc&#12289;test_acc&#26354;&#32447;">train_acc&#12289;test_acc&#26354;&#32447;<a class="anchor-link" href="#train_acc&#12289;test_acc&#26354;&#32447;">&#182;</a></h2>
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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="c1"># 绘制训练精度和测试精度</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">train_acc_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train Accuracy&#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">test_acc_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test Accuracy&#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="c1"># 显示图形</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="train_loss&#12289;test_loss&#26354;&#32447;">train_loss&#12289;test_loss&#26354;&#32447;<a class="anchor-link" href="#train_loss&#12289;test_loss&#26354;&#32447;">&#182;</a></h2>
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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="c1"># 绘制训练精度和测试精度</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">train_loss_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train 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">test_loss_list</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test Loss&#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="c1"># 显示图形</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 直接输出best-net</span>
<span class="c1"># 加载保存的最佳模型</span>
<span class="n">best_model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">best_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./best-net.pth&#39;</span><span class="p">))</span>

<span class="c1"># 测试模式</span>
<span class="n">best_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

<span class="c1"># 初始化各种变量</span>
<span class="n">num_correct</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">num_total</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># 遍历验证集，记录每种数字的识别情况</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
    <span class="k">for</span> <span class="n">image</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
        <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
            <span class="k">if</span> <span class="n">predicted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
                <span class="n">num_correct</span><span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>
            <span class="n">num_total</span><span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>

            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">10</span><span class="p">:</span>
                <span class="n">images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
                <span class="n">labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

<span class="c1"># 可视化每种数字的识别情况</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">row</span> <span class="o">=</span> <span class="n">i</span> <span class="o">//</span> <span class="mi">5</span>
    <span class="n">col</span> <span class="o">=</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">5</span>

    <span class="n">axs</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">, Accuracy: </span><span class="si">{:.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mf">100.0</span><span class="o">*</span><span class="n">num_correct</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="n">num_total</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 以混淆矩阵的形式表示best-net</span>
<span class="c1"># 加载保存的最佳模型</span>
<span class="n">best_model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">best_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./best-net.pth&#39;</span><span class="p">))</span>

<span class="c1"># 测试模式</span>
<span class="n">best_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

<span class="c1"># 初始化混淆矩阵</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

<span class="c1"># 遍历验证集，记录混淆矩阵</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
    <span class="k">for</span> <span class="n">image</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">test_loader</span><span class="p">:</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
        <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
            <span class="n">confusion_matrix</span><span class="p">[</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">predicted</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">1</span>

<span class="c1"># 可视化混淆矩阵</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">)</span>

<span class="c1"># 添加轴标签和刻度</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([</span><span class="s1">&#39;0&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;4&#39;</span><span class="p">,</span> <span class="s1">&#39;5&#39;</span><span class="p">,</span> <span class="s1">&#39;6&#39;</span><span class="p">,</span> <span class="s1">&#39;7&#39;</span><span class="p">,</span> <span class="s1">&#39;8&#39;</span><span class="p">,</span> <span class="s1">&#39;9&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="s1">&#39;0&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;4&#39;</span><span class="p">,</span> <span class="s1">&#39;5&#39;</span><span class="p">,</span> <span class="s1">&#39;6&#39;</span><span class="p">,</span> <span class="s1">&#39;7&#39;</span><span class="p">,</span> <span class="s1">&#39;8&#39;</span><span class="p">,</span> <span class="s1">&#39;9&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Predicted Label&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;True Label&#39;</span><span class="p">)</span>

<span class="c1"># 添加标签到每个单元格</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>

<span class="c1"># 添加标题</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Confusion Matrix&#39;</span><span class="p">)</span>

<span class="c1"># 添加颜色条</span>
<span class="n">cbar</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">cbar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Number of Samples&#39;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=-</span><span class="mi">90</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#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>locator: &lt;matplotlib.ticker.AutoLocator object at 0x000001FC025ABF40&gt;
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<h2 id="&#27169;&#22411;&#27979;&#35797;">&#27169;&#22411;&#27979;&#35797;<a class="anchor-link" href="#&#27169;&#22411;&#27979;&#35797;">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 加载保存的最佳模型</span>
<span class="n">best_model</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">best_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;./best-net.pth&#39;</span><span class="p">))</span>


<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>

<span class="n">image</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="s1">&#39;./write-num/55.png&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;L&#39;</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="c1"># 显示一下这张图片</span>
<span class="n">image</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="c1"># 与处理方法跟训练时候的一样</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>  <span class="c1"># 将图像转换为张量</span>
    <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))</span>  <span class="c1"># 归一化图像张量</span>
<span class="p">])</span>
<span class="n">image_tensor</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

<span class="n">output</span> <span class="o">=</span> <span class="n">best_model</span><span class="p">(</span><span class="n">image_tensor</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The predicted digit is: </span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">predicted</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
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<pre>STREAM b&#39;IHDR&#39; 16 13
STREAM b&#39;IDAT&#39; 41 8192
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<pre>The predicted digit is: 5
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<div class=" highlight hl-ipython3"><pre><span></span> 
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<h2 id="&#31616;&#21333;&#30340;&#27979;&#35797;">&#31616;&#21333;&#30340;&#27979;&#35797;<a class="anchor-link" href="#&#31616;&#21333;&#30340;&#27979;&#35797;">&#182;</a></h2><ol>
<li>搭建好模型之后（或者模型中某层结构的参数无法确定）</li>
<li>查看模型某个层结构的输出  -&gt; 确定后续层结构的参数</li>
<li>检查模型结构、参数</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># MNIST_Net</span>
<span class="k">class</span> <span class="nc">MNIST_Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">MNIST_Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">64</span><span class="o">*</span><span class="mi">7</span><span class="o">*</span><span class="mi">7</span><span class="p">,</span> <span class="mi">160</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">160</span><span class="p">,</span> <span class="mi">80</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">80</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个六层卷积结构的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个最大池化层的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第二个十二层卷积结构的输出shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第二个最大池化层的输出-展平处理前的shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;第一个线性层输入-展平处理后的shape:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>
    <span class="c1"># 为什么定义forward而不定义backend   --  PyTorch内置了自动求导Autograd</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[58]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MNIST_Net</span><span class="p">()</span>

<span class="c1"># 打印模型的定义</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>

<span class="c1"># 打印每个卷积层的权重张量的形状</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
    <span class="k">if</span> <span class="s1">&#39;conv&#39;</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>MNIST_Net(
  (conv1): Sequential(
    (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ReLU()
    (6): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU()
    (8): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU()
    (10): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU()
  )
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Sequential(
    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ReLU()
    (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU()
    (8): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU()
    (10): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU()
    (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU()
    (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU()
    (16): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (17): ReLU()
    (18): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (19): ReLU()
    (20): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (21): ReLU()
    (22): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (23): ReLU()
  )
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (linear1): Linear(in_features=3136, out_features=160, bias=True)
  (linear2): Linear(in_features=160, out_features=80, bias=True)
  (classifier): Linear(in_features=80, out_features=10, bias=True)
)
conv1.0.weight torch.Size([16, 1, 3, 3])
conv1.0.bias torch.Size([16])
conv1.2.weight torch.Size([16, 16, 3, 3])
conv1.2.bias torch.Size([16])
conv1.4.weight torch.Size([16, 16, 3, 3])
conv1.4.bias torch.Size([16])
conv1.6.weight torch.Size([32, 16, 3, 3])
conv1.6.bias torch.Size([32])
conv1.8.weight torch.Size([32, 32, 3, 3])
conv1.8.bias torch.Size([32])
conv1.10.weight torch.Size([32, 32, 3, 3])
conv1.10.bias torch.Size([32])
conv2.0.weight torch.Size([64, 32, 3, 3])
conv2.0.bias torch.Size([64])
conv2.2.weight torch.Size([64, 64, 3, 3])
conv2.2.bias torch.Size([64])
conv2.4.weight torch.Size([64, 64, 3, 3])
conv2.4.bias torch.Size([64])
conv2.6.weight torch.Size([64, 64, 3, 3])
conv2.6.bias torch.Size([64])
conv2.8.weight torch.Size([64, 64, 3, 3])
conv2.8.bias torch.Size([64])
conv2.10.weight torch.Size([64, 64, 3, 3])
conv2.10.bias torch.Size([64])
conv2.12.weight torch.Size([64, 64, 3, 3])
conv2.12.bias torch.Size([64])
conv2.14.weight torch.Size([64, 64, 3, 3])
conv2.14.bias torch.Size([64])
conv2.16.weight torch.Size([64, 64, 3, 3])
conv2.16.bias torch.Size([64])
conv2.18.weight torch.Size([64, 64, 3, 3])
conv2.18.bias torch.Size([64])
conv2.20.weight torch.Size([64, 64, 3, 3])
conv2.20.bias torch.Size([64])
conv2.22.weight torch.Size([64, 64, 3, 3])
conv2.22.bias torch.Size([64])
</pre>
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<div class="prompt input_prompt">In&nbsp;[60]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">input_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MNIST_Net</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span>
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<pre>第一个六层卷积结构的输出shape:torch.Size([1, 32, 28, 28])
第一个最大池化层的输出shape:torch.Size([1, 32, 14, 14])
第二个十二层卷积结构的输出shape:torch.Size([1, 64, 14, 14])
第二个最大池化层的输出-展平处理前的shape:torch.Size([1, 64, 7, 7])
第一个线性层输入-展平处理后的shape:torch.Size([1, 3136])
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