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40523 lines
2.3 MiB
40523 lines
2.3 MiB
<!DOCTYPE html>
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<html>
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<head><meta charset="utf-8" />
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<title>dlnd_face_generation</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
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<style type="text/css">
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/*!
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*
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* Twitter Bootstrap
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*
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*/
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/*!
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* Bootstrap v3.3.7 (http://getbootstrap.com)
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* Copyright 2011-2016 Twitter, Inc.
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* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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*/
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
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font-family: sans-serif;
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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;
|
|
padding-top: 11px;
|
|
padding-bottom: 3px;
|
|
}
|
|
#maintoolbar .navbar-text {
|
|
float: none;
|
|
vertical-align: middle;
|
|
text-align: right;
|
|
margin-left: 5px;
|
|
margin-right: 0px;
|
|
margin-top: 0px;
|
|
}
|
|
.select-xs {
|
|
height: 24px;
|
|
}
|
|
.pulse,
|
|
.dropdown-menu > li > a.pulse,
|
|
li.pulse > a.dropdown-toggle,
|
|
li.pulse.open > a.dropdown-toggle {
|
|
background-color: #F37626;
|
|
color: white;
|
|
}
|
|
/**
|
|
* Primary styles
|
|
*
|
|
* Author: Jupyter Development Team
|
|
*/
|
|
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
|
|
* of chance of beeing generated from the ../less/[samename].less file, you can
|
|
* try to get back the less file by reverting somme commit in history
|
|
**/
|
|
/*
|
|
* We'll try to get something pretty, so we
|
|
* have some strange css to have the scroll bar on
|
|
* the left with fix button on the top right of the tooltip
|
|
*/
|
|
@-moz-keyframes fadeOut {
|
|
from {
|
|
opacity: 1;
|
|
}
|
|
to {
|
|
opacity: 0;
|
|
}
|
|
}
|
|
@-webkit-keyframes fadeOut {
|
|
from {
|
|
opacity: 1;
|
|
}
|
|
to {
|
|
opacity: 0;
|
|
}
|
|
}
|
|
@-moz-keyframes fadeIn {
|
|
from {
|
|
opacity: 0;
|
|
}
|
|
to {
|
|
opacity: 1;
|
|
}
|
|
}
|
|
@-webkit-keyframes fadeIn {
|
|
from {
|
|
opacity: 0;
|
|
}
|
|
to {
|
|
opacity: 1;
|
|
}
|
|
}
|
|
/*properties of tooltip after "expand"*/
|
|
.bigtooltip {
|
|
overflow: auto;
|
|
height: 200px;
|
|
-webkit-transition-property: height;
|
|
-webkit-transition-duration: 500ms;
|
|
-moz-transition-property: height;
|
|
-moz-transition-duration: 500ms;
|
|
transition-property: height;
|
|
transition-duration: 500ms;
|
|
}
|
|
/*properties of tooltip before "expand"*/
|
|
.smalltooltip {
|
|
-webkit-transition-property: height;
|
|
-webkit-transition-duration: 500ms;
|
|
-moz-transition-property: height;
|
|
-moz-transition-duration: 500ms;
|
|
transition-property: height;
|
|
transition-duration: 500ms;
|
|
text-overflow: ellipsis;
|
|
overflow: hidden;
|
|
height: 80px;
|
|
}
|
|
.tooltipbuttons {
|
|
position: absolute;
|
|
padding-right: 15px;
|
|
top: 0px;
|
|
right: 0px;
|
|
}
|
|
.tooltiptext {
|
|
/*avoid the button to overlap on some docstring*/
|
|
padding-right: 30px;
|
|
}
|
|
.ipython_tooltip {
|
|
max-width: 700px;
|
|
/*fade-in animation when inserted*/
|
|
-webkit-animation: fadeOut 400ms;
|
|
-moz-animation: fadeOut 400ms;
|
|
animation: fadeOut 400ms;
|
|
-webkit-animation: fadeIn 400ms;
|
|
-moz-animation: fadeIn 400ms;
|
|
animation: fadeIn 400ms;
|
|
vertical-align: middle;
|
|
background-color: #f7f7f7;
|
|
overflow: visible;
|
|
border: #ababab 1px solid;
|
|
outline: none;
|
|
padding: 3px;
|
|
margin: 0px;
|
|
padding-left: 7px;
|
|
font-family: monospace;
|
|
min-height: 50px;
|
|
-moz-box-shadow: 0px 6px 10px -1px #adadad;
|
|
-webkit-box-shadow: 0px 6px 10px -1px #adadad;
|
|
box-shadow: 0px 6px 10px -1px #adadad;
|
|
border-radius: 2px;
|
|
position: absolute;
|
|
z-index: 1000;
|
|
}
|
|
.ipython_tooltip a {
|
|
float: right;
|
|
}
|
|
.ipython_tooltip .tooltiptext pre {
|
|
border: 0;
|
|
border-radius: 0;
|
|
font-size: 100%;
|
|
background-color: #f7f7f7;
|
|
}
|
|
.pretooltiparrow {
|
|
left: 0px;
|
|
margin: 0px;
|
|
top: -16px;
|
|
width: 40px;
|
|
height: 16px;
|
|
overflow: hidden;
|
|
position: absolute;
|
|
}
|
|
.pretooltiparrow:before {
|
|
background-color: #f7f7f7;
|
|
border: 1px #ababab solid;
|
|
z-index: 11;
|
|
content: "";
|
|
position: absolute;
|
|
left: 15px;
|
|
top: 10px;
|
|
width: 25px;
|
|
height: 25px;
|
|
-webkit-transform: rotate(45deg);
|
|
-moz-transform: rotate(45deg);
|
|
-ms-transform: rotate(45deg);
|
|
-o-transform: rotate(45deg);
|
|
}
|
|
ul.typeahead-list i {
|
|
margin-left: -10px;
|
|
width: 18px;
|
|
}
|
|
ul.typeahead-list {
|
|
max-height: 80vh;
|
|
overflow: auto;
|
|
}
|
|
ul.typeahead-list > li > a {
|
|
/** Firefox bug **/
|
|
/* see https://github.com/jupyter/notebook/issues/559 */
|
|
white-space: normal;
|
|
}
|
|
.cmd-palette .modal-body {
|
|
padding: 7px;
|
|
}
|
|
.cmd-palette form {
|
|
background: white;
|
|
}
|
|
.cmd-palette input {
|
|
outline: none;
|
|
}
|
|
.no-shortcut {
|
|
display: none;
|
|
}
|
|
.command-shortcut:before {
|
|
content: "(command)";
|
|
padding-right: 3px;
|
|
color: #777777;
|
|
}
|
|
.edit-shortcut:before {
|
|
content: "(edit)";
|
|
padding-right: 3px;
|
|
color: #777777;
|
|
}
|
|
#find-and-replace #replace-preview .match,
|
|
#find-and-replace #replace-preview .insert {
|
|
background-color: #BBDEFB;
|
|
border-color: #90CAF9;
|
|
border-style: solid;
|
|
border-width: 1px;
|
|
border-radius: 0px;
|
|
}
|
|
#find-and-replace #replace-preview .replace .match {
|
|
background-color: #FFCDD2;
|
|
border-color: #EF9A9A;
|
|
border-radius: 0px;
|
|
}
|
|
#find-and-replace #replace-preview .replace .insert {
|
|
background-color: #C8E6C9;
|
|
border-color: #A5D6A7;
|
|
border-radius: 0px;
|
|
}
|
|
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<h1 id="Face-Generation">Face Generation<a class="anchor-link" href="#Face-Generation">¶</a></h1><p>In this project, you'll use generative adversarial networks to generate new images of faces.</p>
|
|
<h3 id="Get-the-Data">Get the Data<a class="anchor-link" href="#Get-the-Data">¶</a></h3><p>You'll be using two datasets in this project:</p>
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<ul>
|
|
<li>MNIST</li>
|
|
<li>CelebA</li>
|
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</ul>
|
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<p>Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.</p>
|
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<p>If you're using <a href="https://www.floydhub.com/">FloydHub</a>, set <code>data_dir</code> to "/input" and use the <a href="http://docs.floydhub.com/home/using_datasets/">FloydHub data ID</a> "R5KrjnANiKVhLWAkpXhNBe".</p>
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</div>
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</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="input">
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<div class="prompt input_prompt">In [2]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#data_dir = './data'</span>
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<span class="n">data_dir</span> <span class="o">=</span> <span class="s1">'/input'</span>
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<span class="c1"># FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"</span>
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<span class="c1">#data_dir = '/input'</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL</span>
|
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<span class="sd">"""</span>
|
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<span class="kn">import</span> <span class="nn">helper</span>
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<span class="n">helper</span><span class="o">.</span><span class="n">download_extract</span><span class="p">(</span><span class="s1">'mnist'</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">)</span>
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<span class="n">helper</span><span class="o">.</span><span class="n">download_extract</span><span class="p">(</span><span class="s1">'celeba'</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Found mnist Data
|
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Found celeba Data
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</pre>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<div class="text_cell_render border-box-sizing rendered_html">
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<h2 id="Explore-the-Data">Explore the Data<a class="anchor-link" href="#Explore-the-Data">¶</a></h2><h3 id="MNIST">MNIST<a class="anchor-link" href="#MNIST">¶</a></h3><p>As you're aware, the <a href="http://yann.lecun.com/exdb/mnist/">MNIST</a> dataset contains images of handwritten digits. You can view the first number of examples by changing <code>show_n_images</code>.</p>
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</div>
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</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="input">
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<div class="prompt input_prompt">In [3]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_n_images</span> <span class="o">=</span> <span class="mi">25</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL</span>
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<span class="sd">"""</span>
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<span class="o">%</span><span class="k">matplotlib</span> inline
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<span class="kn">import</span> <span class="nn">os</span>
|
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<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>
|
|
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span>
|
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|
|
<span class="n">mnist_images</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">'mnist/*.jpg'</span><span class="p">))[:</span><span class="n">show_n_images</span><span class="p">],</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="s1">'L'</span><span class="p">)</span>
|
|
<span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">mnist_images</span><span class="p">,</span> <span class="s1">'L'</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'gray'</span><span class="p">)</span>
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</pre></div>
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<div class="prompt output_prompt">Out[3]:</div>
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<pre><matplotlib.image.AxesImage at 0x7fd45b2809b0></pre>
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6O1JYSuAC+JaaD8cpcuaTq7pNowTBKESwG4ielz9tAbArfHvt8LRNaQVRLSQiIYQ0flw2vkfRHQT
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gA0Apmey7Hj5nwCoNsYMjx+6CsAunIa2w9k2jDbG5MafAZd9WtoeR7J2rgHw9bgVYjSAWrXNSAuM
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MZPgbBuvJyIdb28NgJnGmIAxZhgcZed/prPsbqG3lRoAroWjkd0HYFGGy/oSHLFxB4C/xT/Xwtnb
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rwfwXwDeBjAww/X4VwCvx7+XwhkIHwN4GUAgg+VeDOD9ePt/D6DwdLUdwAMA9gDYCeA5AIFMtR3A
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i3B0F61wJKRvJGsnHGXvk/HxVwXHQpLusj+GozvgMfeUOn9RvOyPAEzO5LhL9WMZjRYWFi709vbB
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wsLiDIOdFCwsLFywk4KFhYULdlKwsLBwwU4KFhYWLthJwcLCwgU7KVhYWLhgJwULCwsX/geTBSdf
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FmBaZwAAAABJRU5ErkJggg==
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"
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>
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<h3 id="CelebA">CelebA<a class="anchor-link" href="#CelebA">¶</a></h3><p>The <a href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html">CelebFaces Attributes Dataset (CelebA)</a> dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing <code>show_n_images</code>.</p>
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<div class="prompt input_prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_n_images</span> <span class="o">=</span> <span class="mi">25</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL</span>
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<span class="sd">"""</span>
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<span class="n">mnist_images</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">get_batch</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">'img_align_celeba/*.jpg'</span><span class="p">))[:</span><span class="n">show_n_images</span><span class="p">],</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span>
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<span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">mnist_images</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">))</span>
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</pre></div>
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<div class="prompt output_prompt">Out[4]:</div>
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<pre><matplotlib.image.AxesImage at 0x7fd45b17db70></pre>
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bZ6YYO9vy/57HZMhea/HvoKJHeR77l/f8/8/J2O/DXzyKPfdg/6UiMZSSinlPnnU7kMppZRyzKRU
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CqWUUsp9UiqFUkop5T4plUIppZRyn5RKoZRSSrlPSqVQSiml3CelUiillFLuk1IplFJKKffJ/wGA
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P7E6eb8E6wAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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</div>
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</div>
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</div>
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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</div>
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<div class="inner_cell">
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<div class="text_cell_render border-box-sizing rendered_html">
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<h2 id="Preprocess-the-Data">Preprocess the Data<a class="anchor-link" href="#Preprocess-the-Data">¶</a></h2><p>Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.</p>
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|
<p>The MNIST images are black and white images with a single <a href="https://en.wikipedia.org/wiki/Channel_(digital_image%29">color channel</a> while the CelebA images have <a href="https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images">3 color channels (RGB color channel)</a>.</p>
|
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<h2 id="Build-the-Neural-Network">Build the Neural Network<a class="anchor-link" href="#Build-the-Neural-Network">¶</a></h2><p>You'll build the components necessary to build a GANs by implementing the following functions below:</p>
|
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<ul>
|
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<li><code>model_inputs</code></li>
|
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<li><code>discriminator</code></li>
|
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<li><code>generator</code></li>
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<li><code>model_loss</code></li>
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<li><code>model_opt</code></li>
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<li><code>train</code></li>
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</ul>
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<h3 id="Check-the-Version-of-TensorFlow-and-Access-to-GPU">Check the Version of TensorFlow and Access to GPU<a class="anchor-link" href="#Check-the-Version-of-TensorFlow-and-Access-to-GPU">¶</a></h3><p>This will check to make sure you have the correct version of TensorFlow and access to a GPU</p>
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</div>
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</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="input">
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<div class="prompt input_prompt">In [5]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL</span>
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<span class="sd">"""</span>
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<span class="kn">from</span> <span class="nn">distutils.version</span> <span class="k">import</span> <span class="n">LooseVersion</span>
|
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<span class="kn">import</span> <span class="nn">warnings</span>
|
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<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
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<span class="c1"># Check TensorFlow Version</span>
|
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<span class="k">assert</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">>=</span> <span class="n">LooseVersion</span><span class="p">(</span><span class="s1">'1.0'</span><span class="p">),</span> <span class="s1">'Please use TensorFlow version 1.0 or newer. You are using </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
|
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<span class="nb">print</span><span class="p">(</span><span class="s1">'TensorFlow Version: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">))</span>
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<span class="c1"># Check for a GPU</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">():</span>
|
|
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'No GPU found. Please use a GPU to train your neural network.'</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Default GPU Device: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">gpu_device_name</span><span class="p">()))</span>
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</pre></div>
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</div>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>TensorFlow Version: 1.2.1
|
|
Default GPU Device: /gpu:0
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</pre>
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<h3 id="Input">Input<a class="anchor-link" href="#Input">¶</a></h3><p>Implement the <code>model_inputs</code> function to create TF Placeholders for the Neural Network. It should create the following placeholders:</p>
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<ul>
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<li>Real input images placeholder with rank 4 using <code>image_width</code>, <code>image_height</code>, and <code>image_channels</code>.</li>
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<li>Z input placeholder with rank 2 using <code>z_dim</code>.</li>
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<li>Learning rate placeholder with rank 0.</li>
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</ul>
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<p>Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)</p>
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<div class="prompt input_prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
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<span class="k">def</span> <span class="nf">model_inputs</span><span class="p">(</span><span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Create the model inputs</span>
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<span class="sd"> :param image_width: The input image width</span>
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<span class="sd"> :param image_height: The input image height</span>
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<span class="sd"> :param image_channels: The number of image channels</span>
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<span class="sd"> :param z_dim: The dimension of Z</span>
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<span class="sd"> :return: Tuple of (tensor of real input images, tensor of z data, learning rate)</span>
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<span class="sd"> """</span>
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<span class="n">inputs_real</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">image_channels</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'inputs_real'</span><span class="p">)</span>
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<span class="n">inputs_z</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'inputs_z'</span><span class="p">)</span>
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<span class="n">learn_r</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'lr'</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">inputs_real</span><span class="p">,</span> <span class="n">inputs_z</span><span class="p">,</span> <span class="n">learn_r</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">tests</span><span class="o">.</span><span class="n">test_model_inputs</span><span class="p">(</span><span class="n">model_inputs</span><span class="p">)</span>
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</pre></div>
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<pre>ERROR:tensorflow:==================================
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Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
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<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
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If you want to mark it as used call its "mark_used()" method.
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It was originally created here:
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['File "/usr/local/lib/python3.5/runpy.py", line 193, in _run_module_as_main\n "__main__", mod_spec)', 'File "/usr/local/lib/python3.5/runpy.py", line 85, in _run_code\n exec(code, run_globals)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py", line 16, in <module>\n app.launch_new_instance()', 'File "/usr/local/lib/python3.5/site-packages/traitlets/config/application.py", line 658, in launch_instance\n app.start()', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 477, in start\n ioloop.IOLoop.instance().start()', 'File "/usr/local/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n super(ZMQIOLoop, self).start()', 'File "/usr/local/lib/python3.5/site-packages/tornado/ioloop.py", line 888, in start\n handler_func(fd_obj, events)', 'File "/usr/local/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n self._handle_recv()', 'File "/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n self._run_callback(callback, msg)', 'File "/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n callback(*args, **kwargs)', 'File "/usr/local/lib/python3.5/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n handler(stream, idents, msg)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n user_expressions, allow_stdin)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/usr/local/lib/python3.5/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)', 'File "/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n if self.run_code(code, result):', 'File "/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-6-60e8f0e9d266>", line 22, in <module>\n tests.test_model_inputs(model_inputs)', 'File "/output/problem_unittests.py", line 12, in func_wrapper\n result = func(*args)', 'File "/output/problem_unittests.py", line 68, in test_model_inputs\n _check_input(learn_rate, [], \'Learning Rate\')', 'File "/output/problem_unittests.py", line 34, in _check_input\n _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/output/problem_unittests.py", line 20, in _assert_tensor_shape\n assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n dynamic_condition, data, summarize)', 'File "/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n return _add_should_use_warning(fn(*args, **kwargs))', 'File "/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n wrapped = TFShouldUseWarningWrapper(x)', 'File "/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n stack = [s.strip() for s in traceback.format_stack()]']
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==================================
|
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Tests Passed
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</pre>
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<h3 id="Hyperparameters">Hyperparameters<a class="anchor-link" href="#Hyperparameters">¶</a></h3>
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<div class="prompt input_prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.1</span> <span class="c1"># for leaky relu</span>
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<span class="n">print_every</span> <span class="o">=</span> <span class="mi">10</span>
|
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<span class="n">show_every</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1">#change to 100</span>
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</pre></div>
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<h3 id="Discriminator">Discriminator<a class="anchor-link" href="#Discriminator">¶</a></h3><p>Implement <code>discriminator</code> to create a discriminator neural network that discriminates on <code>images</code>. This function should be able to reuse the variables in the neural network. Use <a href="https://www.tensorflow.org/api_docs/python/tf/variable_scope"><code>tf.variable_scope</code></a> with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).</p>
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<div class="prompt input_prompt">In [8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">discriminator</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
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<span class="sd">"""</span>
|
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<span class="sd"> Create the discriminator network</span>
|
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<span class="sd"> :param images: Tensor of input image(s)</span>
|
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<span class="sd"> :param reuse: Boolean if the weights should be reused</span>
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<span class="sd"> :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)</span>
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<span class="sd"> """</span>
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<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s1">'discriminator'</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="n">reuse</span><span class="p">):</span>
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<span class="c1"># Input layer is 28x28x3</span>
|
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<span class="n">x1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
|
|
<span class="n">relu1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">x1</span><span class="p">,</span> <span class="n">x1</span><span class="p">)</span>
|
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<span class="c1">#print(relu1)</span>
|
|
<span class="c1"># 24x24x64</span>
|
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|
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<span class="n">x2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">relu1</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
|
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<span class="n">bn2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
|
<span class="n">relu2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">bn2</span><span class="p">,</span> <span class="n">bn2</span><span class="p">)</span>
|
|
<span class="c1">#print(relu2)</span>
|
|
<span class="c1"># 20x20x128</span>
|
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|
|
<span class="n">x3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">relu2</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
|
|
<span class="n">bn3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x3</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
|
<span class="n">relu3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">bn3</span><span class="p">,</span> <span class="n">bn3</span><span class="p">)</span>
|
|
<span class="c1">#print(relu3)</span>
|
|
<span class="c1"># 16x16x256</span>
|
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|
|
<span class="n">x4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">relu3</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'valid'</span><span class="p">)</span>
|
|
<span class="n">bn4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">x4</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
|
<span class="n">relu4</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">bn4</span><span class="p">,</span> <span class="n">bn4</span><span class="p">)</span>
|
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<span class="c1">#print(relu4)</span>
|
|
<span class="c1"># 6x6x512</span>
|
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|
|
<span class="c1"># Flatten it</span>
|
|
<span class="n">flat</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">relu4</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="o">*</span><span class="mi">6</span><span class="o">*</span><span class="mi">512</span><span class="p">))</span>
|
|
<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">flat</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
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<span class="c1">#print(flat)</span>
|
|
<span class="n">out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">logits</span>
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<span class="sd">"""</span>
|
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
|
|
<span class="sd">"""</span>
|
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<span class="n">tests</span><span class="o">.</span><span class="n">test_discriminator</span><span class="p">(</span><span class="n">discriminator</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
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<pre>Tests Passed
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<h3 id="Generator">Generator<a class="anchor-link" href="#Generator">¶</a></h3><p>Implement <code>generator</code> to generate an image using <code>z</code>. This function should be able to reuse the variables in the neural network. Use <a href="https://www.tensorflow.org/api_docs/python/tf/variable_scope"><code>tf.variable_scope</code></a> with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x <code>out_channel_dim</code> images.</p>
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<div class="prompt input_prompt">In [93]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">generator</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Create the generator network</span>
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<span class="sd"> :param z: Input z</span>
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<span class="sd"> :param out_channel_dim: The number of channels in the output image</span>
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<span class="sd"> :param is_train: Boolean if generator is being used for training</span>
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<span class="sd"> :return: The tensor output of the generator</span>
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<span class="sd"> """</span>
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<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">variable_scope</span><span class="p">(</span><span class="s1">'generator'</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="ow">not</span> <span class="n">is_train</span><span class="p">):</span>
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<span class="c1"># First fully connected layer</span>
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<span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="mi">7</span><span class="o">*</span><span class="mi">7</span><span class="o">*</span><span class="mi">1024</span><span class="p">)</span>
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<span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">fc</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1024</span><span class="p">))</span>
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<span class="n">fc</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">fc</span><span class="p">,</span> <span class="n">fc</span><span class="p">)</span>
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<span class="c1">#print(fc)</span>
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<span class="c1"># 7x7x1024 now</span>
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<span class="c1"># Reshape it to start the convolutional stack</span>
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<span class="c1">#x1 = tf.layers.batch_normalization(x1, training=is_train)</span>
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<span class="c1"># Deconv1</span>
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<span class="n">dconv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">fc</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">)</span>
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<span class="n">dconv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">dconv1</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
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<span class="n">dconv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">dconv1</span><span class="p">,</span> <span class="n">dconv1</span><span class="p">)</span>
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<span class="c1">#print(dconv1)</span>
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<span class="c1"># 14x14x512 now</span>
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<span class="n">dconv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">dconv1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">)</span>
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<span class="n">dconv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">dconv2</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
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<span class="n">dconv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">dconv2</span><span class="p">,</span> <span class="n">dconv2</span><span class="p">)</span>
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<span class="c1">#print(dconv2)</span>
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<span class="c1"># 28x28x256 now</span>
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<span class="n">dconv3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">dconv2</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">)</span>
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<span class="n">dconv3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_normalization</span><span class="p">(</span><span class="n">dconv3</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">is_train</span><span class="p">)</span>
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<span class="n">dconv3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">dconv3</span><span class="p">,</span> <span class="n">dconv3</span><span class="p">)</span>
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<span class="c1">#print(dconv3)</span>
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<span class="c1"># 28x28x128 now</span>
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<span class="c1"># Output layer</span>
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<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d_transpose</span><span class="p">(</span><span class="n">dconv3</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">)</span>
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<span class="c1">#print(logits)</span>
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<span class="c1"># 28x28x(channels) now</span>
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<span class="n">out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">out</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">tests</span><span class="o">.</span><span class="n">test_generator</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
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<h3 id="Loss">Loss<a class="anchor-link" href="#Loss">¶</a></h3><p>Implement <code>model_loss</code> to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:</p>
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<ul>
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<li><code>discriminator(images, reuse=False)</code></li>
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<li><code>generator(z, out_channel_dim, is_train=True)</code></li>
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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model_loss</span><span class="p">(</span><span class="n">input_real</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Get the loss for the discriminator and generator</span>
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<span class="sd"> :param input_real: Images from the real dataset</span>
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<span class="sd"> :param input_z: Z input</span>
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<span class="sd"> :param out_channel_dim: The number of channels in the output image</span>
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<span class="sd"> :return: A tuple of (discriminator loss, generator loss)</span>
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<span class="sd"> """</span>
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<span class="n">g_model</span> <span class="o">=</span> <span class="n">generator</span><span class="p">(</span><span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
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<span class="n">d_model_real</span><span class="p">,</span> <span class="n">d_logits_real</span> <span class="o">=</span> <span class="n">discriminator</span><span class="p">(</span><span class="n">input_real</span><span class="p">)</span>
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<span class="n">d_model_fake</span><span class="p">,</span> <span class="n">d_logits_fake</span> <span class="o">=</span> <span class="n">discriminator</span><span class="p">(</span><span class="n">g_model</span><span class="p">,</span> <span class="n">reuse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">d_loss_real</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span>
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<span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_real</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">d_model_real</span><span class="p">)))</span>
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<span class="n">d_loss_fake</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span>
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<span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_fake</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">d_model_fake</span><span class="p">)))</span>
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<span class="n">g_loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span>
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<span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">sigmoid_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">d_logits_fake</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">d_model_fake</span><span class="p">)))</span>
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<span class="n">d_loss</span> <span class="o">=</span> <span class="n">d_loss_real</span> <span class="o">+</span> <span class="n">d_loss_fake</span>
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<span class="k">return</span> <span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">tests</span><span class="o">.</span><span class="n">test_model_loss</span><span class="p">(</span><span class="n">model_loss</span><span class="p">)</span>
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</pre></div>
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<pre>Tests Passed
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<h3 id="Optimization">Optimization<a class="anchor-link" href="#Optimization">¶</a></h3><p>Implement <code>model_opt</code> to create the optimization operations for the GANs. Use <a href="https://www.tensorflow.org/api_docs/python/tf/trainable_variables"><code>tf.trainable_variables</code></a> to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model_opt</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Get optimization operations</span>
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<span class="sd"> :param d_loss: Discriminator loss Tensor</span>
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<span class="sd"> :param g_loss: Generator loss Tensor</span>
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<span class="sd"> :param learning_rate: Learning Rate Placeholder</span>
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<span class="sd"> :param beta1: The exponential decay rate for the 1st moment in the optimizer</span>
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<span class="sd"> :return: A tuple of (discriminator training operation, generator training operation)</span>
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<span class="sd"> """</span>
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<span class="c1"># Get weights and bias to update</span>
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<span class="n">t_vars</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">()</span>
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<span class="n">d_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">t_vars</span> <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'discriminator'</span><span class="p">)]</span>
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<span class="n">g_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">t_vars</span> <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'generator'</span><span class="p">)]</span>
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<span class="c1"># Optimize</span>
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<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">control_dependencies</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">get_collection</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">GraphKeys</span><span class="o">.</span><span class="n">UPDATE_OPS</span><span class="p">)):</span>
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<span class="n">d_train_opt</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">var_list</span><span class="o">=</span><span class="n">d_vars</span><span class="p">)</span>
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<span class="n">g_train_opt</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="o">=</span><span class="n">beta1</span><span class="p">)</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">g_loss</span><span class="p">,</span> <span class="n">var_list</span><span class="o">=</span><span class="n">g_vars</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">d_train_opt</span><span class="p">,</span> <span class="n">g_train_opt</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">tests</span><span class="o">.</span><span class="n">test_model_opt</span><span class="p">(</span><span class="n">model_opt</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
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</pre>
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<h2 id="Neural-Network-Training">Neural Network Training<a class="anchor-link" href="#Neural-Network-Training">¶</a></h2><h3 id="Show-Output">Show Output<a class="anchor-link" href="#Show-Output">¶</a></h3><p>Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.</p>
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</div>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="prompt input_prompt">In [12]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="k">def</span> <span class="nf">show_generator_output</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">n_images</span><span class="p">,</span> <span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="n">image_mode</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Show example output for the generator</span>
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<span class="sd"> :param sess: TensorFlow session</span>
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<span class="sd"> :param n_images: Number of Images to display</span>
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<span class="sd"> :param input_z: Input Z Tensor</span>
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<span class="sd"> :param out_channel_dim: The number of channels in the output image</span>
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<span class="sd"> :param image_mode: The mode to use for images ("RGB" or "L")</span>
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<span class="sd"> """</span>
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<span class="n">cmap</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">image_mode</span> <span class="o">==</span> <span class="s1">'RGB'</span> <span class="k">else</span> <span class="s1">'gray'</span>
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<span class="n">z_dim</span> <span class="o">=</span> <span class="n">input_z</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="n">example_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">n_images</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">])</span>
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<span class="n">samples</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
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<span class="n">generator</span><span class="p">(</span><span class="n">input_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="kc">False</span><span class="p">),</span>
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<span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">input_z</span><span class="p">:</span> <span class="n">example_z</span><span class="p">})</span>
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<span class="n">images_grid</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">images_square_grid</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">image_mode</span><span class="p">)</span>
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<span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images_grid</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
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<span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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</pre></div>
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</div>
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</div>
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h3 id="Train">Train<a class="anchor-link" href="#Train">¶</a></h3><p>Implement <code>train</code> to build and train the GANs. Use the following functions you implemented:</p>
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<ul>
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<li><code>model_inputs(image_width, image_height, image_channels, z_dim)</code></li>
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<li><code>model_loss(input_real, input_z, out_channel_dim)</code></li>
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<li><code>model_opt(d_loss, g_loss, learning_rate, beta1)</code></li>
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</ul>
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<p>Use the <code>show_generator_output</code> to show <code>generator</code> output while you train. Running <code>show_generator_output</code> for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the <code>generator</code> output every 100 batches.</p>
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</div>
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</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="input">
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<div class="prompt input_prompt">In [13]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.core.debugger</span> <span class="k">import</span> <span class="n">Tracer</span>
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<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">epoch_count</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">get_batches</span><span class="p">,</span> <span class="n">data_shape</span><span class="p">,</span> <span class="n">data_image_mode</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Train the GAN</span>
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<span class="sd"> :param epoch_count: Number of epochs</span>
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<span class="sd"> :param batch_size: Batch Size</span>
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<span class="sd"> :param z_dim: Z dimension</span>
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<span class="sd"> :param learning_rate: Learning Rate</span>
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<span class="sd"> :param beta1: The exponential decay rate for the 1st moment in the optimizer</span>
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<span class="sd"> :param get_batches: Function to get batches</span>
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<span class="sd"> :param data_shape: Shape of the data</span>
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<span class="sd"> :param data_image_mode: The image mode to use for images ("RGB" or "L")</span>
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<span class="sd"> """</span>
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<span class="c1"># create input placeholders</span>
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<span class="c1">#print(data_shape)</span>
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|
<span class="n">image_shape</span> <span class="o">=</span> <span class="n">data_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
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<span class="c1">#print(image_shape)</span>
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<span class="n">out_channel_dim</span> <span class="o">=</span> <span class="n">image_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="c1">#print(out_channel_dim)</span>
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<span class="n">inputs_real</span><span class="p">,</span> <span class="n">inputs_z</span><span class="p">,</span> <span class="n">lr_rate</span> <span class="o">=</span> <span class="n">model_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">image_shape</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">)</span>
|
|
<span class="c1">#print(inputs_real)</span>
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|
<span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span> <span class="o">=</span> <span class="n">model_loss</span><span class="p">(</span><span class="n">inputs_real</span><span class="p">,</span> <span class="n">inputs_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">)</span>
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|
<span class="n">d_train_opt</span><span class="p">,</span> <span class="n">g_train_opt</span> <span class="o">=</span> <span class="n">model_opt</span><span class="p">(</span><span class="n">d_loss</span><span class="p">,</span> <span class="n">g_loss</span><span class="p">,</span> <span class="n">lr_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">)</span>
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<span class="c1">#print(inputs_real)</span>
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<span class="c1">#t_vars = tf.trainable_variables()</span>
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<span class="c1">#g_vars = [var for var in t_vars if var.name.startswith('generator')]</span>
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<span class="c1">#saver = tf.train.Saver(var_list=g_vars)</span>
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<span class="k">def</span> <span class="nf">scale</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">target_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)):</span>
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<span class="nb">min</span><span class="p">,</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">target_range</span>
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<span class="c1"># One liner, but too expensive </span>
|
|
<span class="c1">#x = (x - x.min()) * (max - min) / (x.max() - x.min()) + min</span>
|
|
|
|
<span class="c1"># First scale to [0,1]</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="p">((</span><span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">())</span><span class="o">/</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">()))</span>
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|
|
<span class="c1"># Then scale to target_range</span>
|
|
<span class="nb">min</span><span class="p">,</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">target_range</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="nb">max</span> <span class="o">-</span> <span class="nb">min</span><span class="p">)</span> <span class="o">+</span> <span class="nb">min</span>
|
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<span class="k">return</span> <span class="n">x</span>
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|
|
<span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span>
|
|
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
|
|
<span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
|
|
<span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epoch_count</span><span class="p">):</span>
|
|
<span class="k">for</span> <span class="n">batch_images</span> <span class="ow">in</span> <span class="n">get_batches</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span>
|
|
<span class="n">steps</span> <span class="o">+=</span> <span class="mi">1</span>
|
|
<span class="c1">#print("Step {} ...".format(steps))</span>
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|
|
<span class="c1">#1 Scale real images to [-1,1] range since that is also the generator output scale</span>
|
|
<span class="n">batch_images</span> <span class="o">=</span> <span class="n">scale</span><span class="p">(</span><span class="n">batch_images</span><span class="p">,</span> <span class="n">target_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
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|
|
<span class="c1">#3 Sample random noise for generator</span>
|
|
<span class="n">batch_z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">))</span>
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|
|
|
<span class="c1">#3 Run optimizers</span>
|
|
<span class="c1">#print(batch_images.shape)</span>
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|
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<span class="c1"># Optimize discriminator</span>
|
|
<span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">d_train_opt</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">inputs_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span>
|
|
<span class="n">inputs_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span>
|
|
<span class="n">lr_rate</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
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<span class="c1"># Optimize generator (Run twice to avoid faster convergence of D)</span>
|
|
<span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">g_train_opt</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">inputs_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span>
|
|
<span class="n">inputs_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span>
|
|
<span class="n">lr_rate</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
|
|
<span class="n">_</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">g_train_opt</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">inputs_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span>
|
|
<span class="n">inputs_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span>
|
|
<span class="n">lr_rate</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
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<span class="c1">#Tracer()() #this one triggers the debugger</span>
|
|
<span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
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|
|
<span class="n">d_train_loss</span> <span class="o">=</span> <span class="n">d_loss</span><span class="o">.</span><span class="n">eval</span><span class="p">({</span><span class="n">inputs_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span>
|
|
<span class="n">inputs_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span>
|
|
<span class="n">lr_rate</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
|
|
|
|
<span class="c1"># Optimize generator</span>
|
|
<span class="n">g_train_loss</span> <span class="o">=</span> <span class="n">g_loss</span><span class="o">.</span><span class="n">eval</span><span class="p">({</span><span class="n">inputs_z</span><span class="p">:</span> <span class="n">batch_z</span><span class="p">,</span>
|
|
<span class="n">inputs_real</span><span class="p">:</span> <span class="n">batch_images</span><span class="p">,</span>
|
|
<span class="n">lr_rate</span><span class="p">:</span> <span class="n">learning_rate</span><span class="p">})</span>
|
|
|
|
<span class="c1">#Tracer()() #this one triggers the debugger</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"Epoch </span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">-Step </span><span class="si">{}</span><span class="s2">..."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch_i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">epoch_count</span><span class="p">,</span><span class="n">steps</span><span class="p">),</span>
|
|
<span class="s2">"Discriminator Loss: </span><span class="si">{:.4f}</span><span class="s2">..."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">d_train_loss</span><span class="p">),</span>
|
|
<span class="s2">"Generator Loss: </span><span class="si">{:.4f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">g_train_loss</span><span class="p">))</span>
|
|
|
|
<span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="n">show_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
|
<span class="n">show_generator_output</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="n">inputs_z</span><span class="p">,</span> <span class="n">out_channel_dim</span><span class="p">,</span> <span class="n">data_image_mode</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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</div>
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</div>
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<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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</div>
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<div class="inner_cell">
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<div class="text_cell_render border-box-sizing rendered_html">
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<h3 id="MNIST">MNIST<a class="anchor-link" href="#MNIST">¶</a></h3><p>Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.</p>
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</div>
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</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="input">
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<div class="prompt input_prompt">In [76]:</div>
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
|
|
<span class="n">z_dim</span> <span class="o">=</span> <span class="mi">100</span>
|
|
<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.001</span>
|
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<span class="n">beta1</span> <span class="o">=</span> <span class="mf">0.5</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span>
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<span class="n">mnist_dataset</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="s1">'mnist'</span><span class="p">,</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">'mnist/*.jpg'</span><span class="p">)))</span>
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<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
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<span class="n">train</span><span class="p">(</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">mnist_dataset</span><span class="o">.</span><span class="n">get_batches</span><span class="p">,</span>
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<span class="n">mnist_dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">mnist_dataset</span><span class="o">.</span><span class="n">image_mode</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 10... Discriminator Loss: 0.9267... Generator Loss: 3.0303
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Epoch 1/2-Step 20... Discriminator Loss: 0.0128... Generator Loss: 5.2754
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Epoch 1/2-Step 30... Discriminator Loss: 1.0608... Generator Loss: 2.6022
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Epoch 1/2-Step 40... Discriminator Loss: 1.6446... Generator Loss: 1.1610
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Epoch 1/2-Step 50... Discriminator Loss: 1.0090... Generator Loss: 1.2422
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Epoch 1/2-Step 60... Discriminator Loss: 1.1629... Generator Loss: 1.4662
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Epoch 1/2-Step 70... Discriminator Loss: 1.5492... Generator Loss: 1.4174
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Epoch 1/2-Step 80... Discriminator Loss: 1.3179... Generator Loss: 0.9317
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Epoch 1/2-Step 90... Discriminator Loss: 1.7052... Generator Loss: 1.2884
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Epoch 1/2-Step 100... Discriminator Loss: 2.0016... Generator Loss: 0.2221
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</pre>
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</div>
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</div>
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rgO2muqx9jC3w4HvN9s7A7cCy4BvAq+d6vF1MY99gCXNe/RdYMuZfH+AvwUeBO4DLgdeO6j7E557
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QmaizQAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 110... Discriminator Loss: 1.0928... Generator Loss: 3.6356
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Epoch 1/2-Step 120... Discriminator Loss: 1.8290... Generator Loss: 0.5105
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Epoch 1/2-Step 130... Discriminator Loss: 1.8758... Generator Loss: 0.6536
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Epoch 1/2-Step 140... Discriminator Loss: 0.9872... Generator Loss: 1.5308
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Epoch 1/2-Step 150... Discriminator Loss: 0.8386... Generator Loss: 3.2762
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Epoch 1/2-Step 160... Discriminator Loss: 0.4610... Generator Loss: 1.8612
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Epoch 1/2-Step 170... Discriminator Loss: 0.8992... Generator Loss: 3.7001
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Epoch 1/2-Step 180... Discriminator Loss: 0.3389... Generator Loss: 3.2347
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Epoch 1/2-Step 190... Discriminator Loss: 1.2121... Generator Loss: 0.9055
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Epoch 1/2-Step 200... Discriminator Loss: 3.2045... Generator Loss: 0.0909
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</pre>
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</div>
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</div>
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<div class="output_area">
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FzB+sNvaRN/2Bm7uLW8CPADMB64BVhvs9jXQj+2AWb3X6AZgneF8fYDvAE8CjwM/B1Zr1/Vxyz3H
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AAAASUVORK5CYII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 210... Discriminator Loss: 1.3834... Generator Loss: 3.7705
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Epoch 1/2-Step 220... Discriminator Loss: 0.8444... Generator Loss: 1.6745
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Epoch 1/2-Step 230... Discriminator Loss: 0.3921... Generator Loss: 1.8625
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Epoch 1/2-Step 240... Discriminator Loss: 0.6757... Generator Loss: 1.7159
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Epoch 1/2-Step 250... Discriminator Loss: 0.8184... Generator Loss: 1.3769
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Epoch 1/2-Step 260... Discriminator Loss: 0.3848... Generator Loss: 2.3387
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Epoch 1/2-Step 270... Discriminator Loss: 0.4555... Generator Loss: 2.0942
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Epoch 1/2-Step 280... Discriminator Loss: 2.1406... Generator Loss: 0.5748
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Epoch 1/2-Step 290... Discriminator Loss: 0.2285... Generator Loss: 2.4981
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Epoch 1/2-Step 300... Discriminator Loss: 1.9429... Generator Loss: 0.4845
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</pre>
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</div>
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</div>
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<div class="output_area">
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RK5CYII=
|
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"
|
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>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 310... Discriminator Loss: 1.4466... Generator Loss: 1.9327
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Epoch 1/2-Step 320... Discriminator Loss: 0.9744... Generator Loss: 0.8699
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Epoch 1/2-Step 330... Discriminator Loss: 1.4074... Generator Loss: 1.3701
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Epoch 1/2-Step 340... Discriminator Loss: 1.0074... Generator Loss: 1.0729
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Epoch 1/2-Step 350... Discriminator Loss: 0.4718... Generator Loss: 2.2176
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Epoch 1/2-Step 360... Discriminator Loss: 1.0672... Generator Loss: 0.8503
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Epoch 1/2-Step 370... Discriminator Loss: 1.8639... Generator Loss: 0.4736
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Epoch 1/2-Step 380... Discriminator Loss: 1.0121... Generator Loss: 0.9073
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Epoch 1/2-Step 390... Discriminator Loss: 0.3627... Generator Loss: 4.4332
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Epoch 1/2-Step 400... Discriminator Loss: 2.8513... Generator Loss: 0.1151
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/2-Step 410... Discriminator Loss: 1.6384... Generator Loss: 0.4555
|
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Epoch 1/2-Step 420... Discriminator Loss: 1.0845... Generator Loss: 0.9582
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Epoch 1/2-Step 430... Discriminator Loss: 2.6283... Generator Loss: 0.2061
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Epoch 1/2-Step 440... Discriminator Loss: 2.0905... Generator Loss: 0.2952
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Epoch 1/2-Step 450... Discriminator Loss: 3.3657... Generator Loss: 0.1126
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Epoch 1/2-Step 460... Discriminator Loss: 0.8958... Generator Loss: 0.8718
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Epoch 1/2-Step 470... Discriminator Loss: 3.1746... Generator Loss: 0.0618
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Epoch 1/2-Step 480... Discriminator Loss: 1.5734... Generator Loss: 0.5139
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Epoch 1/2-Step 490... Discriminator Loss: 0.7358... Generator Loss: 2.6876
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|
Epoch 1/2-Step 500... Discriminator Loss: 1.1104... Generator Loss: 2.3262
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 510... Discriminator Loss: 1.3891... Generator Loss: 0.5434
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Epoch 1/2-Step 520... Discriminator Loss: 1.4872... Generator Loss: 0.8000
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Epoch 1/2-Step 530... Discriminator Loss: 0.7497... Generator Loss: 1.9807
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Epoch 1/2-Step 540... Discriminator Loss: 1.5252... Generator Loss: 1.0185
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Epoch 1/2-Step 550... Discriminator Loss: 1.4869... Generator Loss: 0.4923
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Epoch 1/2-Step 560... Discriminator Loss: 0.9228... Generator Loss: 2.2592
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Epoch 1/2-Step 570... Discriminator Loss: 0.8381... Generator Loss: 1.1396
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Epoch 1/2-Step 580... Discriminator Loss: 0.5408... Generator Loss: 1.6971
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Epoch 1/2-Step 590... Discriminator Loss: 3.1695... Generator Loss: 4.3885
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Epoch 1/2-Step 600... Discriminator Loss: 1.1400... Generator Loss: 1.2354
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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YKjIv/RInRTzDV4GTPPe/1RtuolCzkHooNyD3vvTvfeDvfdDKVyLSd77A+nQXIre+1eBl51zmxS7
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JDdkR14fmp3rMmeDxaeBZ4HngTNbbUCpceyjKaiJjwFTi/99msJ38URgOnAXsEarx1rHuY0Bbi62
|
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hwEPAs8BfwaWb/X4ajiPLYApxWt0A7B6J18f4BzgaeAJ4A/A8lldH/PcM4wuxIx7htGF2INvGF2I
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PfiG0YXYg28YXYg9+IbRhdiDbxhdiD34htGF2INvGF3I/wPopZQXlh5kHgAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 610... Discriminator Loss: 1.2808... Generator Loss: 0.7663
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Epoch 1/2-Step 620... Discriminator Loss: 1.1544... Generator Loss: 1.6539
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Epoch 1/2-Step 630... Discriminator Loss: 2.0728... Generator Loss: 0.2722
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Epoch 1/2-Step 640... Discriminator Loss: 2.4404... Generator Loss: 2.0143
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Epoch 1/2-Step 650... Discriminator Loss: 1.0015... Generator Loss: 1.1314
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Epoch 1/2-Step 660... Discriminator Loss: 2.2646... Generator Loss: 0.2215
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Epoch 1/2-Step 670... Discriminator Loss: 0.8644... Generator Loss: 1.3619
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Epoch 1/2-Step 680... Discriminator Loss: 2.0751... Generator Loss: 0.4561
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Epoch 1/2-Step 690... Discriminator Loss: 1.0712... Generator Loss: 1.1022
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Epoch 1/2-Step 700... Discriminator Loss: 0.8807... Generator Loss: 3.1525
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</pre>
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</div>
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</div>
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<div class="output_area">
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sKcAAAAASUVORK5CYII=
|
|
"
|
|
>
|
|
</div>
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|
|
</div>
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<div class="output_area">
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|
|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/2-Step 710... Discriminator Loss: 1.4505... Generator Loss: 1.3298
|
|
Epoch 1/2-Step 720... Discriminator Loss: 1.6401... Generator Loss: 1.3325
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|
Epoch 1/2-Step 730... Discriminator Loss: 2.1060... Generator Loss: 3.4476
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Epoch 1/2-Step 740... Discriminator Loss: 1.3121... Generator Loss: 0.8670
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Epoch 1/2-Step 750... Discriminator Loss: 1.2358... Generator Loss: 2.5686
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Epoch 1/2-Step 760... Discriminator Loss: 2.6190... Generator Loss: 0.1099
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Epoch 1/2-Step 770... Discriminator Loss: 1.5482... Generator Loss: 2.8853
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Epoch 1/2-Step 780... Discriminator Loss: 2.5531... Generator Loss: 0.1868
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Epoch 1/2-Step 790... Discriminator Loss: 1.1788... Generator Loss: 0.8387
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|
Epoch 1/2-Step 800... Discriminator Loss: 0.6670... Generator Loss: 1.3919
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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5b4iagAAAABJRU5ErkJggg==
|
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"
|
|
>
|
|
</div>
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</div>
|
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|
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<div class="output_area">
|
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|
|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/2-Step 810... Discriminator Loss: 1.8112... Generator Loss: 0.5763
|
|
Epoch 1/2-Step 820... Discriminator Loss: 1.4400... Generator Loss: 0.8454
|
|
Epoch 1/2-Step 830... Discriminator Loss: 2.1204... Generator Loss: 0.1975
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Epoch 1/2-Step 840... Discriminator Loss: 0.9309... Generator Loss: 0.8856
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|
Epoch 1/2-Step 850... Discriminator Loss: 2.2666... Generator Loss: 0.2093
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Epoch 1/2-Step 860... Discriminator Loss: 2.7204... Generator Loss: 0.2036
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Epoch 1/2-Step 870... Discriminator Loss: 1.4407... Generator Loss: 0.7119
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|
Epoch 1/2-Step 880... Discriminator Loss: 1.8585... Generator Loss: 0.3937
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|
Epoch 1/2-Step 890... Discriminator Loss: 1.1191... Generator Loss: 1.4012
|
|
Epoch 1/2-Step 900... Discriminator Loss: 1.9723... Generator Loss: 2.9803
|
|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 910... Discriminator Loss: 1.3118... Generator Loss: 2.4486
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Epoch 1/2-Step 920... Discriminator Loss: 1.2882... Generator Loss: 0.5410
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Epoch 1/2-Step 930... Discriminator Loss: 3.1985... Generator Loss: 0.1271
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Epoch 1/2-Step 940... Discriminator Loss: 2.2658... Generator Loss: 0.2157
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Epoch 1/2-Step 950... Discriminator Loss: 1.4683... Generator Loss: 0.6122
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Epoch 1/2-Step 960... Discriminator Loss: 1.3805... Generator Loss: 0.5894
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Epoch 1/2-Step 970... Discriminator Loss: 0.9084... Generator Loss: 1.3491
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Epoch 1/2-Step 980... Discriminator Loss: 1.1829... Generator Loss: 0.8846
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Epoch 1/2-Step 990... Discriminator Loss: 2.6936... Generator Loss: 0.1252
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Epoch 1/2-Step 1000... Discriminator Loss: 1.0920... Generator Loss: 3.3635
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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F+DxdHt94HVgOvAQsEJr96+A4+gPvJE+R48CHer5/AAXA+8Bk4C7gRXKdX7Mc88wGhAz7hlGA2I3
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YII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 1010... Discriminator Loss: 0.4717... Generator Loss: 2.0444
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Epoch 1/2-Step 1020... Discriminator Loss: 2.2474... Generator Loss: 3.6244
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Epoch 1/2-Step 1030... Discriminator Loss: 1.1826... Generator Loss: 1.1267
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Epoch 1/2-Step 1040... Discriminator Loss: 0.8570... Generator Loss: 1.7126
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Epoch 1/2-Step 1050... Discriminator Loss: 1.2136... Generator Loss: 0.6905
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Epoch 1/2-Step 1060... Discriminator Loss: 1.0117... Generator Loss: 0.8767
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Epoch 1/2-Step 1070... Discriminator Loss: 1.6421... Generator Loss: 0.4355
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Epoch 1/2-Step 1080... Discriminator Loss: 3.1265... Generator Loss: 0.1189
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Epoch 1/2-Step 1090... Discriminator Loss: 1.5463... Generator Loss: 0.5217
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Epoch 1/2-Step 1100... Discriminator Loss: 1.9496... Generator Loss: 0.3557
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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|
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|
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"
|
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>
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|
</div>
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</div>
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<div class="output_area">
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|
|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/2-Step 1110... Discriminator Loss: 1.0777... Generator Loss: 0.8982
|
|
Epoch 1/2-Step 1120... Discriminator Loss: 0.8473... Generator Loss: 2.6787
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Epoch 1/2-Step 1130... Discriminator Loss: 2.1421... Generator Loss: 0.4104
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Epoch 1/2-Step 1140... Discriminator Loss: 0.5819... Generator Loss: 1.3670
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Epoch 1/2-Step 1150... Discriminator Loss: 0.4012... Generator Loss: 3.8247
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Epoch 1/2-Step 1160... Discriminator Loss: 1.8693... Generator Loss: 0.3963
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Epoch 1/2-Step 1170... Discriminator Loss: 1.1938... Generator Loss: 0.7962
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Epoch 1/2-Step 1180... Discriminator Loss: 0.6501... Generator Loss: 1.2007
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Epoch 1/2-Step 1190... Discriminator Loss: 2.0899... Generator Loss: 0.3304
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Epoch 1/2-Step 1200... Discriminator Loss: 1.8512... Generator Loss: 0.6061
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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RK5CYII=
|
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"
|
|
>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/2-Step 1210... Discriminator Loss: 2.4600... Generator Loss: 0.2065
|
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Epoch 1/2-Step 1220... Discriminator Loss: 1.1053... Generator Loss: 1.4613
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Epoch 1/2-Step 1230... Discriminator Loss: 3.1921... Generator Loss: 0.1285
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Epoch 1/2-Step 1240... Discriminator Loss: 2.4057... Generator Loss: 0.2731
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Epoch 1/2-Step 1250... Discriminator Loss: 0.9394... Generator Loss: 2.9006
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Epoch 1/2-Step 1260... Discriminator Loss: 0.8145... Generator Loss: 1.2383
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Epoch 1/2-Step 1270... Discriminator Loss: 0.3363... Generator Loss: 1.9907
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Epoch 1/2-Step 1280... Discriminator Loss: 2.1134... Generator Loss: 0.2527
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|
Epoch 1/2-Step 1290... Discriminator Loss: 4.0813... Generator Loss: 0.0666
|
|
Epoch 1/2-Step 1300... Discriminator Loss: 0.5599... Generator Loss: 2.2968
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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rkJggg==
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/2-Step 1310... Discriminator Loss: 2.8760... Generator Loss: 0.2637
|
|
Epoch 1/2-Step 1320... Discriminator Loss: 1.3657... Generator Loss: 1.6037
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Epoch 1/2-Step 1330... Discriminator Loss: 1.3713... Generator Loss: 0.7570
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Epoch 1/2-Step 1340... Discriminator Loss: 2.7794... Generator Loss: 0.2296
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Epoch 1/2-Step 1350... Discriminator Loss: 1.6856... Generator Loss: 0.4993
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Epoch 1/2-Step 1360... Discriminator Loss: 0.5796... Generator Loss: 1.5256
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Epoch 1/2-Step 1370... Discriminator Loss: 1.6392... Generator Loss: 1.6606
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Epoch 1/2-Step 1380... Discriminator Loss: 0.8350... Generator Loss: 1.6210
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|
Epoch 1/2-Step 1390... Discriminator Loss: 2.5282... Generator Loss: 0.2095
|
|
Epoch 1/2-Step 1400... Discriminator Loss: 1.0859... Generator Loss: 1.1357
|
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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/jsgkesy/56qr0+aD/6LwNp5q+TC5AwVd6d4/JrI5xu8AnjLe/9X9dLd5HIOQhvlHvTen+a97+e9
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|
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jif+42ndugAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 1410... Discriminator Loss: 1.1798... Generator Loss: 0.9080
|
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Epoch 1/2-Step 1420... Discriminator Loss: 1.3249... Generator Loss: 0.7105
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Epoch 1/2-Step 1430... Discriminator Loss: 0.8106... Generator Loss: 1.0409
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Epoch 1/2-Step 1440... Discriminator Loss: 3.8837... Generator Loss: 0.0626
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Epoch 1/2-Step 1450... Discriminator Loss: 0.9882... Generator Loss: 0.8990
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Epoch 1/2-Step 1460... Discriminator Loss: 2.3555... Generator Loss: 0.2031
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Epoch 1/2-Step 1470... Discriminator Loss: 1.1362... Generator Loss: 0.7481
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Epoch 1/2-Step 1480... Discriminator Loss: 3.1776... Generator Loss: 0.1075
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Epoch 1/2-Step 1490... Discriminator Loss: 1.3520... Generator Loss: 1.5787
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Epoch 1/2-Step 1500... Discriminator Loss: 1.0190... Generator Loss: 1.8918
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
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|
</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/2-Step 1510... Discriminator Loss: 1.3130... Generator Loss: 3.2561
|
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Epoch 1/2-Step 1520... Discriminator Loss: 1.3796... Generator Loss: 0.5629
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Epoch 1/2-Step 1530... Discriminator Loss: 1.6151... Generator Loss: 0.6132
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Epoch 1/2-Step 1540... Discriminator Loss: 1.0203... Generator Loss: 1.5598
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Epoch 1/2-Step 1550... Discriminator Loss: 0.7105... Generator Loss: 1.3498
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Epoch 1/2-Step 1560... Discriminator Loss: 1.7961... Generator Loss: 0.4888
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Epoch 1/2-Step 1570... Discriminator Loss: 1.2594... Generator Loss: 2.2816
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Epoch 1/2-Step 1580... Discriminator Loss: 1.1150... Generator Loss: 0.7396
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|
Epoch 1/2-Step 1590... Discriminator Loss: 1.3450... Generator Loss: 0.7354
|
|
Epoch 1/2-Step 1600... Discriminator Loss: 1.4369... Generator Loss: 0.6458
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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RK5CYII=
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 1610... Discriminator Loss: 1.7995... Generator Loss: 0.3567
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Epoch 1/2-Step 1620... Discriminator Loss: 1.0228... Generator Loss: 0.8237
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Epoch 1/2-Step 1630... Discriminator Loss: 2.8634... Generator Loss: 0.1942
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Epoch 1/2-Step 1640... Discriminator Loss: 2.6316... Generator Loss: 0.2240
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Epoch 1/2-Step 1650... Discriminator Loss: 1.7795... Generator Loss: 4.5748
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Epoch 1/2-Step 1660... Discriminator Loss: 1.5611... Generator Loss: 0.6124
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Epoch 1/2-Step 1670... Discriminator Loss: 0.7054... Generator Loss: 1.4339
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Epoch 1/2-Step 1680... Discriminator Loss: 2.1514... Generator Loss: 0.3458
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Epoch 1/2-Step 1690... Discriminator Loss: 1.7364... Generator Loss: 0.4061
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Epoch 1/2-Step 1700... Discriminator Loss: 3.0349... Generator Loss: 0.1355
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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3zASyP8DHw5mJEVK4w4AAAAASUVORK5CYII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 1710... Discriminator Loss: 0.9496... Generator Loss: 0.8696
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Epoch 1/2-Step 1720... Discriminator Loss: 1.4395... Generator Loss: 0.6739
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Epoch 1/2-Step 1730... Discriminator Loss: 0.6664... Generator Loss: 1.6997
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Epoch 1/2-Step 1740... Discriminator Loss: 2.6954... Generator Loss: 0.2849
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Epoch 1/2-Step 1750... Discriminator Loss: 1.3603... Generator Loss: 0.9009
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Epoch 1/2-Step 1760... Discriminator Loss: 1.0816... Generator Loss: 0.9107
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Epoch 1/2-Step 1770... Discriminator Loss: 0.8256... Generator Loss: 1.0552
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Epoch 1/2-Step 1780... Discriminator Loss: 0.4205... Generator Loss: 1.9499
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Epoch 1/2-Step 1790... Discriminator Loss: 1.6130... Generator Loss: 0.3562
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Epoch 1/2-Step 1800... Discriminator Loss: 2.5861... Generator Loss: 0.1800
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/2-Step 1810... Discriminator Loss: 1.5904... Generator Loss: 0.5657
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Epoch 1/2-Step 1820... Discriminator Loss: 2.0948... Generator Loss: 0.2882
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Epoch 1/2-Step 1830... Discriminator Loss: 1.0161... Generator Loss: 1.1578
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Epoch 1/2-Step 1840... Discriminator Loss: 1.3440... Generator Loss: 0.5831
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Epoch 1/2-Step 1850... Discriminator Loss: 1.0551... Generator Loss: 0.9924
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Epoch 1/2-Step 1860... Discriminator Loss: 1.3557... Generator Loss: 0.5949
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Epoch 1/2-Step 1870... Discriminator Loss: 2.1658... Generator Loss: 0.7761
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Epoch 2/2-Step 1880... Discriminator Loss: 1.7988... Generator Loss: 0.6089
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Epoch 2/2-Step 1890... Discriminator Loss: 1.3895... Generator Loss: 1.2412
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Epoch 2/2-Step 1900... Discriminator Loss: 1.1182... Generator Loss: 1.1952
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</pre>
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</div>
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</div>
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<div class="output_png output_subarea ">
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"
|
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>
|
|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 1910... Discriminator Loss: 1.4968... Generator Loss: 0.5684
|
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Epoch 2/2-Step 1920... Discriminator Loss: 1.4977... Generator Loss: 0.6296
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Epoch 2/2-Step 1930... Discriminator Loss: 1.9247... Generator Loss: 0.2941
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Epoch 2/2-Step 1940... Discriminator Loss: 0.8135... Generator Loss: 1.0944
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Epoch 2/2-Step 1950... Discriminator Loss: 2.9539... Generator Loss: 0.1693
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Epoch 2/2-Step 1960... Discriminator Loss: 1.6268... Generator Loss: 0.4130
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Epoch 2/2-Step 1970... Discriminator Loss: 2.4348... Generator Loss: 0.1656
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Epoch 2/2-Step 1980... Discriminator Loss: 1.0910... Generator Loss: 1.1037
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Epoch 2/2-Step 1990... Discriminator Loss: 2.4345... Generator Loss: 0.2697
|
|
Epoch 2/2-Step 2000... Discriminator Loss: 2.8119... Generator Loss: 0.1547
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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/FDuXJcVNlgMA2YCc4DfVtuAUmDfB5FSE98E3kj/G0bqvXgiMAt4CuhW7b624tgGAw+l232AV4DZ
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2I1vGA2I3fiG0YDYjW8YDYjd+IbRgNiNbxgNyP8H9IkGhQZC0BIAAAAASUVORK5CYII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 2010... Discriminator Loss: 2.9430... Generator Loss: 0.1119
|
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Epoch 2/2-Step 2020... Discriminator Loss: 2.4929... Generator Loss: 0.2289
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Epoch 2/2-Step 2030... Discriminator Loss: 1.3928... Generator Loss: 0.6620
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Epoch 2/2-Step 2040... Discriminator Loss: 0.9953... Generator Loss: 0.9853
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Epoch 2/2-Step 2050... Discriminator Loss: 2.0209... Generator Loss: 0.3261
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Epoch 2/2-Step 2060... Discriminator Loss: 1.0119... Generator Loss: 1.4060
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Epoch 2/2-Step 2070... Discriminator Loss: 2.3089... Generator Loss: 0.2537
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Epoch 2/2-Step 2080... Discriminator Loss: 1.8246... Generator Loss: 3.4251
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Epoch 2/2-Step 2090... Discriminator Loss: 3.6070... Generator Loss: 0.2074
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Epoch 2/2-Step 2100... Discriminator Loss: 1.7052... Generator Loss: 1.4942
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
|
>
|
|
</div>
|
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|
|
</div>
|
|
|
|
<div class="output_area">
|
|
|
|
<div class="prompt"></div>
|
|
|
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|
|
<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 2110... Discriminator Loss: 1.7078... Generator Loss: 0.4842
|
|
Epoch 2/2-Step 2120... Discriminator Loss: 1.8781... Generator Loss: 0.3792
|
|
Epoch 2/2-Step 2130... Discriminator Loss: 0.9712... Generator Loss: 1.0053
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Epoch 2/2-Step 2140... Discriminator Loss: 2.0136... Generator Loss: 0.3907
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|
Epoch 2/2-Step 2150... Discriminator Loss: 2.0800... Generator Loss: 0.4026
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Epoch 2/2-Step 2160... Discriminator Loss: 1.2040... Generator Loss: 0.6960
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Epoch 2/2-Step 2170... Discriminator Loss: 0.4719... Generator Loss: 3.4201
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|
Epoch 2/2-Step 2180... Discriminator Loss: 1.2094... Generator Loss: 0.7431
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|
Epoch 2/2-Step 2190... Discriminator Loss: 1.9730... Generator Loss: 0.5210
|
|
Epoch 2/2-Step 2200... Discriminator Loss: 1.3208... Generator Loss: 0.9190
|
|
</pre>
|
|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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rkJggg==
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 2/2-Step 2210... Discriminator Loss: 0.9230... Generator Loss: 1.0733
|
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Epoch 2/2-Step 2220... Discriminator Loss: 1.6415... Generator Loss: 0.4990
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Epoch 2/2-Step 2230... Discriminator Loss: 1.2177... Generator Loss: 0.7985
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Epoch 2/2-Step 2240... Discriminator Loss: 1.7188... Generator Loss: 0.4603
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Epoch 2/2-Step 2250... Discriminator Loss: 1.0832... Generator Loss: 0.9234
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Epoch 2/2-Step 2260... Discriminator Loss: 2.4330... Generator Loss: 0.2552
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Epoch 2/2-Step 2270... Discriminator Loss: 1.0972... Generator Loss: 1.8646
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Epoch 2/2-Step 2280... Discriminator Loss: 4.2175... Generator Loss: 0.0387
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Epoch 2/2-Step 2290... Discriminator Loss: 1.4911... Generator Loss: 0.6146
|
|
Epoch 2/2-Step 2300... Discriminator Loss: 1.0731... Generator Loss: 1.9281
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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obm3tRH7tgPwVHa8HvA6MBN4CPhFc29fCfuxGfBG9hg9DrSv5eMD/AX4AHgPuBv4RbmOj0XuGUYK
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SUVORK5CYII=
|
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 2310... Discriminator Loss: 2.6048... Generator Loss: 0.1825
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Epoch 2/2-Step 2320... Discriminator Loss: 1.3122... Generator Loss: 0.5521
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Epoch 2/2-Step 2330... Discriminator Loss: 1.3775... Generator Loss: 0.6418
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Epoch 2/2-Step 2340... Discriminator Loss: 1.1761... Generator Loss: 0.7925
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Epoch 2/2-Step 2350... Discriminator Loss: 1.1322... Generator Loss: 0.7320
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Epoch 2/2-Step 2360... Discriminator Loss: 0.1161... Generator Loss: 3.6323
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Epoch 2/2-Step 2370... Discriminator Loss: 1.0060... Generator Loss: 1.2201
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Epoch 2/2-Step 2380... Discriminator Loss: 0.6671... Generator Loss: 1.1994
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Epoch 2/2-Step 2390... Discriminator Loss: 3.2159... Generator Loss: 0.2751
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Epoch 2/2-Step 2400... Discriminator Loss: 3.4933... Generator Loss: 0.0844
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</pre>
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</div>
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</div>
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TkSuQmCC
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 2/2-Step 2410... Discriminator Loss: 0.9275... Generator Loss: 2.6940
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Epoch 2/2-Step 2420... Discriminator Loss: 1.1644... Generator Loss: 0.7646
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Epoch 2/2-Step 2430... Discriminator Loss: 1.8302... Generator Loss: 0.3799
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Epoch 2/2-Step 2440... Discriminator Loss: 1.7304... Generator Loss: 0.3922
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Epoch 2/2-Step 2450... Discriminator Loss: 2.1595... Generator Loss: 0.3598
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Epoch 2/2-Step 2460... Discriminator Loss: 1.7138... Generator Loss: 1.4030
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Epoch 2/2-Step 2470... Discriminator Loss: 1.2481... Generator Loss: 0.6815
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Epoch 2/2-Step 2480... Discriminator Loss: 1.4053... Generator Loss: 0.7218
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Epoch 2/2-Step 2490... Discriminator Loss: 3.3293... Generator Loss: 0.1335
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|
Epoch 2/2-Step 2500... Discriminator Loss: 1.8164... Generator Loss: 0.5824
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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"
|
|
>
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</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 2510... Discriminator Loss: 2.6035... Generator Loss: 0.2349
|
|
Epoch 2/2-Step 2520... Discriminator Loss: 0.8650... Generator Loss: 4.0960
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Epoch 2/2-Step 2530... Discriminator Loss: 1.5684... Generator Loss: 0.5153
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Epoch 2/2-Step 2540... Discriminator Loss: 0.7783... Generator Loss: 1.2478
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Epoch 2/2-Step 2550... Discriminator Loss: 2.4847... Generator Loss: 0.3230
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Epoch 2/2-Step 2560... Discriminator Loss: 0.8980... Generator Loss: 4.6620
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Epoch 2/2-Step 2570... Discriminator Loss: 1.3185... Generator Loss: 1.2826
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Epoch 2/2-Step 2580... Discriminator Loss: 3.2025... Generator Loss: 0.2373
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Epoch 2/2-Step 2590... Discriminator Loss: 2.0644... Generator Loss: 0.4936
|
|
Epoch 2/2-Step 2600... Discriminator Loss: 0.9851... Generator Loss: 1.2821
|
|
</pre>
|
|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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TkSuQmCC
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 2610... Discriminator Loss: 1.7005... Generator Loss: 0.7132
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Epoch 2/2-Step 2620... Discriminator Loss: 1.7881... Generator Loss: 0.4661
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Epoch 2/2-Step 2630... Discriminator Loss: 1.1388... Generator Loss: 0.9582
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Epoch 2/2-Step 2640... Discriminator Loss: 1.0748... Generator Loss: 2.2080
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Epoch 2/2-Step 2650... Discriminator Loss: 1.4443... Generator Loss: 0.7525
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Epoch 2/2-Step 2660... Discriminator Loss: 2.8320... Generator Loss: 0.1446
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Epoch 2/2-Step 2670... Discriminator Loss: 1.6948... Generator Loss: 0.9054
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Epoch 2/2-Step 2680... Discriminator Loss: 0.5985... Generator Loss: 1.3193
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Epoch 2/2-Step 2690... Discriminator Loss: 1.9464... Generator Loss: 2.4039
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Epoch 2/2-Step 2700... Discriminator Loss: 1.1410... Generator Loss: 0.8293
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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"
|
|
>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 2/2-Step 2710... Discriminator Loss: 0.9968... Generator Loss: 0.9076
|
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Epoch 2/2-Step 2720... Discriminator Loss: 1.7824... Generator Loss: 0.5328
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Epoch 2/2-Step 2730... Discriminator Loss: 0.2981... Generator Loss: 2.5057
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Epoch 2/2-Step 2740... Discriminator Loss: 1.0728... Generator Loss: 0.9905
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Epoch 2/2-Step 2750... Discriminator Loss: 1.5693... Generator Loss: 0.5073
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Epoch 2/2-Step 2760... Discriminator Loss: 1.0690... Generator Loss: 3.7526
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Epoch 2/2-Step 2770... Discriminator Loss: 2.4759... Generator Loss: 0.2352
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Epoch 2/2-Step 2780... Discriminator Loss: 0.9367... Generator Loss: 2.4314
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Epoch 2/2-Step 2790... Discriminator Loss: 1.5539... Generator Loss: 2.2785
|
|
Epoch 2/2-Step 2800... Discriminator Loss: 1.4832... Generator Loss: 3.2962
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
|
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l3U2WPwAeBV4HTi50QaUEs99azJq4gvAc9l/PyAzL34ImAE8CPRo9LmWcW3bA+Oz7bWBp4DXgFuA
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GG2IvfiG0YbYi28YbYi9+IbRhtiLbxhtyP8D5OV0C+luBrMAAAAASUVORK5CYII=
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 2/2-Step 2810... Discriminator Loss: 1.1598... Generator Loss: 0.6267
|
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Epoch 2/2-Step 2820... Discriminator Loss: 1.3759... Generator Loss: 0.5195
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Epoch 2/2-Step 2830... Discriminator Loss: 1.0197... Generator Loss: 0.9089
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Epoch 2/2-Step 2840... Discriminator Loss: 0.5584... Generator Loss: 1.6436
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Epoch 2/2-Step 2850... Discriminator Loss: 1.8377... Generator Loss: 0.3658
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Epoch 2/2-Step 2860... Discriminator Loss: 1.2147... Generator Loss: 0.6694
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Epoch 2/2-Step 2870... Discriminator Loss: 1.1408... Generator Loss: 0.8225
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Epoch 2/2-Step 2880... Discriminator Loss: 0.8431... Generator Loss: 1.3163
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Epoch 2/2-Step 2890... Discriminator Loss: 2.2990... Generator Loss: 0.2128
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Epoch 2/2-Step 2900... Discriminator Loss: 2.0544... Generator Loss: 0.4308
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 2910... Discriminator Loss: 2.3895... Generator Loss: 0.2125
|
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Epoch 2/2-Step 2920... Discriminator Loss: 0.8310... Generator Loss: 1.0606
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Epoch 2/2-Step 2930... Discriminator Loss: 2.2490... Generator Loss: 4.4028
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Epoch 2/2-Step 2940... Discriminator Loss: 1.2407... Generator Loss: 1.2902
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Epoch 2/2-Step 2950... Discriminator Loss: 2.6363... Generator Loss: 0.2990
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Epoch 2/2-Step 2960... Discriminator Loss: 2.1573... Generator Loss: 0.2985
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Epoch 2/2-Step 2970... Discriminator Loss: 1.2197... Generator Loss: 0.8472
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Epoch 2/2-Step 2980... Discriminator Loss: 1.1789... Generator Loss: 2.3603
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Epoch 2/2-Step 2990... Discriminator Loss: 1.4132... Generator Loss: 1.0563
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Epoch 2/2-Step 3000... Discriminator Loss: 1.4316... Generator Loss: 2.1424
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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8cBrwAPAqo3uawXHthMwNtv+IvAMMA24E/hco/tXxnEMBp7LnqO/AKu08vkBRgJTgEnATcDnanV+
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vILUAAAAAElFTkSuQmCC
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 3010... Discriminator Loss: 2.1096... Generator Loss: 0.3688
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Epoch 2/2-Step 3020... Discriminator Loss: 2.7113... Generator Loss: 0.1992
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Epoch 2/2-Step 3030... Discriminator Loss: 0.7655... Generator Loss: 1.4882
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Epoch 2/2-Step 3040... Discriminator Loss: 0.8179... Generator Loss: 1.1351
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Epoch 2/2-Step 3050... Discriminator Loss: 2.7765... Generator Loss: 0.1775
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Epoch 2/2-Step 3060... Discriminator Loss: 1.6212... Generator Loss: 2.3479
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Epoch 2/2-Step 3070... Discriminator Loss: 1.7499... Generator Loss: 0.4827
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Epoch 2/2-Step 3080... Discriminator Loss: 1.0921... Generator Loss: 1.2112
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Epoch 2/2-Step 3090... Discriminator Loss: 2.1500... Generator Loss: 2.5354
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Epoch 2/2-Step 3100... Discriminator Loss: 1.4504... Generator Loss: 0.6992
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
|
>
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</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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|
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|
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 3110... Discriminator Loss: 0.4549... Generator Loss: 2.0005
|
|
Epoch 2/2-Step 3120... Discriminator Loss: 2.3352... Generator Loss: 0.1726
|
|
Epoch 2/2-Step 3130... Discriminator Loss: 1.8152... Generator Loss: 0.3370
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Epoch 2/2-Step 3140... Discriminator Loss: 1.2448... Generator Loss: 0.6605
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Epoch 2/2-Step 3150... Discriminator Loss: 1.3393... Generator Loss: 0.6704
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Epoch 2/2-Step 3160... Discriminator Loss: 0.3832... Generator Loss: 1.5856
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Epoch 2/2-Step 3170... Discriminator Loss: 1.4028... Generator Loss: 0.6451
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|
Epoch 2/2-Step 3180... Discriminator Loss: 0.6644... Generator Loss: 1.8764
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Epoch 2/2-Step 3190... Discriminator Loss: 0.9284... Generator Loss: 0.8118
|
|
Epoch 2/2-Step 3200... Discriminator Loss: 0.9369... Generator Loss: 1.0325
|
|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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GAAAAABJRU5ErkJggg==
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"
|
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>
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</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 3210... Discriminator Loss: 3.6743... Generator Loss: 0.0663
|
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Epoch 2/2-Step 3220... Discriminator Loss: 1.6144... Generator Loss: 0.5525
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Epoch 2/2-Step 3230... Discriminator Loss: 0.6816... Generator Loss: 1.5077
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Epoch 2/2-Step 3240... Discriminator Loss: 3.3266... Generator Loss: 0.0811
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Epoch 2/2-Step 3250... Discriminator Loss: 1.7431... Generator Loss: 0.5593
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Epoch 2/2-Step 3260... Discriminator Loss: 1.4966... Generator Loss: 0.5404
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Epoch 2/2-Step 3270... Discriminator Loss: 1.6944... Generator Loss: 0.7823
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Epoch 2/2-Step 3280... Discriminator Loss: 1.5925... Generator Loss: 0.6031
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Epoch 2/2-Step 3290... Discriminator Loss: 1.2572... Generator Loss: 0.9174
|
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Epoch 2/2-Step 3300... Discriminator Loss: 1.7342... Generator Loss: 0.5187
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 3310... Discriminator Loss: 0.1825... Generator Loss: 4.5827
|
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Epoch 2/2-Step 3320... Discriminator Loss: 1.2775... Generator Loss: 0.7300
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Epoch 2/2-Step 3330... Discriminator Loss: 0.8732... Generator Loss: 1.2389
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Epoch 2/2-Step 3340... Discriminator Loss: 1.8792... Generator Loss: 0.4560
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Epoch 2/2-Step 3350... Discriminator Loss: 0.8879... Generator Loss: 0.9273
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Epoch 2/2-Step 3360... Discriminator Loss: 0.4954... Generator Loss: 1.6400
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Epoch 2/2-Step 3370... Discriminator Loss: 0.6375... Generator Loss: 1.5675
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Epoch 2/2-Step 3380... Discriminator Loss: 1.5641... Generator Loss: 0.5521
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Epoch 2/2-Step 3390... Discriminator Loss: 0.4753... Generator Loss: 1.8465
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Epoch 2/2-Step 3400... Discriminator Loss: 0.4872... Generator Loss: 1.5438
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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/n9AItdl/jsVn59G3vhPAwPyVsn5yRkq7m7g/qsin2/wamCy9/4C9dHd5HIOQgvlHvTej/De9/Xe
|
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L0/uXDzsvd+PFs2l6L1/H3jXObdKXiS5IVvy/FDvXJcNNljsALwKvA78uqsNKGX2fVNyauILwPP5
|
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fzuQey8eB0wF/g706uq+VnBsQ4Cx+fYKwL+B14BbgO90df/KOI7BwIT8OboTWLSVzw/wW+AVYBJw
|
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HfCdWp0f89wzjDbEjHuG0YbYjW8YbYjd+IbRhtiNbxhtiN34htGG2I1vGG2I3fiG0YbYjW8Ybcj/
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BxVNXpov1iXVAAAAAElFTkSuQmCC
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 2/2-Step 3410... Discriminator Loss: 2.7610... Generator Loss: 0.2020
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Epoch 2/2-Step 3420... Discriminator Loss: 1.1922... Generator Loss: 1.7685
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Epoch 2/2-Step 3430... Discriminator Loss: 0.8931... Generator Loss: 1.5826
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Epoch 2/2-Step 3440... Discriminator Loss: 2.0843... Generator Loss: 0.4748
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Epoch 2/2-Step 3450... Discriminator Loss: 1.3589... Generator Loss: 0.6113
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Epoch 2/2-Step 3460... Discriminator Loss: 0.7527... Generator Loss: 1.3879
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Epoch 2/2-Step 3470... Discriminator Loss: 1.2510... Generator Loss: 0.7047
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Epoch 2/2-Step 3480... Discriminator Loss: 1.1979... Generator Loss: 0.8418
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Epoch 2/2-Step 3490... Discriminator Loss: 1.6452... Generator Loss: 5.2290
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Epoch 2/2-Step 3500... Discriminator Loss: 0.5588... Generator Loss: 1.8905
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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SUVORK5CYII=
|
|
"
|
|
>
|
|
</div>
|
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|
|
</div>
|
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|
|
<div class="output_area">
|
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|
|
<div class="prompt"></div>
|
|
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|
<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 3510... Discriminator Loss: 1.0099... Generator Loss: 1.4121
|
|
Epoch 2/2-Step 3520... Discriminator Loss: 2.7485... Generator Loss: 0.2938
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Epoch 2/2-Step 3530... Discriminator Loss: 1.3084... Generator Loss: 0.5821
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Epoch 2/2-Step 3540... Discriminator Loss: 1.4313... Generator Loss: 3.7524
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Epoch 2/2-Step 3550... Discriminator Loss: 0.9619... Generator Loss: 2.8347
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Epoch 2/2-Step 3560... Discriminator Loss: 0.8457... Generator Loss: 1.6197
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Epoch 2/2-Step 3570... Discriminator Loss: 1.3874... Generator Loss: 0.6073
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Epoch 2/2-Step 3580... Discriminator Loss: 1.4953... Generator Loss: 0.6685
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|
Epoch 2/2-Step 3590... Discriminator Loss: 1.2233... Generator Loss: 0.6353
|
|
Epoch 2/2-Step 3600... Discriminator Loss: 1.0592... Generator Loss: 1.0551
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
|
>
|
|
</div>
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</div>
|
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|
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<div class="output_area">
|
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|
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<div class="prompt"></div>
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|
|
<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 2/2-Step 3610... Discriminator Loss: 1.9337... Generator Loss: 0.3964
|
|
Epoch 2/2-Step 3620... Discriminator Loss: 0.7413... Generator Loss: 1.2235
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Epoch 2/2-Step 3630... Discriminator Loss: 1.1959... Generator Loss: 0.9519
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Epoch 2/2-Step 3640... Discriminator Loss: 1.0010... Generator Loss: 1.5190
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Epoch 2/2-Step 3650... Discriminator Loss: 2.8820... Generator Loss: 0.1989
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Epoch 2/2-Step 3660... Discriminator Loss: 3.1258... Generator Loss: 0.2029
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Epoch 2/2-Step 3670... Discriminator Loss: 1.7464... Generator Loss: 0.5479
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Epoch 2/2-Step 3680... Discriminator Loss: 1.9657... Generator Loss: 0.3596
|
|
Epoch 2/2-Step 3690... Discriminator Loss: 2.0325... Generator Loss: 0.4481
|
|
Epoch 2/2-Step 3700... Discriminator Loss: 0.7904... Generator Loss: 1.4904
|
|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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ow6tdY2mAdN8LmMU5LJGrUHrXp+Q69J7/xOQyHWZ/07F16eRD/54oF/eKjkHOUPFyAYevyry+Qav
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AyZ77y9QH40kl3MQWij3oPf+eO99b+99H3LX4jHv/V60aC5F7/0M4APn3PJ5keSGbMnrQ71zXTbY
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YLE18CbwNvDXrjaglNn3Dcipia8CL+f/bU3uvXg08BbwKLBgV/e1gnPbGBiVby8NPAdMAW4HftXV
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/SvjPAYAz+ev0d3AAq18fYBTgdeBCcD/Ab+q1fUxzz3DaEPMuGcYbYg9+IbRhtiDbxhtiD34htGG
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2INvGG2IPfiG0YbYg28YbYg9+IbRhvx/2+fnrJ9W/7kAAAAASUVORK5CYII=
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"
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>
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<pre>Epoch 2/2-Step 3710... Discriminator Loss: 3.2823... Generator Loss: 0.0943
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Epoch 2/2-Step 3720... Discriminator Loss: 3.5929... Generator Loss: 0.1268
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Epoch 2/2-Step 3730... Discriminator Loss: 2.5083... Generator Loss: 0.2654
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Epoch 2/2-Step 3740... Discriminator Loss: 1.2404... Generator Loss: 0.8706
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Epoch 2/2-Step 3750... Discriminator Loss: 0.4249... Generator Loss: 3.3969
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</pre>
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<h3 id="CelebA">CelebA<a class="anchor-link" href="#CelebA">¶</a></h3><p>Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.</p>
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<div class="prompt input_prompt">In [94]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
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<span class="n">z_dim</span> <span class="o">=</span> <span class="mi">100</span>
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<span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.0002</span>
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<span class="n">beta1</span> <span class="o">=</span> <span class="mf">0.5</span>
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<span class="sd">"""</span>
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<span class="sd">DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
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<span class="sd">"""</span>
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<span class="n">epochs</span> <span class="o">=</span> <span class="mi">1</span>
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<span class="n">celeba_dataset</span> <span class="o">=</span> <span class="n">helper</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="s1">'celeba'</span><span class="p">,</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">'img_align_celeba/*.jpg'</span><span class="p">)))</span>
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<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span>
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<span class="n">train</span><span class="p">(</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">z_dim</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">,</span> <span class="n">beta1</span><span class="p">,</span> <span class="n">celeba_dataset</span><span class="o">.</span><span class="n">get_batches</span><span class="p">,</span>
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<span class="n">celeba_dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">celeba_dataset</span><span class="o">.</span><span class="n">image_mode</span><span class="p">)</span>
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</pre></div>
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<pre>Epoch 1/1-Step 10... Discriminator Loss: 0.0727... Generator Loss: 9.3794
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Epoch 1/1-Step 20... Discriminator Loss: 0.0076... Generator Loss: 12.2277
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Epoch 1/1-Step 30... Discriminator Loss: 0.0632... Generator Loss: 3.0810
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Epoch 1/1-Step 40... Discriminator Loss: 0.0050... Generator Loss: 6.2162
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Epoch 1/1-Step 50... Discriminator Loss: 0.0349... Generator Loss: 5.5600
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Epoch 1/1-Step 60... Discriminator Loss: 0.0111... Generator Loss: 4.7894
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Epoch 1/1-Step 70... Discriminator Loss: 0.0235... Generator Loss: 5.5622
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Epoch 1/1-Step 80... Discriminator Loss: 0.1791... Generator Loss: 1.9836
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Epoch 1/1-Step 90... Discriminator Loss: 0.0956... Generator Loss: 6.7089
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Epoch 1/1-Step 100... Discriminator Loss: 0.6838... Generator Loss: 8.5993
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 110... Discriminator Loss: 0.5457... Generator Loss: 2.6397
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Epoch 1/1-Step 120... Discriminator Loss: 0.8652... Generator Loss: 0.9929
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Epoch 1/1-Step 130... Discriminator Loss: 2.4754... Generator Loss: 8.4404
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Epoch 1/1-Step 140... Discriminator Loss: 4.9178... Generator Loss: 0.0960
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Epoch 1/1-Step 150... Discriminator Loss: 4.8412... Generator Loss: 0.0560
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Epoch 1/1-Step 160... Discriminator Loss: 3.6360... Generator Loss: 0.1029
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Epoch 1/1-Step 170... Discriminator Loss: 2.6070... Generator Loss: 0.1986
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Epoch 1/1-Step 180... Discriminator Loss: 2.5153... Generator Loss: 0.2089
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Epoch 1/1-Step 190... Discriminator Loss: 2.1722... Generator Loss: 0.5444
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Epoch 1/1-Step 200... Discriminator Loss: 2.5551... Generator Loss: 0.2539
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</pre>
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</div>
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</div>
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wPsAAAAASUVORK5CYII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 210... Discriminator Loss: 2.0813... Generator Loss: 0.3078
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Epoch 1/1-Step 220... Discriminator Loss: 1.9055... Generator Loss: 0.4443
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Epoch 1/1-Step 230... Discriminator Loss: 2.5085... Generator Loss: 0.3532
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Epoch 1/1-Step 240... Discriminator Loss: 2.2245... Generator Loss: 0.2461
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Epoch 1/1-Step 250... Discriminator Loss: 2.1935... Generator Loss: 0.4254
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Epoch 1/1-Step 260... Discriminator Loss: 1.9623... Generator Loss: 0.3481
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Epoch 1/1-Step 270... Discriminator Loss: 1.9798... Generator Loss: 0.3965
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Epoch 1/1-Step 280... Discriminator Loss: 1.8648... Generator Loss: 0.3960
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Epoch 1/1-Step 290... Discriminator Loss: 2.0754... Generator Loss: 0.4496
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Epoch 1/1-Step 300... Discriminator Loss: 1.7719... Generator Loss: 0.5716
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</pre>
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</div>
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</div>
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<div class="output_area">
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SUVORK5CYII=
|
|
"
|
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>
|
|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 310... Discriminator Loss: 1.6204... Generator Loss: 0.5335
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Epoch 1/1-Step 320... Discriminator Loss: 2.0243... Generator Loss: 0.3130
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Epoch 1/1-Step 330... Discriminator Loss: 1.6476... Generator Loss: 0.8068
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Epoch 1/1-Step 340... Discriminator Loss: 1.7978... Generator Loss: 0.4309
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Epoch 1/1-Step 350... Discriminator Loss: 2.0922... Generator Loss: 0.3991
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Epoch 1/1-Step 360... Discriminator Loss: 1.6118... Generator Loss: 0.6267
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Epoch 1/1-Step 370... Discriminator Loss: 1.6625... Generator Loss: 0.6424
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Epoch 1/1-Step 380... Discriminator Loss: 1.7148... Generator Loss: 0.4611
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Epoch 1/1-Step 390... Discriminator Loss: 1.5929... Generator Loss: 0.5055
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Epoch 1/1-Step 400... Discriminator Loss: 1.6716... Generator Loss: 0.5413
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 410... Discriminator Loss: 1.5165... Generator Loss: 0.5024
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Epoch 1/1-Step 420... Discriminator Loss: 1.6313... Generator Loss: 0.5201
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Epoch 1/1-Step 430... Discriminator Loss: 1.6951... Generator Loss: 0.3886
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Epoch 1/1-Step 440... Discriminator Loss: 2.0725... Generator Loss: 0.3885
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Epoch 1/1-Step 450... Discriminator Loss: 1.7706... Generator Loss: 0.4545
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Epoch 1/1-Step 460... Discriminator Loss: 1.6135... Generator Loss: 0.4660
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Epoch 1/1-Step 470... Discriminator Loss: 1.7225... Generator Loss: 0.5455
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Epoch 1/1-Step 480... Discriminator Loss: 1.8447... Generator Loss: 0.5592
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Epoch 1/1-Step 490... Discriminator Loss: 1.5571... Generator Loss: 0.6380
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Epoch 1/1-Step 500... Discriminator Loss: 1.6766... Generator Loss: 0.4717
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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|
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|
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YMztTFUAAAAASUVORK5CYII=
|
|
"
|
|
>
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|
</div>
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</div>
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<div class="output_area">
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|
|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/1-Step 510... Discriminator Loss: 1.6623... Generator Loss: 0.5098
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Epoch 1/1-Step 520... Discriminator Loss: 1.7112... Generator Loss: 0.5697
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Epoch 1/1-Step 530... Discriminator Loss: 1.7006... Generator Loss: 0.3925
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Epoch 1/1-Step 540... Discriminator Loss: 1.5537... Generator Loss: 0.4265
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Epoch 1/1-Step 550... Discriminator Loss: 1.7764... Generator Loss: 0.3655
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Epoch 1/1-Step 560... Discriminator Loss: 1.3598... Generator Loss: 0.5995
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Epoch 1/1-Step 570... Discriminator Loss: 1.4859... Generator Loss: 0.6381
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Epoch 1/1-Step 580... Discriminator Loss: 1.6684... Generator Loss: 0.4805
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Epoch 1/1-Step 590... Discriminator Loss: 1.7357... Generator Loss: 0.3859
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Epoch 1/1-Step 600... Discriminator Loss: 1.7320... Generator Loss: 0.5614
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmsbWlWH7a+PZ95uOObh5q6uqvppoFuME7AIAIidrAs
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 610... Discriminator Loss: 1.7415... Generator Loss: 0.3289
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Epoch 1/1-Step 620... Discriminator Loss: 1.7709... Generator Loss: 0.5224
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Epoch 1/1-Step 630... Discriminator Loss: 1.6991... Generator Loss: 0.4420
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Epoch 1/1-Step 640... Discriminator Loss: 1.6305... Generator Loss: 0.4226
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Epoch 1/1-Step 650... Discriminator Loss: 1.3594... Generator Loss: 0.6459
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Epoch 1/1-Step 660... Discriminator Loss: 1.5848... Generator Loss: 0.6701
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Epoch 1/1-Step 670... Discriminator Loss: 1.5340... Generator Loss: 0.5317
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Epoch 1/1-Step 680... Discriminator Loss: 1.4880... Generator Loss: 0.7570
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Epoch 1/1-Step 690... Discriminator Loss: 1.7384... Generator Loss: 0.4051
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Epoch 1/1-Step 700... Discriminator Loss: 1.6310... Generator Loss: 0.3631
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</pre>
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</div>
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</div>
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<div class="output_area">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 710... Discriminator Loss: 1.5873... Generator Loss: 0.5126
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Epoch 1/1-Step 720... Discriminator Loss: 1.5325... Generator Loss: 0.5958
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Epoch 1/1-Step 730... Discriminator Loss: 1.3896... Generator Loss: 0.6622
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Epoch 1/1-Step 740... Discriminator Loss: 1.5005... Generator Loss: 0.7440
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Epoch 1/1-Step 750... Discriminator Loss: 1.7124... Generator Loss: 0.4646
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Epoch 1/1-Step 760... Discriminator Loss: 1.6152... Generator Loss: 0.6327
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Epoch 1/1-Step 770... Discriminator Loss: 1.6164... Generator Loss: 0.5734
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Epoch 1/1-Step 780... Discriminator Loss: 1.7138... Generator Loss: 0.4487
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Epoch 1/1-Step 790... Discriminator Loss: 1.5637... Generator Loss: 0.5049
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Epoch 1/1-Step 800... Discriminator Loss: 1.6402... Generator Loss: 0.5617
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 810... Discriminator Loss: 1.7563... Generator Loss: 0.3923
|
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Epoch 1/1-Step 820... Discriminator Loss: 1.7762... Generator Loss: 0.4678
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Epoch 1/1-Step 830... Discriminator Loss: 1.5962... Generator Loss: 0.5257
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Epoch 1/1-Step 840... Discriminator Loss: 1.6072... Generator Loss: 0.6522
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Epoch 1/1-Step 850... Discriminator Loss: 1.6226... Generator Loss: 0.5555
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Epoch 1/1-Step 860... Discriminator Loss: 1.5246... Generator Loss: 0.6241
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Epoch 1/1-Step 870... Discriminator Loss: 1.6424... Generator Loss: 0.5019
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Epoch 1/1-Step 880... Discriminator Loss: 1.5898... Generator Loss: 0.4438
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Epoch 1/1-Step 890... Discriminator Loss: 1.6457... Generator Loss: 0.5456
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Epoch 1/1-Step 900... Discriminator Loss: 1.6340... Generator Loss: 0.5472
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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CAKSHimHZgAAAABJRU5ErkJggg==
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 910... Discriminator Loss: 1.5515... Generator Loss: 0.5978
|
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Epoch 1/1-Step 920... Discriminator Loss: 1.6087... Generator Loss: 0.5024
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Epoch 1/1-Step 930... Discriminator Loss: 1.6616... Generator Loss: 0.4458
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Epoch 1/1-Step 940... Discriminator Loss: 1.9298... Generator Loss: 0.3957
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Epoch 1/1-Step 950... Discriminator Loss: 1.5688... Generator Loss: 0.6162
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Epoch 1/1-Step 960... Discriminator Loss: 1.5625... Generator Loss: 0.5942
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Epoch 1/1-Step 970... Discriminator Loss: 1.6850... Generator Loss: 0.3905
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Epoch 1/1-Step 980... Discriminator Loss: 1.5903... Generator Loss: 0.5228
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Epoch 1/1-Step 990... Discriminator Loss: 1.5929... Generator Loss: 0.5685
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|
Epoch 1/1-Step 1000... Discriminator Loss: 1.7475... Generator Loss: 0.3601
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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QmCC
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1010... Discriminator Loss: 1.6496... Generator Loss: 0.4886
|
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Epoch 1/1-Step 1020... Discriminator Loss: 1.6259... Generator Loss: 0.4527
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Epoch 1/1-Step 1030... Discriminator Loss: 1.6950... Generator Loss: 0.4469
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Epoch 1/1-Step 1040... Discriminator Loss: 1.5717... Generator Loss: 0.6022
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Epoch 1/1-Step 1050... Discriminator Loss: 1.7170... Generator Loss: 0.5175
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Epoch 1/1-Step 1060... Discriminator Loss: 1.9889... Generator Loss: 0.4170
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Epoch 1/1-Step 1070... Discriminator Loss: 1.7334... Generator Loss: 0.4863
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Epoch 1/1-Step 1080... Discriminator Loss: 1.4844... Generator Loss: 0.6532
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Epoch 1/1-Step 1090... Discriminator Loss: 1.5442... Generator Loss: 0.5522
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|
Epoch 1/1-Step 1100... Discriminator Loss: 1.6279... Generator Loss: 0.5473
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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/B6A7+q/T8Kvi58DcB7ANwG0D7qtd9G3nwPwrH5+EMC3AVwA8E8AigfdvjvoxwcBvKBj9M8AWod5
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BMwgwoMfEDCDCA9+QMAM4v8BKTr0nJiO53gAAAAASUVORK5CYII=
|
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"
|
|
>
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</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 1110... Discriminator Loss: 1.4117... Generator Loss: 0.6320
|
|
Epoch 1/1-Step 1120... Discriminator Loss: 1.5478... Generator Loss: 0.5325
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Epoch 1/1-Step 1130... Discriminator Loss: 1.4858... Generator Loss: 0.6128
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Epoch 1/1-Step 1140... Discriminator Loss: 1.7873... Generator Loss: 0.4608
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Epoch 1/1-Step 1150... Discriminator Loss: 1.5082... Generator Loss: 0.5438
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Epoch 1/1-Step 1160... Discriminator Loss: 1.6175... Generator Loss: 0.5056
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Epoch 1/1-Step 1170... Discriminator Loss: 1.7110... Generator Loss: 0.5155
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Epoch 1/1-Step 1180... Discriminator Loss: 1.8326... Generator Loss: 0.4454
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Epoch 1/1-Step 1190... Discriminator Loss: 1.5819... Generator Loss: 0.5531
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Epoch 1/1-Step 1200... Discriminator Loss: 1.5146... Generator Loss: 0.5342
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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"
|
|
>
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|
</div>
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|
</div>
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|
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<div class="output_area">
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|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 1210... Discriminator Loss: 1.6864... Generator Loss: 0.6659
|
|
Epoch 1/1-Step 1220... Discriminator Loss: 1.6513... Generator Loss: 0.4822
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Epoch 1/1-Step 1230... Discriminator Loss: 1.6701... Generator Loss: 0.5620
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Epoch 1/1-Step 1240... Discriminator Loss: 1.6497... Generator Loss: 0.3909
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Epoch 1/1-Step 1250... Discriminator Loss: 1.5540... Generator Loss: 0.6141
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Epoch 1/1-Step 1260... Discriminator Loss: 1.6308... Generator Loss: 0.4989
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Epoch 1/1-Step 1270... Discriminator Loss: 1.6109... Generator Loss: 0.4821
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Epoch 1/1-Step 1280... Discriminator Loss: 1.6181... Generator Loss: 0.4695
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Epoch 1/1-Step 1290... Discriminator Loss: 1.6461... Generator Loss: 0.5510
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|
Epoch 1/1-Step 1300... Discriminator Loss: 1.5410... Generator Loss: 0.5102
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
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nnXOXYIURnkaskauqIw6cLTGaAnAkhfFKEBUo57A0R2fsdal974P4DqtS/3NBx6fw3zw/wfAWbVK
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RhBDxQ8O8fwfCqo3+B0Ar3vv/5S++gFEcxA4QtqD3vvnvPcnvff3Q8biv733v4ojqqXovb8G4Kpz
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LlbtjLUhj+T44E5rXR6yweLLAN4E8A6AP7rbBpQDtv0XIDTx/wD8VP99GbIufhHAWwD+C0D1brf1
|
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A1zbLwJ4QT8/AOBHAN4G8D0AmbvdvgNcx+MAfqxj9C8Apo/y+AD4FoA3ALwK4O8AZG7X+ITIvYCA
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Fe0LgEwAAAAASUVORK5CYII=
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1310... Discriminator Loss: 1.6111... Generator Loss: 0.4807
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Epoch 1/1-Step 1320... Discriminator Loss: 1.7205... Generator Loss: 0.5266
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Epoch 1/1-Step 1330... Discriminator Loss: 1.6384... Generator Loss: 0.5218
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Epoch 1/1-Step 1340... Discriminator Loss: 1.6635... Generator Loss: 0.6340
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Epoch 1/1-Step 1350... Discriminator Loss: 1.6056... Generator Loss: 0.5998
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Epoch 1/1-Step 1360... Discriminator Loss: 1.5510... Generator Loss: 0.6269
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Epoch 1/1-Step 1370... Discriminator Loss: 1.5337... Generator Loss: 0.5658
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Epoch 1/1-Step 1380... Discriminator Loss: 1.5314... Generator Loss: 0.5803
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Epoch 1/1-Step 1390... Discriminator Loss: 1.5945... Generator Loss: 0.5902
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Epoch 1/1-Step 1400... Discriminator Loss: 1.6117... Generator Loss: 0.5127
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</pre>
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</div>
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</div>
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1410... Discriminator Loss: 1.6758... Generator Loss: 0.4837
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Epoch 1/1-Step 1420... Discriminator Loss: 1.6267... Generator Loss: 0.5655
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Epoch 1/1-Step 1430... Discriminator Loss: 1.5508... Generator Loss: 0.5399
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Epoch 1/1-Step 1440... Discriminator Loss: 1.6216... Generator Loss: 0.5033
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Epoch 1/1-Step 1450... Discriminator Loss: 1.5129... Generator Loss: 0.5440
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Epoch 1/1-Step 1460... Discriminator Loss: 1.5355... Generator Loss: 0.5692
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Epoch 1/1-Step 1470... Discriminator Loss: 1.7166... Generator Loss: 0.4328
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Epoch 1/1-Step 1480... Discriminator Loss: 1.5201... Generator Loss: 0.6278
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Epoch 1/1-Step 1490... Discriminator Loss: 1.5664... Generator Loss: 0.5372
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Epoch 1/1-Step 1500... Discriminator Loss: 1.5953... Generator Loss: 0.4794
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</pre>
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</div>
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</div>
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8/DoQ/gH38OjD+EffA+PPoR/8D08+hD+wffw6EP4B9/Dow/hH3wPjz6Ef/A9PPoQ/w8i7XVQkMum
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MQAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1510... Discriminator Loss: 1.5750... Generator Loss: 0.5716
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Epoch 1/1-Step 1520... Discriminator Loss: 1.5316... Generator Loss: 0.6153
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Epoch 1/1-Step 1530... Discriminator Loss: 1.5014... Generator Loss: 0.5747
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Epoch 1/1-Step 1540... Discriminator Loss: 1.6495... Generator Loss: 0.4497
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Epoch 1/1-Step 1550... Discriminator Loss: 1.6587... Generator Loss: 0.4627
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Epoch 1/1-Step 1560... Discriminator Loss: 1.5300... Generator Loss: 0.5422
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Epoch 1/1-Step 1570... Discriminator Loss: 1.5563... Generator Loss: 0.5813
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Epoch 1/1-Step 1580... Discriminator Loss: 1.6175... Generator Loss: 0.6056
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Epoch 1/1-Step 1590... Discriminator Loss: 1.5851... Generator Loss: 0.5327
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Epoch 1/1-Step 1600... Discriminator Loss: 1.6242... Generator Loss: 0.4894
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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AABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1610... Discriminator Loss: 1.5834... Generator Loss: 0.5294
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Epoch 1/1-Step 1620... Discriminator Loss: 1.6298... Generator Loss: 0.5103
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Epoch 1/1-Step 1630... Discriminator Loss: 1.6286... Generator Loss: 0.5374
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Epoch 1/1-Step 1640... Discriminator Loss: 1.5931... Generator Loss: 0.5947
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Epoch 1/1-Step 1650... Discriminator Loss: 1.4749... Generator Loss: 0.5701
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Epoch 1/1-Step 1660... Discriminator Loss: 1.7635... Generator Loss: 0.4730
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Epoch 1/1-Step 1670... Discriminator Loss: 1.5748... Generator Loss: 0.4961
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Epoch 1/1-Step 1680... Discriminator Loss: 1.4538... Generator Loss: 0.5815
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Epoch 1/1-Step 1690... Discriminator Loss: 1.5649... Generator Loss: 0.5636
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Epoch 1/1-Step 1700... Discriminator Loss: 1.6351... Generator Loss: 0.5656
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1710... Discriminator Loss: 1.5923... Generator Loss: 0.5038
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Epoch 1/1-Step 1720... Discriminator Loss: 1.5411... Generator Loss: 0.5850
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Epoch 1/1-Step 1730... Discriminator Loss: 1.5569... Generator Loss: 0.5380
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Epoch 1/1-Step 1740... Discriminator Loss: 1.6375... Generator Loss: 0.5668
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Epoch 1/1-Step 1750... Discriminator Loss: 1.5744... Generator Loss: 0.5069
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Epoch 1/1-Step 1760... Discriminator Loss: 1.4704... Generator Loss: 0.5417
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Epoch 1/1-Step 1770... Discriminator Loss: 1.5687... Generator Loss: 0.5396
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Epoch 1/1-Step 1780... Discriminator Loss: 1.5869... Generator Loss: 0.5047
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Epoch 1/1-Step 1790... Discriminator Loss: 1.7663... Generator Loss: 0.4312
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Epoch 1/1-Step 1800... Discriminator Loss: 1.4238... Generator Loss: 0.6334
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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AAAASUVORK5CYII=
|
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 1810... Discriminator Loss: 1.5628... Generator Loss: 0.5775
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Epoch 1/1-Step 1820... Discriminator Loss: 1.6301... Generator Loss: 0.5084
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Epoch 1/1-Step 1830... Discriminator Loss: 1.5955... Generator Loss: 0.5984
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Epoch 1/1-Step 1840... Discriminator Loss: 1.5123... Generator Loss: 0.6310
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Epoch 1/1-Step 1850... Discriminator Loss: 1.4927... Generator Loss: 0.5799
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Epoch 1/1-Step 1860... Discriminator Loss: 1.5843... Generator Loss: 0.6180
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Epoch 1/1-Step 1870... Discriminator Loss: 1.5226... Generator Loss: 0.5901
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Epoch 1/1-Step 1880... Discriminator Loss: 1.7258... Generator Loss: 0.4123
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Epoch 1/1-Step 1890... Discriminator Loss: 1.4861... Generator Loss: 0.5732
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Epoch 1/1-Step 1900... Discriminator Loss: 1.6749... Generator Loss: 0.5128
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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AmYQ4cEPCJhBhAc/IGAGER78gIAZxH8BGB+hwEyv4r0AAAAASUVORK5CYII=
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
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<pre>Epoch 1/1-Step 1910... Discriminator Loss: 1.5475... Generator Loss: 0.6490
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Epoch 1/1-Step 1920... Discriminator Loss: 1.5889... Generator Loss: 0.5187
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Epoch 1/1-Step 1930... Discriminator Loss: 1.4351... Generator Loss: 0.5514
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Epoch 1/1-Step 1940... Discriminator Loss: 1.6251... Generator Loss: 0.5055
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Epoch 1/1-Step 1950... Discriminator Loss: 1.6869... Generator Loss: 0.5026
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Epoch 1/1-Step 1960... Discriminator Loss: 1.6051... Generator Loss: 0.5311
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Epoch 1/1-Step 1970... Discriminator Loss: 1.5428... Generator Loss: 0.6195
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Epoch 1/1-Step 1980... Discriminator Loss: 1.5524... Generator Loss: 0.7520
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Epoch 1/1-Step 1990... Discriminator Loss: 1.5038... Generator Loss: 0.6006
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|
Epoch 1/1-Step 2000... Discriminator Loss: 1.4429... Generator Loss: 0.5845
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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p2qcAAAAAElFTkSuQmCC
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/1-Step 2010... Discriminator Loss: 1.6835... Generator Loss: 0.5229
|
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Epoch 1/1-Step 2020... Discriminator Loss: 1.6322... Generator Loss: 0.4383
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Epoch 1/1-Step 2030... Discriminator Loss: 1.5226... Generator Loss: 0.6373
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Epoch 1/1-Step 2040... Discriminator Loss: 1.5326... Generator Loss: 0.5994
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Epoch 1/1-Step 2050... Discriminator Loss: 1.6601... Generator Loss: 0.4407
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Epoch 1/1-Step 2060... Discriminator Loss: 1.4935... Generator Loss: 0.6701
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Epoch 1/1-Step 2070... Discriminator Loss: 1.4603... Generator Loss: 0.6488
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Epoch 1/1-Step 2080... Discriminator Loss: 1.5661... Generator Loss: 0.6325
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Epoch 1/1-Step 2090... Discriminator Loss: 1.5542... Generator Loss: 0.5592
|
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Epoch 1/1-Step 2100... Discriminator Loss: 1.4832... Generator Loss: 0.5962
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/1-Step 2110... Discriminator Loss: 1.6266... Generator Loss: 0.4967
|
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Epoch 1/1-Step 2120... Discriminator Loss: 1.4847... Generator Loss: 0.6371
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Epoch 1/1-Step 2130... Discriminator Loss: 1.6838... Generator Loss: 0.6783
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Epoch 1/1-Step 2140... Discriminator Loss: 1.4585... Generator Loss: 0.6055
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Epoch 1/1-Step 2150... Discriminator Loss: 1.5668... Generator Loss: 0.6090
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Epoch 1/1-Step 2160... Discriminator Loss: 1.5711... Generator Loss: 0.5704
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Epoch 1/1-Step 2170... Discriminator Loss: 1.6091... Generator Loss: 0.4876
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Epoch 1/1-Step 2180... Discriminator Loss: 1.5204... Generator Loss: 0.5957
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Epoch 1/1-Step 2190... Discriminator Loss: 1.5270... Generator Loss: 0.6346
|
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Epoch 1/1-Step 2200... Discriminator Loss: 1.4999... Generator Loss: 0.4813
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
|
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</div>
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</div>
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|
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<div class="output_area">
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|
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 2210... Discriminator Loss: 1.5486... Generator Loss: 0.5724
|
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Epoch 1/1-Step 2220... Discriminator Loss: 1.5582... Generator Loss: 0.5687
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Epoch 1/1-Step 2230... Discriminator Loss: 1.6047... Generator Loss: 0.4993
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Epoch 1/1-Step 2240... Discriminator Loss: 1.4981... Generator Loss: 0.5811
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Epoch 1/1-Step 2250... Discriminator Loss: 1.5765... Generator Loss: 0.5936
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Epoch 1/1-Step 2260... Discriminator Loss: 1.4636... Generator Loss: 0.5365
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Epoch 1/1-Step 2270... Discriminator Loss: 1.5911... Generator Loss: 0.5017
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Epoch 1/1-Step 2280... Discriminator Loss: 1.5097... Generator Loss: 0.5504
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|
Epoch 1/1-Step 2290... Discriminator Loss: 1.5114... Generator Loss: 0.6006
|
|
Epoch 1/1-Step 2300... Discriminator Loss: 1.5466... Generator Loss: 0.4722
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
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>
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</div>
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</div>
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|
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 2310... Discriminator Loss: 1.5663... Generator Loss: 0.5642
|
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Epoch 1/1-Step 2320... Discriminator Loss: 1.5616... Generator Loss: 0.5305
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Epoch 1/1-Step 2330... Discriminator Loss: 1.5452... Generator Loss: 0.4717
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Epoch 1/1-Step 2340... Discriminator Loss: 1.5097... Generator Loss: 0.5591
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Epoch 1/1-Step 2350... Discriminator Loss: 1.5448... Generator Loss: 0.6524
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Epoch 1/1-Step 2360... Discriminator Loss: 1.5724... Generator Loss: 0.5009
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Epoch 1/1-Step 2370... Discriminator Loss: 1.5217... Generator Loss: 0.6050
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Epoch 1/1-Step 2380... Discriminator Loss: 1.4962... Generator Loss: 0.5783
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Epoch 1/1-Step 2390... Discriminator Loss: 1.5700... Generator Loss: 0.6142
|
|
Epoch 1/1-Step 2400... Discriminator Loss: 1.4578... Generator Loss: 0.5684
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 2410... Discriminator Loss: 1.5131... Generator Loss: 0.5926
|
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Epoch 1/1-Step 2420... Discriminator Loss: 1.5050... Generator Loss: 0.7034
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Epoch 1/1-Step 2430... Discriminator Loss: 1.5339... Generator Loss: 0.5644
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Epoch 1/1-Step 2440... Discriminator Loss: 1.4892... Generator Loss: 0.6411
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Epoch 1/1-Step 2450... Discriminator Loss: 1.4928... Generator Loss: 0.5780
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Epoch 1/1-Step 2460... Discriminator Loss: 1.4603... Generator Loss: 0.5994
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Epoch 1/1-Step 2470... Discriminator Loss: 1.5285... Generator Loss: 0.6405
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Epoch 1/1-Step 2480... Discriminator Loss: 1.4590... Generator Loss: 0.6900
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Epoch 1/1-Step 2490... Discriminator Loss: 1.5542... Generator Loss: 0.6006
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Epoch 1/1-Step 2500... Discriminator Loss: 1.4520... Generator Loss: 0.7350
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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TkSuQmCC
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/1-Step 2510... Discriminator Loss: 1.5680... Generator Loss: 0.5199
|
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Epoch 1/1-Step 2520... Discriminator Loss: 1.4815... Generator Loss: 0.5623
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Epoch 1/1-Step 2530... Discriminator Loss: 1.4943... Generator Loss: 0.6459
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Epoch 1/1-Step 2540... Discriminator Loss: 1.4227... Generator Loss: 0.6303
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Epoch 1/1-Step 2550... Discriminator Loss: 1.4773... Generator Loss: 0.6954
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Epoch 1/1-Step 2560... Discriminator Loss: 1.5832... Generator Loss: 0.6401
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Epoch 1/1-Step 2570... Discriminator Loss: 1.5105... Generator Loss: 0.6780
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Epoch 1/1-Step 2580... Discriminator Loss: 1.5102... Generator Loss: 0.5282
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Epoch 1/1-Step 2590... Discriminator Loss: 1.5095... Generator Loss: 0.5546
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|
Epoch 1/1-Step 2600... Discriminator Loss: 1.4809... Generator Loss: 0.5799
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
|
>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 2610... Discriminator Loss: 1.4600... Generator Loss: 0.6504
|
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Epoch 1/1-Step 2620... Discriminator Loss: 1.5427... Generator Loss: 0.6289
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Epoch 1/1-Step 2630... Discriminator Loss: 1.6130... Generator Loss: 0.5409
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Epoch 1/1-Step 2640... Discriminator Loss: 1.5036... Generator Loss: 0.5403
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Epoch 1/1-Step 2650... Discriminator Loss: 1.5142... Generator Loss: 0.6227
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Epoch 1/1-Step 2660... Discriminator Loss: 1.4610... Generator Loss: 0.6740
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Epoch 1/1-Step 2670... Discriminator Loss: 1.5189... Generator Loss: 0.6304
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Epoch 1/1-Step 2680... Discriminator Loss: 1.4857... Generator Loss: 0.5588
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Epoch 1/1-Step 2690... Discriminator Loss: 1.4780... Generator Loss: 0.6343
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|
Epoch 1/1-Step 2700... Discriminator Loss: 1.4853... Generator Loss: 0.6399
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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"
|
|
>
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</div>
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</div>
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|
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<div class="output_area">
|
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|
|
<div class="prompt"></div>
|
|
|
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|
|
<div class="output_subarea output_stream output_stdout output_text">
|
|
<pre>Epoch 1/1-Step 2710... Discriminator Loss: 1.4660... Generator Loss: 0.5470
|
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Epoch 1/1-Step 2720... Discriminator Loss: 1.6547... Generator Loss: 0.5747
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Epoch 1/1-Step 2730... Discriminator Loss: 1.6056... Generator Loss: 0.5382
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Epoch 1/1-Step 2740... Discriminator Loss: 1.5344... Generator Loss: 0.6217
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Epoch 1/1-Step 2750... Discriminator Loss: 1.4596... Generator Loss: 0.6441
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Epoch 1/1-Step 2760... Discriminator Loss: 1.5443... Generator Loss: 0.5064
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Epoch 1/1-Step 2770... Discriminator Loss: 1.5773... Generator Loss: 0.5066
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|
Epoch 1/1-Step 2780... Discriminator Loss: 1.5259... Generator Loss: 0.6386
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|
Epoch 1/1-Step 2790... Discriminator Loss: 1.5320... Generator Loss: 0.5977
|
|
Epoch 1/1-Step 2800... Discriminator Loss: 1.5102... Generator Loss: 0.6136
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|
</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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AAAASUVORK5CYII=
|
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"
|
|
>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 2810... Discriminator Loss: 1.4854... Generator Loss: 0.6670
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Epoch 1/1-Step 2820... Discriminator Loss: 1.5372... Generator Loss: 0.6835
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Epoch 1/1-Step 2830... Discriminator Loss: 1.5328... Generator Loss: 0.7039
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Epoch 1/1-Step 2840... Discriminator Loss: 1.6758... Generator Loss: 0.5030
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Epoch 1/1-Step 2850... Discriminator Loss: 1.3977... Generator Loss: 0.6095
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Epoch 1/1-Step 2860... Discriminator Loss: 1.6221... Generator Loss: 0.5926
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Epoch 1/1-Step 2870... Discriminator Loss: 1.5251... Generator Loss: 0.6481
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Epoch 1/1-Step 2880... Discriminator Loss: 1.4604... Generator Loss: 0.5213
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Epoch 1/1-Step 2890... Discriminator Loss: 1.5202... Generator Loss: 0.5007
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Epoch 1/1-Step 2900... Discriminator Loss: 1.4630... Generator Loss: 0.6140
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</pre>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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AAAAAElFTkSuQmCC
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"
|
|
>
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|
</div>
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|
</div>
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|
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<div class="output_area">
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|
|
<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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|
<pre>Epoch 1/1-Step 2910... Discriminator Loss: 1.3858... Generator Loss: 0.6565
|
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Epoch 1/1-Step 2920... Discriminator Loss: 1.4337... Generator Loss: 0.7014
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Epoch 1/1-Step 2930... Discriminator Loss: 1.5349... Generator Loss: 0.5818
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Epoch 1/1-Step 2940... Discriminator Loss: 1.3969... Generator Loss: 0.6059
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Epoch 1/1-Step 2950... Discriminator Loss: 1.5284... Generator Loss: 0.6069
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Epoch 1/1-Step 2960... Discriminator Loss: 1.4804... Generator Loss: 0.5743
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Epoch 1/1-Step 2970... Discriminator Loss: 1.4891... Generator Loss: 0.7033
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Epoch 1/1-Step 2980... Discriminator Loss: 1.5484... Generator Loss: 0.5540
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Epoch 1/1-Step 2990... Discriminator Loss: 1.5461... Generator Loss: 0.6244
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|
Epoch 1/1-Step 3000... Discriminator Loss: 1.4820... Generator Loss: 0.6587
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</pre>
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|
</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
|
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AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMJVl2HnZuLG/fcs+szNq7ep2Z7p4ZcoYckqA4pkDL
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d2t8YuZeRMQMIjr3IiJmEPHBj4iYQcQHPyJiBhEf/IiIGUR88CMiZhDxwY+ImEHEBz8iYgYRH/yI
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iBnE/wH62H+kwvGIKAAAAABJRU5ErkJggg==
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"
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>
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</div>
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</div>
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<div class="output_area">
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<div class="prompt"></div>
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<div class="output_subarea output_stream output_stdout output_text">
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<pre>Epoch 1/1-Step 3010... Discriminator Loss: 1.5879... Generator Loss: 0.5517
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Epoch 1/1-Step 3020... Discriminator Loss: 1.4577... Generator Loss: 0.6005
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Epoch 1/1-Step 3030... Discriminator Loss: 1.4562... Generator Loss: 0.6765
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Epoch 1/1-Step 3040... Discriminator Loss: 1.5195... Generator Loss: 0.6498
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Epoch 1/1-Step 3050... Discriminator Loss: 1.4982... Generator Loss: 0.6116
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Epoch 1/1-Step 3060... Discriminator Loss: 1.4935... Generator Loss: 0.6510
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Epoch 1/1-Step 3070... Discriminator Loss: 1.5386... Generator Loss: 0.5516
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Epoch 1/1-Step 3080... Discriminator Loss: 1.4903... Generator Loss: 0.6672
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Epoch 1/1-Step 3090... Discriminator Loss: 1.5832... Generator Loss: 0.5869
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Epoch 1/1-Step 3100... Discriminator Loss: 1.4703... Generator Loss: 0.6179
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</pre>
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</div>
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</div>
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<div class="prompt"></div>
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Pws/5cN3pU7VdXQ8jpLxrlKj7ZDadQYULjyj+f5zNOaFor1ffsjPWYuSga5vevq7smwG2VqRQm01
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LFi/KnTOLd+3TU6c0sKth0sUp0LJTz1V9Zk6TuOvajw3pvVaXAu6+gEBAbfAHqfl5nHkhF8Nilpu
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5ZUxlEn1ZbRNGoOaBBRHZhRtKwPiqMLuphnL4q43PBbFxnJ+xreDpaWLBWMZ60uv+raRhzUp+HEr
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0tXXGtnqXln0/VjqWR9ncv4HFn/80uRYjeRrCmoQjm+SjVkj3bpUptxlctT8XXbHanRerm/tWZhV
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jcC+Gd02KYovK99eqhurzKu234hcbyNS9YnVfcnJPqMt/ztDcuHdIC3CS5Hv4/g66S0OPSM4fNj/
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z3P3/yGs+AEBBxDhxg8IOICQnRbZuyMnE1kF0AWwdrvP7iPMI/TnfsVHqS/Azvpzyjl36Daf2dsb
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HwBE5IfOuSf39KR3EaE/9y8+Sn0B7mx/AtUPCDiACDd+QMABxL248f/6HpzzbiL05/7FR6kvwB3s
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z57v8QMCAu49AtUPCDiA2NMbX0Q+JyLnReSiiHx9L8/9YSEiJ0XkeyLyuoi8JiJf1eOzIvIfInJB
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/2/e7rfuJ4hIJCKviMjz+vcZEXlR5+gfRUgH/D6HiMyIyHdE5E0ReUNEPrWf50dEvqbX2jkR+ZaI
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lO/U/OzZjS8iEYC/BPB5AI8A+LKIPLJX578DSAD8rnPuEQBPAfhNbf/XAbzgnHsIwAv6937CVwG8
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QX/vZy3FvwDwr865jwN4DL5f+3J+7rrWpXNuT/4B+BSAf6O/nwXw7F6d/y70518APAPgPICjeuwo
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gPP3um276MMJ+JvhaQDPAxD4AJH8+83Z/fwPQAPAItRuRcf35fzAS9ldBTALn1PzPIBfvFPzs5dU
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P+tIhh3p9N2PEJHTAD4B4EUAR5xzmVzLDQBHbvG1+xF/DuD3YMV85vABtBTvE5wBsArgb3Xr8jci
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UsM+nR/n3DKATOvybQBtfECty/dDMO7tEiJSB/BPAH7bOdfh95x/DO8LN4mI/BKAFefcS7f98P5A
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HsATAP7KOfcJ+NDwd9D6fTY/H0rr8nbYyxt/GcBJ+vuWOn33K0SkAH/T/71z7jk9fFNEjur7RwGs
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3Or79xk+DeCLIrIEXxjlafg98ozKqAP7a46uAbjmvGIU4FWjnsD+nZ+J1qVzLgbwDq1L/cwHnp+9
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vPF/AOAhtUoW4Q0V393D838oqN7gNwG84Zz7U3rru/Cag8A+0h50zj3rnDvhnDsNPxf/5Zz7Cvap
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lqJz7gaAqyLysB7KtCH35fzgbmtd7rHB4gsAfgLgEoA/uNcGlF22/efhaeKrAH6k/74Avy9+AcAF
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AP8JYPZet/UD9O0XADyvrx8E8H0AFwF8G0DpXrdvF/14HMAPdY7+GUBzP88PgD8E8CaAcwD+DkDp
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Ts1PiNwLCDiACMa9gIADiHDjBwQcQIQbPyDgACLc+AEBBxDhxg8IOIAIN35AwAFEuPEDAg4gwo0f
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EHAA8X8dn481LhCDrAAAAABJRU5ErkJggg==
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"
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>
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<pre>Epoch 1/1-Step 3110... Discriminator Loss: 1.5633... Generator Loss: 0.5450
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Epoch 1/1-Step 3120... Discriminator Loss: 1.5169... Generator Loss: 0.5910
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Epoch 1/1-Step 3130... Discriminator Loss: 1.5457... Generator Loss: 0.6699
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Epoch 1/1-Step 3140... Discriminator Loss: 1.4988... Generator Loss: 0.5929
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Epoch 1/1-Step 3150... Discriminator Loss: 1.4436... Generator Loss: 0.6491
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</pre>
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<pre>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
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<span class="ansi-red-fg">KeyboardInterrupt</span> Traceback (most recent call last)
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<span class="ansi-green-fg"><ipython-input-94-9e4afd78e3c4></span> in <span class="ansi-cyan-fg"><module></span><span class="ansi-blue-fg">()</span>
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<span class="ansi-green-intense-fg ansi-bold"> 13</span> <span class="ansi-green-fg">with</span> tf<span class="ansi-blue-fg">.</span>Graph<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">.</span>as_default<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 14</span> train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
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<span class="ansi-green-fg">---> 15</span><span class="ansi-red-fg"> celeba_dataset.shape, celeba_dataset.image_mode)
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</span>
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<span class="ansi-green-fg"><ipython-input-13-10fc281759f1></span> in <span class="ansi-cyan-fg">train</span><span class="ansi-blue-fg">(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 77</span> _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,
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<span class="ansi-green-intense-fg ansi-bold"> 78</span> inputs_real<span class="ansi-blue-fg">:</span> batch_images<span class="ansi-blue-fg">,</span>
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<span class="ansi-green-fg">---> 79</span><span class="ansi-red-fg"> lr_rate: learning_rate})
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</span><span class="ansi-green-intense-fg ansi-bold"> 80</span> _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,
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<span class="ansi-green-intense-fg ansi-bold"> 81</span> inputs_real<span class="ansi-blue-fg">:</span> batch_images<span class="ansi-blue-fg">,</span>
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<span class="ansi-green-fg">/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">run</span><span class="ansi-blue-fg">(self, fetches, feed_dict, options, run_metadata)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 787</span> <span class="ansi-green-fg">try</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 788</span> result = self._run(None, fetches, feed_dict, options_ptr,
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<span class="ansi-green-fg">--> 789</span><span class="ansi-red-fg"> run_metadata_ptr)
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</span><span class="ansi-green-intense-fg ansi-bold"> 790</span> <span class="ansi-green-fg">if</span> run_metadata<span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 791</span> proto_data <span class="ansi-blue-fg">=</span> tf_session<span class="ansi-blue-fg">.</span>TF_GetBuffer<span class="ansi-blue-fg">(</span>run_metadata_ptr<span class="ansi-blue-fg">)</span>
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<span class="ansi-green-fg">/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">_run</span><span class="ansi-blue-fg">(self, handle, fetches, feed_dict, options, run_metadata)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 995</span> <span class="ansi-green-fg">if</span> final_fetches <span class="ansi-green-fg">or</span> final_targets<span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 996</span> results = self._do_run(handle, final_targets, final_fetches,
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<span class="ansi-green-fg">--> 997</span><span class="ansi-red-fg"> feed_dict_string, options, run_metadata)
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</span><span class="ansi-green-intense-fg ansi-bold"> 998</span> <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 999</span> results <span class="ansi-blue-fg">=</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span>
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<span class="ansi-green-fg">/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">_do_run</span><span class="ansi-blue-fg">(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1130</span> <span class="ansi-green-fg">if</span> handle <span class="ansi-green-fg">is</span> <span class="ansi-green-fg">None</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1131</span> return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
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<span class="ansi-green-fg">-> 1132</span><span class="ansi-red-fg"> target_list, options, run_metadata)
|
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</span><span class="ansi-green-intense-fg ansi-bold"> 1133</span> <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1134</span> return self._do_call(_prun_fn, self._session, handle, feed_dict,
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<span class="ansi-green-fg">/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">_do_call</span><span class="ansi-blue-fg">(self, fn, *args)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1137</span> <span class="ansi-green-fg">def</span> _do_call<span class="ansi-blue-fg">(</span>self<span class="ansi-blue-fg">,</span> fn<span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">*</span>args<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1138</span> <span class="ansi-green-fg">try</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-green-fg">-> 1139</span><span class="ansi-red-fg"> </span><span class="ansi-green-fg">return</span> fn<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">*</span>args<span class="ansi-blue-fg">)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1140</span> <span class="ansi-green-fg">except</span> errors<span class="ansi-blue-fg">.</span>OpError <span class="ansi-green-fg">as</span> e<span class="ansi-blue-fg">:</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1141</span> message <span class="ansi-blue-fg">=</span> compat<span class="ansi-blue-fg">.</span>as_text<span class="ansi-blue-fg">(</span>e<span class="ansi-blue-fg">.</span>message<span class="ansi-blue-fg">)</span>
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<span class="ansi-green-fg">/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py</span> in <span class="ansi-cyan-fg">_run_fn</span><span class="ansi-blue-fg">(session, feed_dict, fetch_list, target_list, options, run_metadata)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1119</span> return tf_session.TF_Run(session, options,
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<span class="ansi-green-intense-fg ansi-bold"> 1120</span> feed_dict<span class="ansi-blue-fg">,</span> fetch_list<span class="ansi-blue-fg">,</span> target_list<span class="ansi-blue-fg">,</span>
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<span class="ansi-green-fg">-> 1121</span><span class="ansi-red-fg"> status, run_metadata)
|
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</span><span class="ansi-green-intense-fg ansi-bold"> 1122</span>
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<span class="ansi-green-intense-fg ansi-bold"> 1123</span> <span class="ansi-green-fg">def</span> _prun_fn<span class="ansi-blue-fg">(</span>session<span class="ansi-blue-fg">,</span> handle<span class="ansi-blue-fg">,</span> feed_dict<span class="ansi-blue-fg">,</span> fetch_list<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">:</span>
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<span class="ansi-red-fg">KeyboardInterrupt</span>: </pre>
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<h3 id="Submitting-This-Project">Submitting This Project<a class="anchor-link" href="#Submitting-This-Project">¶</a></h3><p>When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.</p>
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