MLE_ND_P5_V0_S2.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>MLE_ND_P5_V0_S2</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
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}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Deep-Learning">Deep Learning<a class="anchor-link" href="#Deep-Learning">&#182;</a></h2><h1>&#x1F4D1; &nbsp;  P5: Build a Digit Recognition Program. Step 2</h1>

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<h2 id="Code-Library-and-Links">Code Library and Links<a class="anchor-link" href="#Code-Library-and-Links">&#182;</a></h2><p><a href="https://docs.scipy.org/doc/scipy/reference/ndimage.html">Multi-dimensional image processing</a></p>
<p><a href="https://keras.io/">Keras: Deep Learning library for Theano and TensorFlow</a></p>
<p><a href="https://www.tensorflow.org/get_started/mnist/pros">Deep MNIST for Experts</a></p>
<p><a href="http://docs.seldon.io/tensorflow-deep-mnist-example.html">Tensorflow Deep MNIST Advanced Tutorial</a></p>
<p><a href="http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/">Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras</a></p>
<p><a href="http://www.aimechanic.com/2016/09/22/d221-svhn-tensorflow-deep-learning/">d221: SVHN TensorFlow examples and source code</a></p>

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<div class="prompt input_prompt">In&nbsp;[2]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">html</span>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">scipy.ndimage</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">scipy.misc</span>
<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="k">import</span> <span class="n">expit</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>

<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">h5py</span>
<span class="kn">import</span> <span class="nn">tarfile</span>

<span class="kn">from</span> <span class="nn">six.moves.urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
<span class="kn">from</span> <span class="nn">six.moves</span> <span class="k">import</span> <span class="n">cPickle</span> <span class="k">as</span> <span class="n">pickle</span>

<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Image</span><span class="p">,</span> <span class="n">IFrame</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">offsetbox</span>

<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
</pre></div>

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</div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="k">import</span> <span class="n">MLPClassifier</span><span class="p">,</span> <span class="n">BernoulliRBM</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">linear_model</span><span class="p">,</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">metrics</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="k">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">manifold</span><span class="p">,</span> <span class="n">decomposition</span><span class="p">,</span> <span class="n">ensemble</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">discriminant_analysis</span><span class="p">,</span> <span class="n">random_projection</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">tensorflow.examples.tutorials.mnist</span> <span class="k">as</span> <span class="nn">mnist</span>

<span class="kn">import</span> <span class="nn">keras</span> <span class="k">as</span> <span class="nn">ks</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">load_model</span>
<span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">sequence</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="k">import</span> <span class="n">SGD</span><span class="p">,</span> <span class="n">RMSprop</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">LSTM</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Input</span><span class="p">,</span> <span class="n">BatchNormalization</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv1D</span><span class="p">,</span> <span class="n">MaxPooling1D</span><span class="p">,</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.layers.embeddings</span> <span class="k">import</span> <span class="n">Embedding</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>
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<pre>Using TensorFlow backend.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">download_progress_hook</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">blockSize</span><span class="p">,</span> <span class="n">totalSize</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;A hook to report the progress of a download. This is mostly intended for users with</span>
<span class="sd">    slow internet connections. Reports every 5% change in download progress.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">global</span> <span class="n">last_percent_reported</span>
    <span class="n">percent</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">count</span> <span class="o">*</span> <span class="n">blockSize</span> <span class="o">*</span> <span class="mi">100</span> <span class="o">/</span> <span class="n">totalSize</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">last_percent_reported</span> <span class="o">!=</span> <span class="n">percent</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">percent</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">percent</span><span class="p">)</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
            <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
      
        <span class="n">last_percent_reported</span> <span class="o">=</span> <span class="n">percent</span>
        
<span class="k">def</span> <span class="nf">maybe_download</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">expected_bytes</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Download a file if not present, and make sure it&#39;s the right size.&quot;&quot;&quot;</span>
    <span class="n">dest_filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_root</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">force</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dest_filename</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Attempting to download:&#39;</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span> 
        <span class="n">filename</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span> <span class="o">+</span> <span class="n">filename</span><span class="p">,</span> <span class="n">dest_filename</span><span class="p">,</span> <span class="n">reporthook</span><span class="o">=</span><span class="n">download_progress_hook</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Download Complete!&#39;</span><span class="p">)</span>
    <span class="n">statinfo</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">stat</span><span class="p">(</span><span class="n">dest_filename</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">statinfo</span><span class="o">.</span><span class="n">st_size</span> <span class="o">==</span> <span class="n">expected_bytes</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Found and verified&#39;</span><span class="p">,</span> <span class="n">dest_filename</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Failed to verify &#39;</span> <span class="o">+</span> <span class="n">dest_filename</span> <span class="o">+</span> <span class="s1">&#39;. Can you get to it with a browser?&#39;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">dest_filename</span>

<span class="k">def</span> <span class="nf">maybe_extract</span><span class="p">(</span><span class="n">file_</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
    <span class="n">filename</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">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">file_</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="c1"># remove .tar.gz</span>
    
    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force</span><span class="p">:</span>
        <span class="c1"># You may override by setting force=True.</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%s</span><span class="s1"> is already presented - Skipping extraction of </span><span class="si">%s</span><span class="s1">.&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">file_</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Extracting </span><span class="si">%s</span><span class="s1"> file data. Please wait...&#39;</span> <span class="o">%</span> <span class="n">file_</span><span class="p">)</span>
        <span class="n">tar</span> <span class="o">=</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">file_</span><span class="p">)</span>
        <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
        <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
        <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;File </span><span class="si">%s</span><span class="s1"> is successfully extracted into </span><span class="si">%s</span><span class="s1"> directory.&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">file_</span><span class="p">,</span> <span class="n">filename</span><span class="p">))</span>        
    
    <span class="k">return</span> <span class="n">filename</span>
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<span class="c1"># https://github.com/sarahrn/Py-Gsvhn-DigitStruct-Reader/blob/master/digitStruct.py</span>
<span class="c1">#################</span>
<span class="c1"># Bounding Box #</span>
<span class="c1">################</span>
<span class="k">class</span> <span class="nc">BBox</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>     <span class="c1"># Digit</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">top</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="mi">0</span>

<span class="k">class</span> <span class="nc">DigitStruct</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="kc">None</span>    <span class="c1"># Image file name</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bboxList</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># List of BBox structs</span>

<span class="c1"># Function for debugging</span>
<span class="k">def</span> <span class="nf">printHDFObj</span><span class="p">(</span><span class="n">theObj</span><span class="p">,</span> <span class="n">theObjName</span><span class="p">):</span>
    <span class="n">isFile</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">theObj</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">)</span>
    <span class="n">isGroup</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">theObj</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">Group</span><span class="p">)</span>
    <span class="n">isDataSet</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">theObj</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">Dataset</span><span class="p">)</span>
    <span class="n">isReference</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">theObj</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">Reference</span><span class="p">)</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;</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">theObjName</span><span class="p">))</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    type(): </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="nb">type</span><span class="p">(</span><span class="n">theObj</span><span class="p">)))</span>
    <span class="k">if</span> <span class="n">isFile</span> <span class="ow">or</span> <span class="n">isGroup</span> <span class="ow">or</span> <span class="n">isDataSet</span><span class="p">:</span>
        <span class="c1"># if theObj.name != None:</span>
        <span class="c1">#    print &quot;    name: {}&quot;.format(theObj.name)</span>
        <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    id: </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">theObj</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
    <span class="k">if</span> <span class="n">isFile</span> <span class="ow">or</span> <span class="n">isGroup</span><span class="p">:</span>
        <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    keys: </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">theObj</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">isReference</span><span class="p">:</span>
        <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    Len: </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="nb">len</span><span class="p">(</span><span class="n">theObj</span><span class="p">)))</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">isFile</span> <span class="ow">or</span> <span class="n">isGroup</span> <span class="ow">or</span> <span class="n">isDataSet</span> <span class="ow">or</span> <span class="n">isReference</span><span class="p">):</span>
        <span class="nb">print</span> <span class="p">(</span><span class="n">theObj</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">readDigitStructGroup</span><span class="p">(</span><span class="n">dsFile</span><span class="p">):</span>
    <span class="n">dsGroup</span> <span class="o">=</span> <span class="n">dsFile</span><span class="p">[</span><span class="s2">&quot;digitStruct&quot;</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">dsGroup</span>

<span class="c1">#####################################################</span>
<span class="c1"># Reads a string from the file using its reference #</span>
<span class="c1">####################################################</span>
<span class="k">def</span> <span class="nf">readString</span><span class="p">(</span><span class="n">strRef</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
    <span class="n">strObj</span> <span class="o">=</span> <span class="n">dsFile</span><span class="p">[</span><span class="n">strRef</span><span class="p">]</span>
    <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">chr</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">strObj</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">str</span>
<span class="c1">#########################################</span>
<span class="c1"># Reads an integer value from the file #</span>
<span class="c1">########################################</span>
<span class="k">def</span> <span class="nf">readInt</span><span class="p">(</span><span class="n">intArray</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
    <span class="n">intRef</span> <span class="o">=</span> <span class="n">intArray</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">isReference</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">intRef</span><span class="p">,</span> <span class="n">h5py</span><span class="o">.</span><span class="n">Reference</span><span class="p">)</span>
    <span class="n">intVal</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">if</span> <span class="n">isReference</span><span class="p">:</span>
        <span class="n">intObj</span> <span class="o">=</span> <span class="n">dsFile</span><span class="p">[</span><span class="n">intRef</span><span class="p">]</span>
        <span class="n">intVal</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">intObj</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="k">else</span><span class="p">:</span> <span class="c1"># Assuming value type</span>
        <span class="n">intVal</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">intRef</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">intVal</span>

<span class="k">def</span> <span class="nf">yieldNextInt</span><span class="p">(</span><span class="n">intDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">intData</span> <span class="ow">in</span> <span class="n">intDataset</span><span class="p">:</span>
        <span class="n">intVal</span> <span class="o">=</span> <span class="n">readInt</span><span class="p">(</span><span class="n">intData</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
        <span class="k">yield</span> <span class="n">intVal</span> 

<span class="k">def</span> <span class="nf">yieldNextBBox</span><span class="p">(</span><span class="n">bboxDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">bboxArray</span> <span class="ow">in</span> <span class="n">bboxDataset</span><span class="p">:</span>
        <span class="n">bboxGroupRef</span> <span class="o">=</span> <span class="n">bboxArray</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">bboxGroup</span> <span class="o">=</span> <span class="n">dsFile</span><span class="p">[</span><span class="n">bboxGroupRef</span><span class="p">]</span>
        <span class="n">labelDataset</span> <span class="o">=</span> <span class="n">bboxGroup</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
        <span class="n">leftDataset</span> <span class="o">=</span> <span class="n">bboxGroup</span><span class="p">[</span><span class="s2">&quot;left&quot;</span><span class="p">]</span>
        <span class="n">topDataset</span> <span class="o">=</span> <span class="n">bboxGroup</span><span class="p">[</span><span class="s2">&quot;top&quot;</span><span class="p">]</span>
        <span class="n">widthDataset</span> <span class="o">=</span> <span class="n">bboxGroup</span><span class="p">[</span><span class="s2">&quot;width&quot;</span><span class="p">]</span>
        <span class="n">heightDataset</span> <span class="o">=</span> <span class="n">bboxGroup</span><span class="p">[</span><span class="s2">&quot;height&quot;</span><span class="p">]</span>

        <span class="n">left</span> <span class="o">=</span> <span class="n">yieldNextInt</span><span class="p">(</span><span class="n">leftDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
        <span class="n">top</span> <span class="o">=</span> <span class="n">yieldNextInt</span><span class="p">(</span><span class="n">topDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
        <span class="n">width</span> <span class="o">=</span> <span class="n">yieldNextInt</span><span class="p">(</span><span class="n">widthDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
        <span class="n">height</span> <span class="o">=</span> <span class="n">yieldNextInt</span><span class="p">(</span><span class="n">heightDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>

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

        <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">yieldNextInt</span><span class="p">(</span><span class="n">labelDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
            <span class="n">bbox</span> <span class="o">=</span> <span class="n">BBox</span><span class="p">()</span>
            <span class="n">bbox</span><span class="o">.</span><span class="n">label</span> <span class="o">=</span> <span class="n">label</span>
            <span class="n">bbox</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">left</span><span class="p">)</span>
            <span class="n">bbox</span><span class="o">.</span><span class="n">top</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">top</span><span class="p">)</span>
            <span class="n">bbox</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">width</span><span class="p">)</span>
            <span class="n">bbox</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">height</span><span class="p">)</span>
            <span class="n">bboxList</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bbox</span><span class="p">)</span>

        <span class="k">yield</span> <span class="n">bboxList</span>

<span class="k">def</span> <span class="nf">yieldNextFileName</span><span class="p">(</span><span class="n">nameDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">nameArray</span> <span class="ow">in</span> <span class="n">nameDataset</span><span class="p">:</span>
        <span class="n">nameRef</span> <span class="o">=</span> <span class="n">nameArray</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">name</span> <span class="o">=</span> <span class="n">readString</span><span class="p">(</span><span class="n">nameRef</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
        <span class="k">yield</span> <span class="n">name</span>

<span class="k">def</span> <span class="nf">yieldNextDigitStruct</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">):</span>
    <span class="n">dsFile</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
    <span class="n">dsGroup</span> <span class="o">=</span> <span class="n">readDigitStructGroup</span><span class="p">(</span><span class="n">dsFile</span><span class="p">)</span>
    <span class="n">nameDataset</span> <span class="o">=</span> <span class="n">dsGroup</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span>
    <span class="n">bboxDataset</span> <span class="o">=</span> <span class="n">dsGroup</span><span class="p">[</span><span class="s2">&quot;bbox&quot;</span><span class="p">]</span>

    <span class="n">bboxListIter</span> <span class="o">=</span> <span class="n">yieldNextBBox</span><span class="p">(</span><span class="n">bboxDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">yieldNextFileName</span><span class="p">(</span><span class="n">nameDataset</span><span class="p">,</span> <span class="n">dsFile</span><span class="p">):</span>
        <span class="n">bboxList</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">bboxListIter</span><span class="p">)</span>
        <span class="n">obj</span> <span class="o">=</span> <span class="n">DigitStruct</span><span class="p">()</span>
        <span class="n">obj</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
        <span class="n">obj</span><span class="o">.</span><span class="n">bboxList</span> <span class="o">=</span> <span class="n">bboxList</span>
        <span class="k">yield</span> <span class="n">obj</span>
        
<span class="k">def</span> <span class="nf">testMain</span><span class="p">():</span>

    <span class="n">dsFileName</span> <span class="o">=</span> <span class="s1">&#39;train/digitStruct.mat&#39;</span>
    <span class="n">testCounter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">dsObj</span> <span class="ow">in</span> <span class="n">yieldNextDigitStruct</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">):</span>
        <span class="c1"># testCounter += 1</span>
        <span class="nb">print</span> <span class="p">(</span><span class="n">dsObj</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">bbox</span> <span class="ow">in</span> <span class="n">dsObj</span><span class="o">.</span><span class="n">bboxList</span><span class="p">:</span>
            <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    </span><span class="si">{}</span><span class="s2">:</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bbox</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">top</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">height</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">testCounter</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="k">break</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     testMain()</span>
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    <div class="input_area">
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<span class="k">def</span> <span class="nf">testMain</span><span class="p">():</span>

    <span class="n">dsFileName</span> <span class="o">=</span> <span class="s1">&#39;test/digitStruct.mat&#39;</span>
    <span class="n">testCounter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">dsObj</span> <span class="ow">in</span> <span class="n">yieldNextDigitStruct</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">):</span>
        <span class="c1"># testCounter += 1</span>
        <span class="nb">print</span> <span class="p">(</span><span class="n">dsObj</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">bbox</span> <span class="ow">in</span> <span class="n">dsObj</span><span class="o">.</span><span class="n">bboxList</span><span class="p">:</span>
            <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    </span><span class="si">{}</span><span class="s2">:</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bbox</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">top</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">height</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">testCounter</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="k">break</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     testMain()</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">testMain</span><span class="p">():</span>

    <span class="n">dsFileName</span> <span class="o">=</span> <span class="s1">&#39;extra/digitStruct.mat&#39;</span>
    <span class="n">testCounter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">dsObj</span> <span class="ow">in</span> <span class="n">yieldNextDigitStruct</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">):</span>
        <span class="c1"># testCounter += 1</span>
        <span class="nb">print</span> <span class="p">(</span><span class="n">dsObj</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">bbox</span> <span class="ow">in</span> <span class="n">dsObj</span><span class="o">.</span><span class="n">bboxList</span><span class="p">:</span>
            <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;    </span><span class="si">{}</span><span class="s2">:</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bbox</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">top</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">height</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">testCounter</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">:</span>
            <span class="k">break</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     testMain()</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="c1"># https://github.com/sarahrn/Py-Gsvhn-DigitStruct-Reader/blob/master/digitStructMatToCsv.py</span>
<span class="k">def</span> <span class="nf">writeToCsvFile</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">,</span> <span class="n">csvFile</span><span class="p">):</span>
    <span class="n">fileCount</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">csvFile</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;FileName,DigitLabel,Left,Top,Width,Height</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">dsObj</span> <span class="ow">in</span> <span class="n">yieldNextDigitStruct</span><span class="p">(</span><span class="n">dsFileName</span><span class="p">):</span>
        <span class="n">fileCount</span> <span class="o">+=</span> <span class="mi">1</span>
        <span class="k">for</span> <span class="n">bbox</span> <span class="ow">in</span> <span class="n">dsObj</span><span class="o">.</span><span class="n">bboxList</span><span class="p">:</span>
            <span class="n">csvLine</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                <span class="n">dsObj</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
                <span class="n">bbox</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">top</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="n">bbox</span><span class="o">.</span><span class="n">height</span><span class="p">)</span>
            <span class="n">csvFile</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">csvLine</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of image files: </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">fileCount</span><span class="p">))</span>

<span class="k">def</span> <span class="nf">convertToCsv</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">):</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">csvFilePath</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">csvFile</span><span class="p">:</span>
        <span class="n">writeToCsvFile</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFile</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
    <span class="n">dsFilePath</span> <span class="o">=</span> <span class="s1">&#39;train/digitStruct.mat&#39;</span>
    <span class="n">csvFilePath</span> <span class="o">=</span> <span class="s1">&#39;train_digitStruct.csv&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converting </span><span class="si">{0}</span><span class="s2"> to </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">))</span>
    <span class="n">convertToCsv</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">)</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     main()</span>
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
    <span class="n">dsFilePath</span> <span class="o">=</span> <span class="s1">&#39;test/digitStruct.mat&#39;</span>
    <span class="n">csvFilePath</span> <span class="o">=</span> <span class="s1">&#39;test_digitStruct.csv&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converting </span><span class="si">{0}</span><span class="s2"> to </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">))</span>
    <span class="n">convertToCsv</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">)</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     main()</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
    <span class="n">dsFilePath</span> <span class="o">=</span> <span class="s1">&#39;extra/digitStruct.mat&#39;</span>
    <span class="n">csvFilePath</span> <span class="o">=</span> <span class="s1">&#39;extra_digitStruct.csv&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converting </span><span class="si">{0}</span><span class="s2"> to </span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">))</span>
    <span class="n">convertToCsv</span><span class="p">(</span><span class="n">dsFilePath</span><span class="p">,</span> <span class="n">csvFilePath</span><span class="p">)</span>

<span class="c1"># if __name__ == &quot;__main__&quot;:</span>
<span class="c1">#     main()</span>
</pre></div>

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</div>
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<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
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<span class="k">def</span> <span class="nf">grouped_by_filename</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>    
    <span class="n">filename_set</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;filename&#39;</span><span class="p">])))</span>
    <span class="n">n</span> <span class="o">=</span> <span class="n">filename_set</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">boxes</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">data_gr</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;filename&#39;</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">n</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">filename_set</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data_gr</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="s1">&#39;label&#39;</span><span class="p">]))</span>
        <span class="n">boxes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data_gr</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="s1">&#39;box&#39;</span><span class="p">]))</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
    <span class="n">boxes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">boxes</span><span class="p">)</span>
    <span class="n">data_g</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="n">filename_set</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">labels</span><span class="p">,</span> <span class="s1">&#39;box&#39;</span><span class="p">:</span> <span class="n">boxes</span><span class="p">}</span>
    <span class="n">new_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data_g</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">new_data</span>
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<span class="k">def</span> <span class="nf">fivedigit_label</span><span class="p">(</span><span class="n">label</span><span class="p">):</span>
    <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">size</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">label</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">num_zeros</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">5</span><span class="o">-</span><span class="n">size</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">num_zeros</span><span class="p">,</span> <span class="n">label</span><span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">))</span>
    
<span class="k">def</span> <span class="nf">get_labels</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
    <span class="n">string</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;[&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;]&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">string</span><span class="o">.</span><span class="n">split</span><span class="p">()])</span>
    <span class="k">return</span> <span class="n">labels</span>

<span class="k">def</span> <span class="nf">image_box</span><span class="p">(</span><span class="n">box</span><span class="p">):</span>
        <span class="n">left</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">box</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">box</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">box</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">box</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
        
        <span class="n">image_left</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">left</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
        <span class="n">image_top</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">top</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
        <span class="n">image_bottom</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">top</span><span class="p">)</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="n">height</span><span class="p">)</span>
        <span class="n">image_right</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">left</span><span class="p">)</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="n">width</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">image_left</span><span class="p">,</span> <span class="n">image_right</span><span class="p">,</span> <span class="n">image_top</span><span class="p">,</span> <span class="n">image_bottom</span><span class="p">])</span>

<span class="k">def</span> <span class="nf">get_box</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
    <span class="n">value</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">string</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;[&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;]&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
    <span class="n">numbers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">string</span><span class="o">.</span><span class="n">split</span><span class="p">()])</span>        
    <span class="n">k</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">numbers</span><span class="p">)</span><span class="o">/</span><span class="mi">4</span>
    <span class="n">numbers</span> <span class="o">=</span> <span class="n">numbers</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>       
    <span class="k">return</span> <span class="n">numbers</span>

<span class="k">def</span> <span class="nf">get_image</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">image_file</span><span class="p">):</span>
    <span class="n">filename</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">image_file</span><span class="p">)</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">ndimage</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">folder</span> <span class="o">==</span> <span class="s1">&#39;train&#39;</span><span class="p">:</span>
        <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">train_filenames</span> <span class="o">==</span> <span class="n">image_file</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">box</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">get_box</span><span class="p">(</span><span class="n">train_b</span><span class="p">[</span><span class="s1">&#39;box&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">]))</span>
        <span class="n">label</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
        
    <span class="k">elif</span> <span class="n">folder</span> <span class="o">==</span> <span class="s1">&#39;test&#39;</span><span class="p">:</span>
        <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">test_filenames</span> <span class="o">==</span> <span class="n">image_file</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">box</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">get_box</span><span class="p">(</span><span class="n">test_b</span><span class="p">[</span><span class="s1">&#39;box&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">]))</span>
        <span class="n">label</span> <span class="o">=</span> <span class="n">test_labels</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
        
    <span class="k">elif</span> <span class="n">folder</span> <span class="o">==</span> <span class="s1">&#39;extra&#39;</span><span class="p">:</span>
        <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">extra_filenames</span> <span class="o">==</span> <span class="n">image_file</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">box</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">get_box</span><span class="p">(</span><span class="n">extra_b</span><span class="p">[</span><span class="s1">&#39;box&#39;</span><span class="p">][</span><span class="n">n</span><span class="p">]))</span>
        <span class="n">label</span> <span class="o">=</span> <span class="n">extra_labels</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
        
    <span class="n">nbox</span> <span class="o">=</span> <span class="n">image_box</span><span class="p">(</span><span class="n">box</span><span class="p">)</span>
    <span class="n">image_left</span><span class="p">,</span> <span class="n">image_right</span><span class="p">,</span> <span class="n">image_top</span><span class="p">,</span> <span class="n">image_bottom</span> <span class="o">=</span> <span class="n">nbox</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">nbox</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">nbox</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
    
    <span class="n">image_bottom</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_bottom</span><span class="p">)</span>
    <span class="n">image_right</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">image_right</span><span class="p">)</span>
                   
    <span class="n">nimage</span> <span class="o">=</span> <span class="n">image</span><span class="p">[</span><span class="n">image_top</span><span class="p">:</span><span class="n">image_bottom</span><span class="p">,</span> <span class="n">image_left</span><span class="p">:</span><span class="n">image_right</span><span class="p">]</span>
    
    <span class="n">image32_1</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">nimage</span><span class="p">,</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="o">/</span> <span class="mi">255</span>
    <span class="n">image32_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image64_1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">),</span> <span class="p">[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">image32_1</span><span class="p">,</span> <span class="n">image32_2</span><span class="p">,</span> <span class="n">label</span>
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<h2 id="Experimental-Datasets">Experimental Datasets<a class="anchor-link" href="#Experimental-Datasets">&#182;</a></h2><h3 id="Dataset-#5.-Stanford.edu.-Housenumbers">Dataset #5. Stanford.edu. <a href="http://ufldl.stanford.edu/housenumbers/">Housenumbers</a><a class="anchor-link" href="#Dataset-#5.-Stanford.edu.-Housenumbers">&#182;</a></h3>
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<span class="n">url</span> <span class="o">=</span> <span class="s1">&#39;http://ufldl.stanford.edu/housenumbers/&#39;</span>
<span class="n">last_percent_reported</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">data_root</span> <span class="o">=</span> <span class="s1">&#39;.&#39;</span> <span class="c1"># Change me to store data elsewhere</span>

<span class="n">train_filename</span> <span class="o">=</span> <span class="n">maybe_download</span><span class="p">(</span><span class="s1">&#39;train.tar.gz&#39;</span><span class="p">,</span> <span class="mi">404141560</span><span class="p">)</span>
<span class="n">test_filename</span> <span class="o">=</span> <span class="n">maybe_download</span><span class="p">(</span><span class="s1">&#39;test.tar.gz&#39;</span><span class="p">,</span> <span class="mi">276555967</span><span class="p">)</span>
<span class="n">extra_filename</span> <span class="o">=</span> <span class="n">maybe_download</span><span class="p">(</span><span class="s1">&#39;extra.tar.gz&#39;</span><span class="p">,</span> <span class="mi">1955489752</span><span class="p">)</span>
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<pre>Found and verified ./train.tar.gz
Found and verified ./test.tar.gz
Found and verified ./extra.tar.gz
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<span class="n">train_folder</span> <span class="o">=</span> <span class="n">maybe_extract</span><span class="p">(</span><span class="n">train_filename</span><span class="p">)</span>
<span class="n">test_folder</span> <span class="o">=</span> <span class="n">maybe_extract</span><span class="p">(</span><span class="n">test_filename</span><span class="p">)</span>
<span class="n">extra_folder</span> <span class="o">=</span> <span class="n">maybe_extract</span><span class="p">(</span><span class="n">extra_filename</span><span class="p">)</span>
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<pre>./train is already presented - Skipping extraction of ./train.tar.gz.
./test is already presented - Skipping extraction of ./test.tar.gz.
./extra is already presented - Skipping extraction of ./extra.tar.gz.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;A few examples of images&#39;</span><span class="p">)</span>
<span class="n">image_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">)</span>
<span class="n">images</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">image_path</span><span class="p">)</span> <span class="k">if</span> <span class="n">f</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;.png&#39;</span><span class="p">)][</span><span class="mi">1010</span><span class="p">:</span><span class="mi">1015</span><span class="p">]</span>
<span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Image</span><span class="p">(</span><span class="n">filename</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span><span class="n">image</span><span class="p">)))</span>
</pre></div>

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<div class="output">


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>A few examples of images
</pre>
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<div class="prompt"></div>


<div class="output_png output_subarea ">
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"
>
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<div class="prompt input_prompt">In&nbsp;[21]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_set</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_digitStruct.csv&quot;</span><span class="p">)</span>
<span class="n">test_set</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_digitStruct.csv&quot;</span><span class="p">)</span>
<span class="n">extra_set</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_digitStruct.csv&quot;</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">train_set</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_set</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_set</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
</pre></div>

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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>FileName</th>
      <th>DigitLabel</th>
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      <td>2.png</td>
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      <td>77</td>
      <td>29</td>
      <td>23</td>
      <td>32</td>
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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>FileName</th>
      <th>DigitLabel</th>
      <th>Left</th>
      <th>Top</th>
      <th>Width</th>
      <th>Height</th>
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  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1.png</td>
      <td>5</td>
      <td>43</td>
      <td>7</td>
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      <td>30</td>
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    <tr>
      <th>1</th>
      <td>2.png</td>
      <td>2</td>
      <td>99</td>
      <td>5</td>
      <td>14</td>
      <td>23</td>
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      <th>2</th>
      <td>2.png</td>
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      <td>8</td>
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    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>FileName</th>
      <th>DigitLabel</th>
      <th>Left</th>
      <th>Top</th>
      <th>Width</th>
      <th>Height</th>
    </tr>
  </thead>
  <tbody>
    <tr>
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<div class="prompt input_prompt">In&nbsp;[19]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_box</span> <span class="o">=</span> <span class="p">[[</span><span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;Left&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;Top&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> 
              <span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;Width&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;Height&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">train_set</span><span class="p">))]</span>

<span class="n">test_box</span> <span class="o">=</span> <span class="p">[[</span><span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;Left&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;Top&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> 
             <span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;Width&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;Height&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">test_set</span><span class="p">))]</span>

<span class="n">extra_box</span> <span class="o">=</span> <span class="p">[[</span><span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;Left&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;Top&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> 
              <span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;Width&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;Height&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">extra_set</span><span class="p">))]</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[23]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;FileName&#39;</span><span class="p">],</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">train_set</span><span class="p">[</span><span class="s1">&#39;DigitLabel&#39;</span><span class="p">],</span> <span class="s1">&#39;box&#39;</span><span class="p">:</span> <span class="n">train_box</span><span class="p">}</span>
<span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_d</span><span class="p">)</span>

<span class="n">test_d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;FileName&#39;</span><span class="p">],</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">test_set</span><span class="p">[</span><span class="s1">&#39;DigitLabel&#39;</span><span class="p">],</span> <span class="s1">&#39;box&#39;</span><span class="p">:</span> <span class="n">test_box</span><span class="p">}</span>
<span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_d</span><span class="p">)</span>

<span class="n">extra_d</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;FileName&#39;</span><span class="p">],</span> <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="n">extra_set</span><span class="p">[</span><span class="s1">&#39;DigitLabel&#39;</span><span class="p">],</span> <span class="s1">&#39;box&#39;</span><span class="p">:</span> <span class="n">extra_box</span><span class="p">}</span>
<span class="n">extra_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_d</span><span class="p">)</span>

<span class="n">display</span><span class="p">(</span><span class="n">train_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
</pre></div>

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      <th></th>
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    <tr>
      <th>0</th>
      <td>[246, 77, 81, 219]</td>
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      <td>1.png</td>
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      <td>[77, 29, 23, 32]</td>
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      <td>[43, 7, 19, 30]</td>
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      <td>[24, 70, 38, 56]</td>
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<h4 id="Group-by-file-name">Group by file name<a class="anchor-link" href="#Group-by-file-name">&#182;</a></h4>
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<span class="c1"># train_g = grouped_by_filename(train_df)</span>
<span class="c1"># test_g = grouped_by_filename(test_df)</span>
<span class="c1"># extra_g = grouped_by_filename(extra_df)</span>
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<pre>&#39;&#39;</pre>
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<span class="c1"># train_g.to_csv(&#39;train_group.csv&#39;, index=False)</span>
<span class="c1"># test_g.to_csv(&#39;test_group.csv&#39;, index=False)</span>
<span class="c1"># extra_g.to_csv(&#39;extra_group.csv&#39;, index=False)</span>
<span class="n">display</span><span class="p">(</span><span class="n">train_g</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_g</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_g</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
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      <td>[[33, 10, 12, 24], [45, 10, 11, 24]]</td>
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      <th>1</th>
      <td>[[35, 7, 11, 37], [44, 3, 11, 37]]</td>
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      <th>2</th>
      <td>[[99, 32, 35, 51], [125, 29, 31, 51]]</td>
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      <td>[[135, 40, 10, 22], [146, 38, 16, 22]]</td>
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      <td>[1, 7]</td>
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      <td>[[125, 37, 17, 25], [141, 41, 16, 25]]</td>
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      <td>[[21, 16, 34, 56], [55, 6, 26, 56], [80, 1, 42...</td>
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      <td>[[5, 4, 11, 22], [15, 2, 14, 22]]</td>
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      <td>[5, 3]</td>
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      <th>2</th>
      <td>[[9, 16, 24, 48], [36, 17, 25, 48], [63, 14, 2...</td>
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      <td>[4, 9, 10]</td>
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<span class="n">train_b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_group.csv&quot;</span><span class="p">)</span>
<span class="n">test_b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_group.csv&quot;</span><span class="p">)</span>
<span class="n">extra_b</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_group.csv&quot;</span><span class="p">)</span>

<span class="n">display</span><span class="p">(</span><span class="n">train_b</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_b</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_b</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
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      <td>[[35, 7, 11, 37] [44, 3, 11, 37]]</td>
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      <td>[1 5]</td>
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      <th>2</th>
      <td>[[99, 32, 35, 51] [125, 29, 31, 51]]</td>
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      <td>[2 9]</td>
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      <td>[[35, 10, 15, 22] [51, 9, 12, 22] [63, 10, 12,...</td>
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      <td>[1 7 1]</td>
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      <td>[[135, 40, 10, 22] [146, 38, 16, 22]]</td>
      <td>1166.png</td>
      <td>[1 7]</td>
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      <th>2</th>
      <td>[[125, 37, 17, 25] [141, 41, 16, 25]]</td>
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      <td>[[21, 16, 34, 56] [55, 6, 26, 56] [80, 1, 42, ...</td>
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      <td>[[5, 4, 11, 22] [15, 2, 14, 22]]</td>
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      <td>[5 3]</td>
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      <th>2</th>
      <td>[[9, 16, 24, 48] [36, 17, 25, 48] [63, 14, 29,...</td>
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      <td>[ 4  9 10]</td>
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<h4 id="Check-labels">Check labels<a class="anchor-link" href="#Check-labels">&#182;</a></h4>
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<span class="n">train_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_labels</span><span class="p">(</span><span class="n">train_b</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">train_b</span><span class="p">))])</span>
<span class="n">train_filenames</span> <span class="o">=</span> <span class="n">train_b</span><span class="p">[</span><span class="s1">&#39;filename&#39;</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">array</span><span class="p">([</span><span class="n">get_labels</span><span class="p">(</span><span class="n">test_b</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">test_b</span><span class="p">))])</span>
<span class="n">test_filenames</span> <span class="o">=</span> <span class="n">test_b</span><span class="p">[</span><span class="s1">&#39;filename&#39;</span><span class="p">]</span>

<span class="n">extra_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_labels</span><span class="p">(</span><span class="n">extra_b</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">][</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">extra_b</span><span class="p">))])</span>
<span class="n">extra_filenames</span> <span class="o">=</span> <span class="n">extra_b</span><span class="p">[</span><span class="s1">&#39;filename&#39;</span><span class="p">]</span>
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<span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">&gt;</span><span class="mi">5</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="nb">len</span><span class="p">(</span><span class="n">train_b</span><span class="p">))])[</span><span class="mi">0</span><span class="p">]</span>
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<pre>array([29190])</pre>
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<span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">test_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">&gt;</span><span class="mi">5</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="nb">len</span><span class="p">(</span><span class="n">test_b</span><span class="p">))])[</span><span class="mi">0</span><span class="p">]</span>
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<pre>array([], dtype=int64)</pre>
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<span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">&gt;</span><span class="mi">5</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="nb">len</span><span class="p">(</span><span class="n">extra_b</span><span class="p">))])[</span><span class="mi">0</span><span class="p">]</span>
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<pre>array([], dtype=int64)</pre>
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<span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">==</span><span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">train_b</span><span class="p">))]))</span>
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<pre>0</pre>
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<span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">test_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">==</span><span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">test_b</span><span class="p">))]))</span>
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<pre>0</pre>
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<span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">==</span><span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">extra_b</span><span class="p">))]))</span>
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<pre>0</pre>
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<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="nb">len</span><span class="p">(</span><span class="n">train_labels</span><span class="p">)):</span>
    <span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">9</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">test_labels</span><span class="p">)):</span>
    <span class="n">test_labels</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">test_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">9</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</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="nb">len</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">)):</span>
    <span class="n">extra_labels</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">extra_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">9</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="n">train_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">train_labels</span><span class="p">,</span> <span class="mi">29190</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">train_filenames</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="mi">29190</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">array</span><span class="p">([</span><span class="n">fivedigit_label</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">train_labels</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">array</span><span class="p">([</span><span class="n">fivedigit_label</span><span class="p">(</span><span class="n">test_labels</span><span class="p">[</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">test_labels</span><span class="p">))])</span>
<span class="n">extra_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">fivedigit_label</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">[</span><span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">))])</span>
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<h4 id="Display-examples-of-preprocessing">Display examples of preprocessing<a class="anchor-link" href="#Display-examples-of-preprocessing">&#182;</a></h4>
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<span class="n">image1</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">ndimage</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;train/30065.png&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Initial image size:&quot;</span><span class="p">,</span> <span class="n">image1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>

<span class="n">n1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">train_filenames</span> <span class="o">==</span> <span class="s1">&#39;30065.png&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">label1</span> <span class="o">=</span> <span class="n">train_labels</span><span class="p">[</span><span class="n">n1</span><span class="p">]</span>

<span class="n">box1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">get_box</span><span class="p">(</span><span class="n">train_b</span><span class="p">[</span><span class="s1">&#39;box&#39;</span><span class="p">][</span><span class="n">n1</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Digit boxes:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">box1</span><span class="p">)</span>
<span class="n">box_n1</span> <span class="o">=</span> <span class="n">image_box</span><span class="p">(</span><span class="n">box1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Digits&#39; box: &quot;</span><span class="p">,</span> <span class="n">box_n1</span><span class="p">)</span>

<span class="n">image_n1</span> <span class="o">=</span> <span class="n">image1</span><span class="p">[</span><span class="n">box_n1</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span><span class="n">box_n1</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">box_n1</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span><span class="n">box_n1</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
<span class="n">image64_1</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">image_n1</span><span class="p">,</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="o">/</span><span class="mi">255</span>
<span class="n">image64_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image64_1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">),</span> <span class="p">[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final image size: &quot;</span><span class="p">,</span> <span class="n">image64_2</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final image label: &quot;</span><span class="p">,</span> <span class="n">label1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Example of image preprocessing&#39;</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">image64_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">Blues</span><span class="p">);</span>
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<pre>Initial image size: (81, 120, 3)
Digit boxes:
 [[12  9 30 55]
 [36 11 31 55]]
Digits&#39; box:  [13 67 10 66]
Final image size:  (64, 64)
Final image label:  [[ 10.  10.  10.   4.   1.]]

Example of image preprocessing
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"
>
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<div class="prompt input_prompt">In&nbsp;[30]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">image_example</span> <span class="o">=</span> <span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;178.png&#39;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Image size: &quot;</span><span class="p">,</span> <span class="n">image_example</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Image label: &quot;</span><span class="p">,</span> <span class="n">image_example</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Example of image preprocessing with the function&#39;</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">image_example</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">Blues</span><span class="p">);</span>
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<pre>Image size:  (32, 32)
Image label:  [[ 10.  10.  10.   2.   1.]]

Example of image preprocessing with the function
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<h4 id="Image-preprocessing.-Variant-#1">Image preprocessing. Variant #1<a class="anchor-link" href="#Image-preprocessing.-Variant-#1">&#182;</a></h4>
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<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Applying the preprocessing function&quot;</span><span class="p">)</span>
<span class="c1"># train_images1 = np.array([get_image(&#39;train&#39;, x)[0] for x in train_filenames[:13000])</span>
<span class="c1"># train_images2 = np.array([get_image(&#39;train&#39;, x)[1] for x in train_filenames[:13000]])</span>

<span class="c1"># test_images1 = np.array([get_image(&#39;test&#39;, x)[0] for x in test_filenames[:6000]])</span>
<span class="c1"># test_images2 = np.array([get_image(&#39;test&#39;, x)[1] for x in test_filenames[:6000]])</span>

<span class="c1"># extra_images1 = np.array([get_image(&#39;extra&#39;, x)[0] for x in extra_filenames[:13000]])</span>
<span class="c1"># extra_images2 = np.array([get_image(&#39;extra&#39;, x)[1] for x in extra_filenames[:13000]])</span>
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<pre>Applying the preprocessing function
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<h4 id="Image-preprocessing.-Variant-#2">Image preprocessing. Variant #2<a class="anchor-link" href="#Image-preprocessing.-Variant-#2">&#182;</a></h4><h5 id="Set-#1:-13-000-train-data-points,-6-000-test-data-points,-13-000-extra-data-points">Set #1: 13 000 train data points, 6 000 test data points, 13 000 extra data points<a class="anchor-link" href="#Set-#1:-13-000-train-data-points,-6-000-test-data-points,-13-000-extra-data-points">&#182;</a></h5>
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<span class="n">train_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span>
<span class="n">train_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span> 

<span class="n">i</span><span class="o">=</span> <span class="mi">1000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">7001</span><span class="p">:</span> 

        <span class="n">train_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
        <span class="n">train_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
        <span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images01</span><span class="p">,</span> <span class="n">train_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images02</span><span class="p">,</span> <span class="n">train_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
        <span class="n">train_images01</span> <span class="o">=</span> <span class="n">train_images1</span>
        <span class="n">train_images02</span> <span class="o">=</span> <span class="n">train_images2</span>
        
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
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<pre>1000
2000
3000
4000
5000
6000
7000
8000
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<span class="n">train_images_8000_1</span> <span class="o">=</span> <span class="n">train_images1</span>
<span class="n">train_images_8000_2</span> <span class="o">=</span> <span class="n">train_images2</span>

<span class="nb">len</span><span class="p">(</span><span class="n">train_images_8000_1</span><span class="p">)</span>
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<pre>8000</pre>
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<span class="n">train_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">]])</span>
<span class="n">train_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">]])</span> 

<span class="n">i</span><span class="o">=</span> <span class="mi">9000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">12001</span><span class="p">:</span> 

        <span class="n">train_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
        <span class="n">train_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
        <span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images01</span><span class="p">,</span> <span class="n">train_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images02</span><span class="p">,</span> <span class="n">train_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
        <span class="n">train_images01</span> <span class="o">=</span> <span class="n">train_images1</span>
        <span class="n">train_images02</span> <span class="o">=</span> <span class="n">train_images2</span>
        
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
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<pre>9000
10000
11000
12000
13000
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<span class="n">train_images_13000_1</span> <span class="o">=</span> <span class="n">train_images1</span>
<span class="n">train_images_13000_2</span> <span class="o">=</span> <span class="n">train_images2</span>

<span class="nb">len</span><span class="p">(</span><span class="n">train_images_13000_1</span><span class="p">)</span>
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<pre>5000</pre>
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<span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images_8000_1</span><span class="p">,</span> <span class="n">train_images_13000_1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images_8000_2</span><span class="p">,</span> <span class="n">train_images_13000_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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    <div class="input_area">
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<span class="n">train_images1t</span> <span class="o">=</span> <span class="n">train_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">train_images2t</span> <span class="o">=</span> <span class="n">train_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">train_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">train_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">train_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_labels</span><span class="p">[:</span><span class="mi">13000</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_images1.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_images2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_labels.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span>
<span class="n">test_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span> 

<span class="n">i</span><span class="o">=</span> <span class="mi">1000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">5001</span><span class="p">:</span>
    
    <span class="n">test_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">test_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">test_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_images01</span><span class="p">,</span> <span class="n">test_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">test_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_images02</span><span class="p">,</span> <span class="n">test_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">test_images01</span> <span class="o">=</span> <span class="n">test_images1</span>
    <span class="n">test_images02</span> <span class="o">=</span> <span class="n">test_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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

<div class="output_wrapper">
<div class="output">


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>1000
2000
3000
4000
5000
6000
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images_6000_1</span> <span class="o">=</span> <span class="n">test_images1</span>
<span class="n">test_images_6000_2</span> <span class="o">=</span> <span class="n">test_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">test_images_6000_1</span><span class="p">)</span>
</pre></div>

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

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt output_prompt">Out[41]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>6000</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[43]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images1t</span> <span class="o">=</span> <span class="n">test_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">6000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">test_images2t</span> <span class="o">=</span> <span class="n">test_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">6000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">test_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">6000</span><span class="p">])</span>
<span class="n">test_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">6000</span><span class="p">])</span>
<span class="n">test_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_labels</span><span class="p">[:</span><span class="mi">6000</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">6000</span><span class="p">])</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_images1.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_images2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_labels.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[44]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span>
<span class="n">extra_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]])</span>

<span class="n">i</span><span class="o">=</span> <span class="mi">1000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">7001</span><span class="p">:</span>
    
    <span class="n">extra_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">extra_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images01</span><span class="p">,</span> <span class="n">extra_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images02</span><span class="p">,</span> <span class="n">extra_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">extra_images01</span> <span class="o">=</span> <span class="n">extra_images1</span>
    <span class="n">extra_images02</span> <span class="o">=</span> <span class="n">extra_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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

<div class="output_wrapper">
<div class="output">


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>1000
2000
3000
4000
5000
6000
7000
8000
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[45]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images_8000_1</span> <span class="o">=</span> <span class="n">extra_images1</span>
<span class="n">extra_images_8000_2</span> <span class="o">=</span> <span class="n">extra_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">extra_images_8000_1</span><span class="p">)</span>
</pre></div>

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

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt output_prompt">Out[45]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>8000</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[46]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">]])</span>
<span class="n">extra_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">8000</span><span class="p">:</span><span class="mi">9000</span><span class="p">]])</span>

<span class="n">i</span> <span class="o">=</span> <span class="mi">9000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">12001</span><span class="p">:</span>
    
    <span class="n">extra_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">extra_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images01</span><span class="p">,</span> <span class="n">extra_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images02</span><span class="p">,</span> <span class="n">extra_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">extra_images01</span> <span class="o">=</span> <span class="n">extra_images1</span>
    <span class="n">extra_images02</span> <span class="o">=</span> <span class="n">extra_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
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<pre>9000
10000
11000
12000
13000
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<span class="n">extra_images_13000_1</span> <span class="o">=</span> <span class="n">extra_images1</span>
<span class="n">extra_images_13000_2</span> <span class="o">=</span> <span class="n">extra_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">extra_images_13000_1</span><span class="p">)</span>
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<pre>5000</pre>
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<span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images_8000_1</span><span class="p">,</span> <span class="n">extra_images_13000_1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images_8000_2</span><span class="p">,</span> <span class="n">extra_images_13000_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">extra_images1t</span> <span class="o">=</span> <span class="n">extra_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">extra_images2t</span> <span class="o">=</span> <span class="n">extra_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">extra_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">extra_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">extra_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_labels</span><span class="p">[:</span><span class="mi">13000</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">])</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_images1.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_images2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_labels.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<span class="n">pickle_file</span> <span class="o">=</span> <span class="s1">&#39;svhn1.pickle&#39;</span>

<span class="k">try</span><span class="p">:</span>
    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span>
    <span class="n">save</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train_images1&#39;</span><span class="p">:</span> <span class="n">train_images1</span><span class="p">,</span> <span class="s1">&#39;train_images2&#39;</span><span class="p">:</span> <span class="n">train_images2</span><span class="p">,</span>
            <span class="s1">&#39;train_labels&#39;</span><span class="p">:</span> <span class="n">train_labels</span><span class="p">[:</span><span class="mi">13000</span><span class="p">],</span> <span class="s1">&#39;train_filenames&#39;</span><span class="p">:</span> <span class="n">train_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">],</span>
            <span class="s1">&#39;test_images1&#39;</span><span class="p">:</span> <span class="n">test_images1</span><span class="p">,</span> <span class="s1">&#39;test_images2&#39;</span><span class="p">:</span> <span class="n">test_images2</span><span class="p">,</span>
            <span class="s1">&#39;test_labels&#39;</span><span class="p">:</span> <span class="n">test_labels</span><span class="p">[:</span><span class="mi">6000</span><span class="p">],</span> <span class="s1">&#39;test_filenames&#39;</span><span class="p">:</span> <span class="n">test_filenames</span><span class="p">[:</span><span class="mi">6000</span><span class="p">],</span>
            <span class="s1">&#39;extra_images1&#39;</span><span class="p">:</span> <span class="n">extra_images1</span><span class="p">,</span> <span class="s1">&#39;extra_images2&#39;</span><span class="p">:</span> <span class="n">extra_images2</span><span class="p">,</span>
            <span class="s1">&#39;extra_labels&#39;</span><span class="p">:</span> <span class="n">extra_labels</span><span class="p">[:</span><span class="mi">13000</span><span class="p">],</span> <span class="s1">&#39;extra_filenames&#39;</span><span class="p">:</span> <span class="n">extra_filenames</span><span class="p">[:</span><span class="mi">13000</span><span class="p">]}</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Unable to save data to&#39;</span><span class="p">,</span> <span class="n">pickle_file</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
    <span class="k">raise</span>

<span class="k">del</span> <span class="n">save</span>
    
<span class="n">statinfo</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">stat</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Compressed pickle size:&#39;</span><span class="p">,</span> <span class="n">statinfo</span><span class="o">.</span><span class="n">st_size</span><span class="p">)</span>
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<pre>Compressed pickle size: 1050431359
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<h5 id="Set-#2:-13-000-train-data-points,-6-000-test-data-points,-13-000-extra-data-points">Set #2: 13 000 train data points, 6 000 test data points, 13 000 extra data points<a class="anchor-link" href="#Set-#2:-13-000-train-data-points,-6-000-test-data-points,-13-000-extra-data-points">&#182;</a></h5>
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<div class="prompt input_prompt">In&nbsp;[54]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">14000</span><span class="p">]])</span>
<span class="n">train_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">14000</span><span class="p">]])</span> 

<span class="n">i</span><span class="o">=</span> <span class="mi">14000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">18001</span><span class="p">:</span> 
    
        <span class="n">train_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
        <span class="n">train_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
        <span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images01</span><span class="p">,</span> <span class="n">train_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images02</span><span class="p">,</span> <span class="n">train_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
        <span class="n">train_images01</span> <span class="o">=</span> <span class="n">train_images1</span>
        <span class="n">train_images02</span> <span class="o">=</span> <span class="n">train_images2</span>
        
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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<pre>14000
15000
16000
17000
18000
19000
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images_19000_1</span> <span class="o">=</span> <span class="n">train_images1</span>
<span class="n">train_images_19000_2</span> <span class="o">=</span> <span class="n">train_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">train_images_19000_1</span><span class="p">)</span>
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<pre>6000</pre>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">19000</span><span class="p">:</span><span class="mi">20000</span><span class="p">]])</span>
<span class="n">train_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">19000</span><span class="p">:</span><span class="mi">20000</span><span class="p">]])</span> 

<span class="n">i</span><span class="o">=</span> <span class="mi">20000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">25001</span><span class="p">:</span> 
    
        <span class="n">train_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
        <span class="n">train_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">train_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
        <span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images01</span><span class="p">,</span> <span class="n">train_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images02</span><span class="p">,</span> <span class="n">train_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
        <span class="n">train_images01</span> <span class="o">=</span> <span class="n">train_images1</span>
        <span class="n">train_images02</span> <span class="o">=</span> <span class="n">train_images2</span>
        
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>20000
21000
22000
23000
24000
25000
26000
</pre>
</div>
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</div>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[57]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images_26000_1</span> <span class="o">=</span> <span class="n">train_images1</span>
<span class="n">train_images_26000_2</span> <span class="o">=</span> <span class="n">train_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">train_images_26000_1</span><span class="p">)</span>
</pre></div>

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<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt output_prompt">Out[57]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>7000</pre>
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</div>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[58]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images_19000_1</span><span class="p">,</span> <span class="n">train_images_26000_1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">train_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">train_images_19000_2</span><span class="p">,</span> <span class="n">train_images_26000_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>

</div>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[61]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images1t</span> <span class="o">=</span> <span class="n">train_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">train_images2t</span> <span class="o">=</span> <span class="n">train_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">train_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">])</span>
<span class="n">train_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">])</span>
<span class="n">train_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train_labels</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">])</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_images1_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_images2_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;train_labels_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[62]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">8000</span><span class="p">]])</span>
<span class="n">test_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">8000</span><span class="p">]])</span>

<span class="n">i</span><span class="o">=</span> <span class="mi">8000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">12001</span><span class="p">:</span>
    
    <span class="n">test_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">test_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">test_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">test_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_images01</span><span class="p">,</span> <span class="n">test_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">test_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">test_images02</span><span class="p">,</span> <span class="n">test_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">test_images01</span> <span class="o">=</span> <span class="n">test_images1</span>
    <span class="n">test_images02</span> <span class="o">=</span> <span class="n">test_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>8000
9000
10000
11000
12000
13000
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[63]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images_13000_1</span> <span class="o">=</span> <span class="n">test_images1</span>
<span class="n">test_images_13000_2</span> <span class="o">=</span> <span class="n">test_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">test_images_13000_1</span><span class="p">)</span>
</pre></div>

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

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt output_prompt">Out[63]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>6000</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[66]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">test_images1t</span> <span class="o">=</span> <span class="n">test_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">6000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">test_images2t</span> <span class="o">=</span> <span class="n">test_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">6000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">test_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">test_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">])</span>
<span class="n">test_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">test_labels</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">])</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_images1_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_images2_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test_labels_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[67]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">14000</span><span class="p">]])</span>
<span class="n">extra_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">14000</span><span class="p">]])</span>

<span class="n">i</span><span class="o">=</span> <span class="mi">14000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">18001</span><span class="p">:</span>
    
    <span class="n">extra_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">extra_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images01</span><span class="p">,</span> <span class="n">extra_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images02</span><span class="p">,</span> <span class="n">extra_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">extra_images01</span> <span class="o">=</span> <span class="n">extra_images1</span>
    <span class="n">extra_images02</span> <span class="o">=</span> <span class="n">extra_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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<pre>14000
15000
16000
17000
18000
19000
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<div class="prompt input_prompt">In&nbsp;[68]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images_19000_1</span> <span class="o">=</span> <span class="n">extra_images1</span>
<span class="n">extra_images_19000_2</span> <span class="o">=</span> <span class="n">extra_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">extra_images_19000_1</span><span class="p">)</span>
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<pre>6000</pre>
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<div class="prompt input_prompt">In&nbsp;[78]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images01</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">25000</span><span class="p">:</span><span class="mi">26000</span><span class="p">]])</span>
<span class="n">extra_images02</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">25000</span><span class="p">:</span><span class="mi">26000</span><span class="p">]])</span>

<span class="n">i</span><span class="o">=</span> <span class="mi">26000</span>
<span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">31001</span><span class="p">:</span>
    
    <span class="n">extra_images11</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span>
    <span class="n">extra_images12</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">get_image</span><span class="p">(</span><span class="s1">&#39;extra&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1000</span><span class="p">]])</span> 
        
    <span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images01</span><span class="p">,</span> <span class="n">extra_images11</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images02</span><span class="p">,</span> <span class="n">extra_images12</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        
    <span class="n">extra_images01</span> <span class="o">=</span> <span class="n">extra_images1</span>
    <span class="n">extra_images02</span> <span class="o">=</span> <span class="n">extra_images2</span>
        
    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1000</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
</pre></div>

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<pre>26000
27000
28000
29000
30000
31000
32000
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<div class="prompt input_prompt">In&nbsp;[79]:</div>
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<span class="n">extra_images_32000_1</span> <span class="o">=</span> <span class="n">extra_images1</span>
<span class="n">extra_images_32000_2</span> <span class="o">=</span> <span class="n">extra_images2</span>
<span class="nb">len</span><span class="p">(</span><span class="n">extra_images_32000_1</span><span class="p">)</span>
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<pre>7000</pre>
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<div class="prompt input_prompt">In&nbsp;[80]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">extra_images1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images_19000_1</span><span class="p">,</span> <span class="n">extra_images_32000_1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">extra_images2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_images_19000_2</span><span class="p">,</span> <span class="n">extra_images_32000_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">extra_filenames2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">19000</span><span class="p">],</span> <span class="n">extra_filenames</span><span class="p">[</span><span class="mi">25000</span><span class="p">:</span><span class="mi">32000</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">extra_labels2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">extra_labels</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">19000</span><span class="p">],</span> <span class="n">extra_labels</span><span class="p">[</span><span class="mi">25000</span><span class="p">:</span><span class="mi">32000</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">extra_images1t</span> <span class="o">=</span> <span class="n">extra_images1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="o">*</span><span class="mi">3</span><span class="p">)</span>
<span class="n">extra_images2t</span> <span class="o">=</span> <span class="n">extra_images2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">13000</span><span class="p">,</span> <span class="mi">32</span><span class="o">*</span><span class="mi">32</span><span class="p">)</span>

<span class="n">extra_images_df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_images1t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames2</span><span class="p">)</span>
<span class="n">extra_images_df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_images2t</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames2</span><span class="p">)</span>
<span class="n">extra_labels_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">extra_labels2</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">extra_filenames2</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_images1_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_images_df1</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_images2_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_images_df2</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>    
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;extra_labels_2.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">extra_labels_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">pickle_file</span> <span class="o">=</span> <span class="s1">&#39;svhn2.pickle&#39;</span>

<span class="k">try</span><span class="p">:</span>
    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span>
    <span class="n">save</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train_images1&#39;</span><span class="p">:</span> <span class="n">train_images1</span><span class="p">,</span> <span class="s1">&#39;train_images2&#39;</span><span class="p">:</span> <span class="n">train_images2</span><span class="p">,</span>
            <span class="s1">&#39;train_labels&#39;</span><span class="p">:</span> <span class="n">train_labels</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">],</span> <span class="s1">&#39;train_filenames&#39;</span><span class="p">:</span> <span class="n">train_filenames</span><span class="p">[</span><span class="mi">13000</span><span class="p">:</span><span class="mi">26000</span><span class="p">],</span>
            <span class="s1">&#39;test_images1&#39;</span><span class="p">:</span> <span class="n">test_images1</span><span class="p">,</span> <span class="s1">&#39;test_images2&#39;</span><span class="p">:</span> <span class="n">test_images2</span><span class="p">,</span>
            <span class="s1">&#39;test_labels&#39;</span><span class="p">:</span> <span class="n">test_labels</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">],</span> <span class="s1">&#39;test_filenames&#39;</span><span class="p">:</span> <span class="n">test_filenames</span><span class="p">[</span><span class="mi">7000</span><span class="p">:</span><span class="mi">13000</span><span class="p">],</span>
            <span class="s1">&#39;extra_images1&#39;</span><span class="p">:</span> <span class="n">extra_images1</span><span class="p">,</span> <span class="s1">&#39;extra_images2&#39;</span><span class="p">:</span> <span class="n">extra_images2</span><span class="p">,</span>
            <span class="s1">&#39;extra_labels&#39;</span><span class="p">:</span> <span class="n">extra_labels2</span><span class="p">,</span> <span class="s1">&#39;extra_filenames&#39;</span><span class="p">:</span> <span class="n">extra_filenames2</span><span class="p">}</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Unable to save data to&#39;</span><span class="p">,</span> <span class="n">pickle_file</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
    <span class="k">raise</span>

<span class="k">del</span> <span class="n">save</span>
    
<span class="n">statinfo</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">stat</span><span class="p">(</span><span class="n">pickle_file</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Compressed pickle size:&#39;</span><span class="p">,</span> <span class="n">statinfo</span><span class="o">.</span><span class="n">st_size</span><span class="p">)</span>
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<pre>Compressed pickle size: 1050440267
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<h4 id="Checkpoint-2.-Load-the-realistic-data-and-prepare-features-and-targets">Checkpoint 2. Load the realistic data and prepare features and targets<a class="anchor-link" href="#Checkpoint-2.-Load-the-realistic-data-and-prepare-features-and-targets">&#182;</a></h4>
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<div class="prompt input_prompt">In&nbsp;[14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_images2.csv&quot;</span><span class="p">)</span>
<span class="n">train_labels</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_labels.csv&quot;</span><span class="p">)</span>

<span class="n">test_images2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_images2.csv&quot;</span><span class="p">)</span>
<span class="n">test_labels</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_labels.csv&quot;</span><span class="p">)</span>

<span class="n">extra_images2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_images2.csv&quot;</span><span class="p">)</span>
<span class="n">extra_labels</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_labels.csv&quot;</span><span class="p">)</span>

<span class="n">display</span><span class="p">(</span><span class="n">train_images2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">train_labels</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>

<span class="n">display</span><span class="p">(</span><span class="n">test_images2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_labels</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>

<span class="n">display</span><span class="p">(</span><span class="n">extra_images2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_labels</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
</pre></div>

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      <td>0.726082</td>
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      <td>0.572475</td>
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      <td>0.192925</td>
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      <td>0.233271</td>
      <td>0.227729</td>
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      <td>5502.png</td>
      <td>10.0</td>
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      <th>4</th>
      <th>5</th>
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    <tr>
      <th>0</th>
      <td>0.731729</td>
      <td>0.731729</td>
      <td>0.731729</td>
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      <td>0.732859</td>
      <td>0.732859</td>
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      <td>0.630882</td>
      <td>0.635082</td>
      <td>0.611831</td>
      <td>0.572169</td>
      <td>0.529757</td>
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      <td>0.167078</td>
      <td>0.128478</td>
      <td>0.133976</td>
      <td>0.132525</td>
      <td>0.134549</td>
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  </tbody>
</table>
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      <th>3</th>
      <th>4</th>
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      <th>5997</th>
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      <td>10.0</td>
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      <td>10.0</td>
      <td>10.0</td>
      <td>10.0</td>
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      <th>4</th>
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      <td>0.857800</td>
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      <td>0.845800</td>
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      <td>0.404569</td>
      <td>0.421384</td>
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      <td>0.266596</td>
      <td>0.236608</td>
      <td>0.205769</td>
      <td>0.195455</td>
      <td>0.208671</td>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[15]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">train_images2_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_images2_2.csv&quot;</span><span class="p">)</span>
<span class="n">train_labels_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;train_labels_2.csv&quot;</span><span class="p">)</span>

<span class="n">test_images2_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_images2_2.csv&quot;</span><span class="p">)</span>
<span class="n">test_labels_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;test_labels_2.csv&quot;</span><span class="p">)</span>

<span class="n">extra_images2_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_images2_2.csv&quot;</span><span class="p">)</span>
<span class="n">extra_labels_2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;extra_labels_2.csv&quot;</span><span class="p">)</span>

<span class="n">display</span><span class="p">(</span><span class="n">train_images2_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">train_labels_2</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>

<span class="n">display</span><span class="p">(</span><span class="n">test_images2_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_labels_2</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>

<span class="n">display</span><span class="p">(</span><span class="n">extra_images2_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">,</span> <span class="s2">&quot;5&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">extra_labels_2</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
</pre></div>

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      <th>3</th>
      <th>4</th>
      <th>5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.775827</td>
      <td>0.747204</td>
      <td>0.702894</td>
      <td>0.695498</td>
      <td>0.694773</td>
      <td>0.713933</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.924996</td>
      <td>0.893624</td>
      <td>0.846353</td>
      <td>0.800020</td>
      <td>0.749486</td>
      <td>0.703769</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.595980</td>
      <td>0.603780</td>
      <td>0.615224</td>
      <td>0.632933</td>
      <td>0.653882</td>
      <td>0.679435</td>
    </tr>
  </tbody>
</table>
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      <th></th>
      <th>filename</th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
    </tr>
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      <th>0</th>
      <td>20953.png</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>9.0</td>
      <td>5.0</td>
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    <tr>
      <th>1</th>
      <td>30030.png</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>3.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2036.png</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>8.0</td>
      <td>2.0</td>
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      <th>3</th>
      <th>4</th>
      <th>5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.160784</td>
      <td>0.163533</td>
      <td>0.165835</td>
      <td>0.160784</td>
      <td>0.161914</td>
      <td>0.161914</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.642588</td>
      <td>0.638388</td>
      <td>0.637427</td>
      <td>0.638769</td>
      <td>0.628067</td>
      <td>0.590192</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.059059</td>
      <td>0.066220</td>
      <td>0.077302</td>
      <td>0.085145</td>
      <td>0.094396</td>
      <td>0.078710</td>
    </tr>
  </tbody>
</table>
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<span class="n">images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">train_images2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> 
                         <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">extra_images2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))),</span> 
                        <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">train_labels</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]]),</span> 
                         <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">extra_labels</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]])),</span> 
                        <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">images_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">train_images2_2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> 
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">extra_images2_2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))),</span> 
                          <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">labels_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">train_labels_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]]),</span> 
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">extra_labels_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]])),</span> 
                          <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">images</span><span class="p">,</span> <span class="n">images_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">labels</span><span class="p">,</span> <span class="n">labels_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">images_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test_images2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span> 
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test_images2_2</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;filename&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))),</span> 
                          <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">labels_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test_labels</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]]),</span> 
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">test_labels_2</span><span class="p">[[</span><span class="s2">&quot;0&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="s2">&quot;4&quot;</span><span class="p">]])),</span> 
                          <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<span class="n">X_train</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">52000</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">labels</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">images_t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">12000</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">labels_t</span>
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<span class="k">def</span> <span class="nf">digit_to_categorical</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
    <span class="n">n</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="mi">1</span><span class="p">]</span>
    <span class="n">data_cat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</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="n">n</span><span class="p">,</span> <span class="mi">11</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">n</span><span class="p">):</span>
        <span class="n">data_cat</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">ks</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">data</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
        
    <span class="k">return</span> <span class="n">data_cat</span>

<span class="n">y_train_cat</span> <span class="o">=</span> <span class="n">digit_to_categorical</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_cat</span> <span class="o">=</span> <span class="n">digit_to_categorical</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>

<span class="n">y_train_cat_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_train_cat</span><span class="p">[:,</span> <span class="n">i</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">5</span><span class="p">)]</span>
<span class="n">y_test_cat_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_test_cat</span><span class="p">[:,</span> <span class="n">i</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">5</span><span class="p">)]</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Train loaded set&#39;</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train_cat</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;Test loaded set&#39;</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_test_cat</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>Train loaded set (52000, 32, 32, 1) (52000, 5, 11)
Test loaded set (12000, 32, 32, 1) (12000, 5, 11)
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<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">12</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">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">Blues</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train_images2</span><span class="p">[</span><span class="s1">&#39;filename&#39;</span><span class="p">][</span><span class="mi">12</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="mi">12</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_train_cat</span><span class="p">[:][</span><span class="mi">12</span><span class="p">])</span>
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<pre>8629.png
[ 10.  10.   5.   0.   7.]
[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]
 [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]
 [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]]
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<h2 id="Step-2:-Train-a-Model-on-a-Realistic-Dataset">Step 2: Train a Model on a Realistic Dataset<a class="anchor-link" href="#Step-2:-Train-a-Model-on-a-Realistic-Dataset">&#182;</a></h2><p>Once we have settled on a good architecture, we can train the model on real data. In particular, the <a href="http://ufldl.stanford.edu/housenumbers/">Street View House Numbers (SVHN)</a> dataset is a good large-scale dataset collected from house numbers in Google Street View. Training on this more challenging dataset, where the digits are not neatly lined-up and have various skews, fonts and colors, likely means you have to do some hyperparameter exploration to perform well.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">cnn_model</span><span class="p">():</span>    
    <span class="n">model_input</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">model_input</span><span class="p">)</span>
        
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)(</span><span class="n">model_input</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>       
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
              
    <span class="n">x</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">y1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y3</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y4</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y5</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">model_input</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">,</span> <span class="n">y4</span><span class="p">,</span> <span class="n">y5</span><span class="p">])</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
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<div class="prompt input_prompt">In&nbsp;[25]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">cnn_model</span> <span class="o">=</span> <span class="n">cnn_model</span><span class="p">()</span>
<span class="n">cnn_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.cnn1.hdf5&#39;</span><span class="p">,</span> 
                                   <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">cnn_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train_cat_list</span><span class="p">,</span> 
                        <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test_cat_list</span><span class="p">),</span> 
                        <span class="n">epochs</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                        <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cnn_checkpointer</span><span class="p">])</span>
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<pre>Epoch 00000: val_loss improved from inf to 4.85822, saving model to weights.best.cnn1.hdf5
Epoch 00001: val_loss improved from 4.85822 to 3.47396, saving model to weights.best.cnn1.hdf5
Epoch 00002: val_loss improved from 3.47396 to 2.67593, saving model to weights.best.cnn1.hdf5
Epoch 00003: val_loss improved from 2.67593 to 2.04893, saving model to weights.best.cnn1.hdf5
Epoch 00004: val_loss improved from 2.04893 to 1.81501, saving model to weights.best.cnn1.hdf5
Epoch 00005: val_loss improved from 1.81501 to 1.53273, saving model to weights.best.cnn1.hdf5
Epoch 00006: val_loss improved from 1.53273 to 1.39475, saving model to weights.best.cnn1.hdf5
Epoch 00007: val_loss improved from 1.39475 to 1.38398, saving model to weights.best.cnn1.hdf5
Epoch 00008: val_loss improved from 1.38398 to 1.28566, saving model to weights.best.cnn1.hdf5
Epoch 00009: val_loss improved from 1.28566 to 1.19139, saving model to weights.best.cnn1.hdf5
Epoch 00010: val_loss improved from 1.19139 to 1.14903, saving model to weights.best.cnn1.hdf5
Epoch 00011: val_loss improved from 1.14903 to 1.09842, saving model to weights.best.cnn1.hdf5
Epoch 00012: val_loss improved from 1.09842 to 1.07015, saving model to weights.best.cnn1.hdf5
Epoch 00013: val_loss did not improve
Epoch 00014: val_loss improved from 1.07015 to 1.05801, saving model to weights.best.cnn1.hdf5
Epoch 00015: val_loss improved from 1.05801 to 0.99764, saving model to weights.best.cnn1.hdf5
Epoch 00016: val_loss did not improve
Epoch 00017: val_loss did not improve
Epoch 00018: val_loss did not improve
Epoch 00019: val_loss improved from 0.99764 to 0.96585, saving model to weights.best.cnn1.hdf5
Epoch 00020: val_loss improved from 0.96585 to 0.95185, saving model to weights.best.cnn1.hdf5
Epoch 00021: val_loss did not improve
Epoch 00022: val_loss improved from 0.95185 to 0.94672, saving model to weights.best.cnn1.hdf5
Epoch 00023: val_loss improved from 0.94672 to 0.93866, saving model to weights.best.cnn1.hdf5
Epoch 00024: val_loss improved from 0.93866 to 0.91641, saving model to weights.best.cnn1.hdf5
Epoch 00025: val_loss improved from 0.91641 to 0.89374, saving model to weights.best.cnn1.hdf5
Epoch 00026: val_loss did not improve
Epoch 00027: val_loss improved from 0.89374 to 0.87660, saving model to weights.best.cnn1.hdf5
Epoch 00028: val_loss did not improve
Epoch 00029: val_loss did not improve
Epoch 00030: val_loss improved from 0.87660 to 0.86503, saving model to weights.best.cnn1.hdf5
Epoch 00031: val_loss did not improve
Epoch 00032: val_loss did not improve
Epoch 00033: val_loss improved from 0.86503 to 0.85255, saving model to weights.best.cnn1.hdf5
Epoch 00034: val_loss did not improve
Epoch 00035: val_loss did not improve
Epoch 00036: val_loss did not improve
Epoch 00037: val_loss did not improve
Epoch 00038: val_loss improved from 0.85255 to 0.83615, saving model to weights.best.cnn1.hdf5
Epoch 00039: val_loss did not improve
Epoch 00040: val_loss did not improve
Epoch 00041: val_loss did not improve
Epoch 00042: val_loss improved from 0.83615 to 0.81315, saving model to weights.best.cnn1.hdf5
Epoch 00043: val_loss did not improve
Epoch 00044: val_loss did not improve
Epoch 00045: val_loss did not improve
Epoch 00046: val_loss did not improve
Epoch 00047: val_loss did not improve
Epoch 00048: val_loss did not improve
Epoch 00049: val_loss did not improve
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<div class="prompt input_prompt">In&nbsp;[26]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">cnn_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.cnn1.hdf5&#39;</span><span class="p">)</span>
<span class="n">cnn_scores</span> <span class="o">=</span> <span class="n">cnn_model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test_cat_list</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CNN Model 1. </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Scores: </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="p">,</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Second digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Third digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fourth digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fifth digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">cnn_model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre>CNN Model 1. 

Scores: 
 [0.81314641666412357, 0.0015125020390175906, 0.01810593365719736, 0.13173956616191815, 0.30869675874710084, 0.35309165848294893, 0.99983333333333335, 0.99533333333333329, 0.96366666666666667, 0.91508333333333336, 0.89358333333333329]
First digit. Accuracy: 99.98%
Second digit. Accuracy: 99.53%
Third digit. Accuracy: 96.37%
Fourth digit. Accuracy: 91.51%
Fifth digit. Accuracy: 89.36%
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 32, 32, 1)     0                                            
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 32, 32, 32)    320         input_1[0][0]                    
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 16, 16, 32)    0           conv2d_1[0][0]                   
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 14, 14, 32)    9248        max_pooling2d_1[0][0]            
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 7, 7, 32)      0           conv2d_2[0][0]                   
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 7, 7, 32)      0           max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
conv2d_3 (Conv2D)                (None, 5, 5, 64)      18496       dropout_1[0][0]                  
____________________________________________________________________________________________________
conv2d_4 (Conv2D)                (None, 3, 3, 64)      36928       conv2d_3[0][0]                   
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 3, 3, 64)      0           conv2d_4[0][0]                   
____________________________________________________________________________________________________
conv2d_5 (Conv2D)                (None, 1, 1, 128)     73856       dropout_2[0][0]                  
____________________________________________________________________________________________________
dropout_3 (Dropout)              (None, 1, 1, 128)     0           conv2d_5[0][0]                   
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 128)           0           dropout_3[0][0]                  
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 512)           66048       flatten_1[0][0]                  
____________________________________________________________________________________________________
dropout_4 (Dropout)              (None, 512)           0           dense_1[0][0]                    
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 11)            5643        dropout_4[0][0]                  
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 11)            5643        dropout_4[0][0]                  
____________________________________________________________________________________________________
dense_4 (Dense)                  (None, 11)            5643        dropout_4[0][0]                  
____________________________________________________________________________________________________
dense_5 (Dense)                  (None, 11)            5643        dropout_4[0][0]                  
____________________________________________________________________________________________________
dense_6 (Dense)                  (None, 11)            5643        dropout_4[0][0]                  
====================================================================================================
Total params: 233,111
Trainable params: 233,111
Non-trainable params: 0
____________________________________________________________________________________________________
None
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<div class="prompt input_prompt">In&nbsp;[28]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_2_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;First digit&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_3_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Second digit&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_4_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Third digit&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_5_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Fourth digit&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_6_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Fifth digit&#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="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">);</span>
</pre></div>

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<span class="n">avg_accuracy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">cnn_scores</span><span class="p">[</span><span class="n">i</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">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">)])</span><span class="o">/</span><span class="mi">5</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CNN Model 1. Average Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">avg_accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>CNN Model 1. Average Accuracy: 95.35%
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="k">def</span> <span class="nf">cnn_model2</span><span class="p">():</span>    
    <span class="n">model_input</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>  
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">model_input</span><span class="p">)</span>
        
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
              
    <span class="n">x</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">y1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y2</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y3</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y4</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y5</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">model_input</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="p">[</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y3</span><span class="p">,</span> <span class="n">y4</span><span class="p">,</span> <span class="n">y5</span><span class="p">])</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
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<span class="n">cnn_model2</span> <span class="o">=</span> <span class="n">cnn_model2</span><span class="p">()</span>
<span class="n">cnn_checkpointer2</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;weights.best.cnn2.hdf5&#39;</span><span class="p">,</span> 
                                    <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">history2</span> <span class="o">=</span> <span class="n">cnn_model2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train_cat_list</span><span class="p">,</span> 
                          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test_cat_list</span><span class="p">),</span> 
                          <span class="n">epochs</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                          <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cnn_checkpointer2</span><span class="p">])</span>
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<pre>Epoch 00000: val_loss improved from inf to 2.37346, saving model to weights.best.cnn2.hdf5
Epoch 00001: val_loss improved from 2.37346 to 1.78446, saving model to weights.best.cnn2.hdf5
Epoch 00002: val_loss improved from 1.78446 to 1.55476, saving model to weights.best.cnn2.hdf5
Epoch 00003: val_loss improved from 1.55476 to 1.43268, saving model to weights.best.cnn2.hdf5
Epoch 00004: val_loss improved from 1.43268 to 1.41285, saving model to weights.best.cnn2.hdf5
Epoch 00005: val_loss improved from 1.41285 to 1.24013, saving model to weights.best.cnn2.hdf5
Epoch 00006: val_loss improved from 1.24013 to 1.21874, saving model to weights.best.cnn2.hdf5
Epoch 00007: val_loss improved from 1.21874 to 1.16470, saving model to weights.best.cnn2.hdf5
Epoch 00008: val_loss improved from 1.16470 to 1.14959, saving model to weights.best.cnn2.hdf5
Epoch 00009: val_loss improved from 1.14959 to 1.10223, saving model to weights.best.cnn2.hdf5
Epoch 00010: val_loss did not improve
Epoch 00011: val_loss improved from 1.10223 to 1.07837, saving model to weights.best.cnn2.hdf5
Epoch 00012: val_loss improved from 1.07837 to 1.06007, saving model to weights.best.cnn2.hdf5
Epoch 00013: val_loss improved from 1.06007 to 1.03300, saving model to weights.best.cnn2.hdf5
Epoch 00014: val_loss improved from 1.03300 to 1.01226, saving model to weights.best.cnn2.hdf5
Epoch 00015: val_loss improved from 1.01226 to 1.00669, saving model to weights.best.cnn2.hdf5
Epoch 00016: val_loss improved from 1.00669 to 0.98712, saving model to weights.best.cnn2.hdf5
Epoch 00017: val_loss did not improve
Epoch 00018: val_loss did not improve
Epoch 00019: val_loss improved from 0.98712 to 0.96057, saving model to weights.best.cnn2.hdf5
Epoch 00020: val_loss did not improve
Epoch 00021: val_loss did not improve
Epoch 00022: val_loss improved from 0.96057 to 0.95088, saving model to weights.best.cnn2.hdf5
Epoch 00023: val_loss did not improve
Epoch 00024: val_loss did not improve
Epoch 00025: val_loss did not improve
Epoch 00026: val_loss improved from 0.95088 to 0.93518, saving model to weights.best.cnn2.hdf5
Epoch 00027: val_loss did not improve
Epoch 00028: val_loss did not improve
Epoch 00029: val_loss improved from 0.93518 to 0.93088, saving model to weights.best.cnn2.hdf5
Epoch 00030: val_loss did not improve
Epoch 00031: val_loss improved from 0.93088 to 0.92076, saving model to weights.best.cnn2.hdf5
Epoch 00032: val_loss did not improve
Epoch 00033: val_loss did not improve
Epoch 00034: val_loss improved from 0.92076 to 0.90297, saving model to weights.best.cnn2.hdf5
Epoch 00035: val_loss did not improve
Epoch 00036: val_loss improved from 0.90297 to 0.90220, saving model to weights.best.cnn2.hdf5
Epoch 00037: val_loss did not improve
Epoch 00038: val_loss did not improve
Epoch 00039: val_loss did not improve
Epoch 00040: val_loss did not improve
Epoch 00041: val_loss did not improve
Epoch 00042: val_loss improved from 0.90220 to 0.89694, saving model to weights.best.cnn2.hdf5
Epoch 00043: val_loss did not improve
Epoch 00044: val_loss improved from 0.89694 to 0.87600, saving model to weights.best.cnn2.hdf5
Epoch 00045: val_loss did not improve
Epoch 00046: val_loss did not improve
Epoch 00047: val_loss did not improve
Epoch 00048: val_loss did not improve
Epoch 00049: val_loss did not improve
Epoch 00050: val_loss did not improve
Epoch 00051: val_loss improved from 0.87600 to 0.87020, saving model to weights.best.cnn2.hdf5
Epoch 00052: val_loss improved from 0.87020 to 0.86521, saving model to weights.best.cnn2.hdf5
Epoch 00053: val_loss did not improve
Epoch 00054: val_loss did not improve
Epoch 00055: val_loss did not improve
Epoch 00056: val_loss improved from 0.86521 to 0.86226, saving model to weights.best.cnn2.hdf5
Epoch 00057: val_loss did not improve
Epoch 00058: val_loss did not improve
Epoch 00059: val_loss did not improve
Epoch 00060: val_loss improved from 0.86226 to 0.85869, saving model to weights.best.cnn2.hdf5
Epoch 00061: val_loss improved from 0.85869 to 0.85794, saving model to weights.best.cnn2.hdf5
Epoch 00062: val_loss improved from 0.85794 to 0.85754, saving model to weights.best.cnn2.hdf5
Epoch 00063: val_loss did not improve
Epoch 00064: val_loss did not improve
Epoch 00065: val_loss improved from 0.85754 to 0.85489, saving model to weights.best.cnn2.hdf5
Epoch 00066: val_loss improved from 0.85489 to 0.85392, saving model to weights.best.cnn2.hdf5
Epoch 00067: val_loss did not improve
Epoch 00068: val_loss improved from 0.85392 to 0.84375, saving model to weights.best.cnn2.hdf5
Epoch 00069: val_loss did not improve
Epoch 00070: val_loss did not improve
Epoch 00071: val_loss improved from 0.84375 to 0.83953, saving model to weights.best.cnn2.hdf5
Epoch 00072: val_loss did not improve
Epoch 00073: val_loss did not improve
Epoch 00074: val_loss did not improve
</pre>
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<div class="prompt input_prompt">In&nbsp;[33]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">cnn_model2</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;weights.best.cnn2.hdf5&#39;</span><span class="p">)</span>
<span class="n">cnn_scores2</span> <span class="o">=</span> <span class="n">cnn_model2</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test_cat_list</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CNN Model 2. </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Scores: </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="p">,</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;First digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Second digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Third digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fourth digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fifth digit. Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">cnn_model2</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
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<pre>CNN Model 2. 

Scores: 
 [0.83953403600056964, 0.0021868164065130791, 0.014856562036930428, 0.11848371175241967, 0.32086406788229943, 0.38314287703235944, 0.99983333333333335, 0.99616666666666664, 0.96708333333333329, 0.91200000000000003, 0.88624999999999998]
First digit. Accuracy: 99.98%
Second digit. Accuracy: 99.62%
Third digit. Accuracy: 96.71%
Fourth digit. Accuracy: 91.20%
Fifth digit. Accuracy: 88.62%
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_2 (InputLayer)             (None, 32, 32, 1)     0                                            
____________________________________________________________________________________________________
batch_normalization_2 (BatchNorm (None, 32, 32, 1)     4           input_2[0][0]                    
____________________________________________________________________________________________________
conv2d_6 (Conv2D)                (None, 28, 28, 32)    832         batch_normalization_2[0][0]      
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)   (None, 14, 14, 32)    0           conv2d_6[0][0]                   
____________________________________________________________________________________________________
dropout_5 (Dropout)              (None, 14, 14, 32)    0           max_pooling2d_3[0][0]            
____________________________________________________________________________________________________
conv2d_7 (Conv2D)                (None, 10, 10, 64)    51264       dropout_5[0][0]                  
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)   (None, 5, 5, 64)      0           conv2d_7[0][0]                   
____________________________________________________________________________________________________
dropout_6 (Dropout)              (None, 5, 5, 64)      0           max_pooling2d_4[0][0]            
____________________________________________________________________________________________________
flatten_2 (Flatten)              (None, 1600)          0           dropout_6[0][0]                  
____________________________________________________________________________________________________
dense_7 (Dense)                  (None, 256)           409856      flatten_2[0][0]                  
____________________________________________________________________________________________________
dropout_7 (Dropout)              (None, 256)           0           dense_7[0][0]                    
____________________________________________________________________________________________________
dense_8 (Dense)                  (None, 11)            2827        dropout_7[0][0]                  
____________________________________________________________________________________________________
dense_9 (Dense)                  (None, 11)            2827        dropout_7[0][0]                  
____________________________________________________________________________________________________
dense_10 (Dense)                 (None, 11)            2827        dropout_7[0][0]                  
____________________________________________________________________________________________________
dense_11 (Dense)                 (None, 11)            2827        dropout_7[0][0]                  
____________________________________________________________________________________________________
dense_12 (Dense)                 (None, 11)            2827        dropout_7[0][0]                  
====================================================================================================
Total params: 476,091
Trainable params: 476,089
Non-trainable params: 2
____________________________________________________________________________________________________
None
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hide_code</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">4</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">history2</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_8_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;First digit&#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">history2</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_9_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Second digit&#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">history2</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_10_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Third digit&#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">history2</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_11_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Fourth digit&#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">history2</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_dense_12_acc&#39;</span><span class="p">],</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Fifth digit&#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="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">);</span>
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<span class="n">avg_accuracy2</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">cnn_scores2</span><span class="p">[</span><span class="n">i</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">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">)])</span><span class="o">/</span><span class="mi">5</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;CNN Model 2. Average Accuracy: </span><span class="si">%.2f%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">avg_accuracy2</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>CNN Model 2. Average Accuracy: 95.23%
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<h4 id="Save-the-Models">Save the Models<a class="anchor-link" href="#Save-the-Models">&#182;</a></h4>
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<span class="n">cnn_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;cnn_model.h5&#39;</span><span class="p">)</span>
<span class="n">cnn_model2</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;cnn_model2.h5&#39;</span><span class="p">)</span>
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<h2 id="Questions-and-Answers">Questions and Answers<a class="anchor-link" href="#Questions-and-Answers">&#182;</a></h2>
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<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><p><em>Describe how you set up the training and testing data for your model. How does the model perform on a realistic dataset?</em></p>
<h3 id="Answer-4">Answer 4<a class="anchor-link" href="#Answer-4">&#182;</a></h3><p>Tens of thousands of photos were processed as follows:</p>
<ul>
<li>fields of photos that do not contain digits were cut off;</li>
<li>the photos were formatted to the standard 32X32 size and three color channels were converted into one channel (gray scale);</li>
<li>each of the resulting images was represented as an array of numbers;</li>
<li>two sets of data (training and test data) were made from these arrays; </li>
<li>the data were recorded in files of two different formats for the convenience of repeated use.</li>
</ul>
<p>I have found two ways to impove the model for the best results on a realistic dataset:</p>
<ul>
<li>the first model shows the average accuracy 95.35%;</li>
<li>the second model shows the average accuracy 95.23%.</li>
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<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><p><em>What changes did you have to make, if any, to achieve "good" results? Were there any options you explored that made the results worse?</em></p>
<h3 id="Answer-5">Answer 5<a class="anchor-link" href="#Answer-5">&#182;</a></h3><p>The architecture of the model were changed.</p>
<ul>
<li><p>In the first case:</p>
<ul>
<li>the layer <em>Conv2D(128, (3, 3), activation='relu')</em> was added;</li>
<li><em>Dropout(0.25)</em>, <em>Dropout(0.25)</em>, <em>Dropout(0.25)</em>, <em>Dropout(0.5)</em> layers were used instead of <em>Dropout(0.2)</em>, <em>Dropout(0.2)</em>, <em>Dropout(0.4)</em>.</li>
</ul>
</li>
<li><p>In the second case, there is a big difference between the initial model and the starter one:</p>
<ul>
<li>two convolutional layers <em>Conv2D(32, (5, 5), activation='relu')</em> and <em>Conv2D(64, (5, 5), activation='relu')</em> instead of four <em>Conv2D(32, (3, 3), activation='relu')</em>, <em>Conv2D(32, (3, 3), activation='relu')</em>, <em>Conv2D(64, (3, 3), activation='relu')</em>, <em>Conv2D(64, (3, 3), activation='relu')</em>; </li>
<li>the <em>BatchNormalization()</em> layer was added;</li>
<li>the first <em>Dense</em> layer has dimensionality [256] instead of [512];</li>
<li><em>Dropout(0.25)</em>, <em>Dropout(0.25)</em>, <em>Dropout(0.5)</em> layers instead of <em>Dropout(0.2)</em>, <em>Dropout(0.2)</em>, <em>Dropout(0.4)</em>.</li>
</ul>
</li>
</ul>
<p>The number of the epochs became greater (50 and 75 instead of 20).</p>
<p>In both cases the result became worse with reducing the batch size (for example, 128 -&gt; 64).</p>

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<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><p><em>What were your initial and final results with testing on a realistic dataset? Do you believe your model is doing a good enough job at classifying numbers correctly?</em></p>
<h3 id="Answer-6">Answer 6<a class="anchor-link" href="#Answer-6">&#182;</a></h3><p>The initial values of accuracy were in 79-86%. The final level about 95% is an advanced result for the real data.
So the model can do a good job at classifying numbers. Of course, the result can be improved by further research to achieve even higher accuracy.</p>

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