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mihkell/dlnd_imageClassification.html

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dlnd_imageClassification.html
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>dlnd_image_classification</title><script src="https://unpkg.com/jupyter-js-widgets@2.0.*/dist/embed.js"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
<style type="text/css">
/*!
*
* Twitter Bootstrap
*
*/
/*!
* Bootstrap v3.3.7 (http://getbootstrap.com)
* Copyright 2011-2016 Twitter, Inc.
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
*/
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
font-family: sans-serif;
-ms-text-size-adjust: 100%;
-webkit-text-size-adjust: 100%;
}
body {
margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
display: block;
}
audio,
canvas,
progress,
video {
display: inline-block;
vertical-align: baseline;
}
audio:not([controls]) {
display: none;
height: 0;
}
[hidden],
template {
display: none;
}
a {
background-color: transparent;
}
a:active,
a:hover {
outline: 0;
}
abbr[title] {
border-bottom: 1px dotted;
}
b,
strong {
font-weight: bold;
}
dfn {
font-style: italic;
}
h1 {
font-size: 2em;
margin: 0.67em 0;
}
mark {
background: #ff0;
color: #000;
}
small {
font-size: 80%;
}
sub,
sup {
font-size: 75%;
line-height: 0;
position: relative;
vertical-align: baseline;
}
sup {
top: -0.5em;
}
sub {
bottom: -0.25em;
}
img {
border: 0;
}
svg:not(:root) {
overflow: hidden;
}
figure {
margin: 1em 40px;
}
hr {
box-sizing: content-box;
height: 0;
}
pre {
overflow: auto;
}
code,
kbd,
pre,
samp {
font-family: monospace, monospace;
font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
color: inherit;
font: inherit;
margin: 0;
}
button {
overflow: visible;
}
button,
select {
text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
-webkit-appearance: button;
cursor: pointer;
}
button[disabled],
html input[disabled] {
cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
border: 0;
padding: 0;
}
input {
line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
box-sizing: border-box;
padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
height: auto;
}
input[type="search"] {
-webkit-appearance: textfield;
box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
-webkit-appearance: none;
}
fieldset {
border: 1px solid #c0c0c0;
margin: 0 2px;
padding: 0.35em 0.625em 0.75em;
}
legend {
border: 0;
padding: 0;
}
textarea {
overflow: auto;
}
optgroup {
font-weight: bold;
}
table {
border-collapse: collapse;
border-spacing: 0;
}
td,
th {
padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
*,
*:before,
*:after {
background: transparent !important;
color: #000 !important;
box-shadow: none !important;
text-shadow: none !important;
}
a,
a:visited {
text-decoration: underline;
}
a[href]:after {
content: " (" attr(href) ")";
}
abbr[title]:after {
content: " (" attr(title) ")";
}
a[href^="#"]:after,
a[href^="javascript:"]:after {
content: "";
}
pre,
blockquote {
border: 1px solid #999;
page-break-inside: avoid;
}
thead {
display: table-header-group;
}
tr,
img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
p,
h2,
h3 {
orphans: 3;
widows: 3;
}
h2,
h3 {
page-break-after: avoid;
}
.navbar {
display: none;
}
.btn > .caret,
.dropup > .btn > .caret {
border-top-color: #000 !important;
}
.label {
border: 1px solid #000;
}
.table {
border-collapse: collapse !important;
}
.table td,
.table th {
background-color: #fff !important;
}
.table-bordered th,
.table-bordered td {
border: 1px solid #ddd !important;
}
}
@font-face {
font-family: 'Glyphicons Halflings';
src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
position: relative;
top: 1px;
display: inline-block;
font-family: 'Glyphicons Halflings';
font-style: normal;
font-weight: normal;
line-height: 1;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
content: "\002a";
}
.glyphicon-plus:before {
content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
content: "\20ac";
}
.glyphicon-minus:before {
content: "\2212";
}
.glyphicon-cloud:before {
content: "\2601";
}
.glyphicon-envelope:before {
content: "\2709";
}
.glyphicon-pencil:before {
content: "\270f";
}
.glyphicon-glass:before {
content: "\e001";
}
.glyphicon-music:before {
content: "\e002";
}
.glyphicon-search:before {
content: "\e003";
}
.glyphicon-heart:before {
content: "\e005";
}
.glyphicon-star:before {
content: "\e006";
}
.glyphicon-star-empty:before {
content: "\e007";
}
.glyphicon-user:before {
content: "\e008";
}
.glyphicon-film:before {
content: "\e009";
}
.glyphicon-th-large:before {
content: "\e010";
}
.glyphicon-th:before {
content: "\e011";
}
.glyphicon-th-list:before {
content: "\e012";
}
.glyphicon-ok:before {
content: "\e013";
}
.glyphicon-remove:before {
content: "\e014";
}
.glyphicon-zoom-in:before {
content: "\e015";
}
.glyphicon-zoom-out:before {
content: "\e016";
}
.glyphicon-off:before {
content: "\e017";
}
.glyphicon-signal:before {
content: "\e018";
}
.glyphicon-cog:before {
content: "\e019";
}
.glyphicon-trash:before {
content: "\e020";
}
.glyphicon-home:before {
content: "\e021";
}
.glyphicon-file:before {
content: "\e022";
}
.glyphicon-time:before {
content: "\e023";
}
.glyphicon-road:before {
content: "\e024";
}
.glyphicon-download-alt:before {
content: "\e025";
}
.glyphicon-download:before {
content: "\e026";
}
.glyphicon-upload:before {
content: "\e027";
}
.glyphicon-inbox:before {
content: "\e028";
}
.glyphicon-play-circle:before {
content: "\e029";
}
.glyphicon-repeat:before {
content: "\e030";
}
.glyphicon-refresh:before {
content: "\e031";
}
.glyphicon-list-alt:before {
content: "\e032";
}
.glyphicon-lock:before {
content: "\e033";
}
.glyphicon-flag:before {
content: "\e034";
}
.glyphicon-headphones:before {
content: "\e035";
}
.glyphicon-volume-off:before {
content: "\e036";
}
.glyphicon-volume-down:before {
content: "\e037";
}
.glyphicon-volume-up:before {
content: "\e038";
}
.glyphicon-qrcode:before {
content: "\e039";
}
.glyphicon-barcode:before {
content: "\e040";
}
.glyphicon-tag:before {
content: "\e041";
}
.glyphicon-tags:before {
content: "\e042";
}
.glyphicon-book:before {
content: "\e043";
}
.glyphicon-bookmark:before {
content: "\e044";
}
.glyphicon-print:before {
content: "\e045";
}
.glyphicon-camera:before {
content: "\e046";
}
.glyphicon-font:before {
content: "\e047";
}
.glyphicon-bold:before {
content: "\e048";
}
.glyphicon-italic:before {
content: "\e049";
}
.glyphicon-text-height:before {
content: "\e050";
}
.glyphicon-text-width:before {
content: "\e051";
}
.glyphicon-align-left:before {
content: "\e052";
}
.glyphicon-align-center:before {
content: "\e053";
}
.glyphicon-align-right:before {
content: "\e054";
}
.glyphicon-align-justify:before {
content: "\e055";
}
.glyphicon-list:before {
content: "\e056";
}
.glyphicon-indent-left:before {
content: "\e057";
}
.glyphicon-indent-right:before {
content: "\e058";
}
.glyphicon-facetime-video:before {
content: "\e059";
}
.glyphicon-picture:before {
content: "\e060";
}
.glyphicon-map-marker:before {
content: "\e062";
}
.glyphicon-adjust:before {
content: "\e063";
}
.glyphicon-tint:before {
content: "\e064";
}
.glyphicon-edit:before {
content: "\e065";
}
.glyphicon-share:before {
content: "\e066";
}
.glyphicon-check:before {
content: "\e067";
}
.glyphicon-move:before {
content: "\e068";
}
.glyphicon-step-backward:before {
content: "\e069";
}
.glyphicon-fast-backward:before {
content: "\e070";
}
.glyphicon-backward:before {
content: "\e071";
}
.glyphicon-play:before {
content: "\e072";
}
.glyphicon-pause:before {
content: "\e073";
}
.glyphicon-stop:before {
content: "\e074";
}
.glyphicon-forward:before {
content: "\e075";
}
.glyphicon-fast-forward:before {
content: "\e076";
}
.glyphicon-step-forward:before {
content: "\e077";
}
.glyphicon-eject:before {
content: "\e078";
}
.glyphicon-chevron-left:before {
content: "\e079";
}
.glyphicon-chevron-right:before {
content: "\e080";
}
.glyphicon-plus-sign:before {
content: "\e081";
}
.glyphicon-minus-sign:before {
content: "\e082";
}
.glyphicon-remove-sign:before {
content: "\e083";
}
.glyphicon-ok-sign:before {
content: "\e084";
}
.glyphicon-question-sign:before {
content: "\e085";
}
.glyphicon-info-sign:before {
content: "\e086";
}
.glyphicon-screenshot:before {
content: "\e087";
}
.glyphicon-remove-circle:before {
content: "\e088";
}
.glyphicon-ok-circle:before {
content: "\e089";
}
.glyphicon-ban-circle:before {
content: "\e090";
}
.glyphicon-arrow-left:before {
content: "\e091";
}
.glyphicon-arrow-right:before {
content: "\e092";
}
.glyphicon-arrow-up:before {
content: "\e093";
}
.glyphicon-arrow-down:before {
content: "\e094";
}
.glyphicon-share-alt:before {
content: "\e095";
}
.glyphicon-resize-full:before {
content: "\e096";
}
.glyphicon-resize-small:before {
content: "\e097";
}
.glyphicon-exclamation-sign:before {
content: "\e101";
}
.glyphicon-gift:before {
content: "\e102";
}
.glyphicon-leaf:before {
content: "\e103";
}
.glyphicon-fire:before {
content: "\e104";
}
.glyphicon-eye-open:before {
content: "\e105";
}
.glyphicon-eye-close:before {
content: "\e106";
}
.glyphicon-warning-sign:before {
content: "\e107";
}
.glyphicon-plane:before {
content: "\e108";
}
.glyphicon-calendar:before {
content: "\e109";
}
.glyphicon-random:before {
content: "\e110";
}
.glyphicon-comment:before {
content: "\e111";
}
.glyphicon-magnet:before {
content: "\e112";
}
.glyphicon-chevron-up:before {
content: "\e113";
}
.glyphicon-chevron-down:before {
content: "\e114";
}
.glyphicon-retweet:before {
content: "\e115";
}
.glyphicon-shopping-cart:before {
content: "\e116";
}
.glyphicon-folder-close:before {
content: "\e117";
}
.glyphicon-folder-open:before {
content: "\e118";
}
.glyphicon-resize-vertical:before {
content: "\e119";
}
.glyphicon-resize-horizontal:before {
content: "\e120";
}
.glyphicon-hdd:before {
content: "\e121";
}
.glyphicon-bullhorn:before {
content: "\e122";
}
.glyphicon-bell:before {
content: "\e123";
}
.glyphicon-certificate:before {
content: "\e124";
}
.glyphicon-thumbs-up:before {
content: "\e125";
}
.glyphicon-thumbs-down:before {
content: "\e126";
}
.glyphicon-hand-right:before {
content: "\e127";
}
.glyphicon-hand-left:before {
content: "\e128";
}
.glyphicon-hand-up:before {
content: "\e129";
}
.glyphicon-hand-down:before {
content: "\e130";
}
.glyphicon-circle-arrow-right:before {
content: "\e131";
}
.glyphicon-circle-arrow-left:before {
content: "\e132";
}
.glyphicon-circle-arrow-up:before {
content: "\e133";
}
.glyphicon-circle-arrow-down:before {
content: "\e134";
}
.glyphicon-globe:before {
content: "\e135";
}
.glyphicon-wrench:before {
content: "\e136";
}
.glyphicon-tasks:before {
content: "\e137";
}
.glyphicon-filter:before {
content: "\e138";
}
.glyphicon-briefcase:before {
content: "\e139";
}
.glyphicon-fullscreen:before {
content: "\e140";
}
.glyphicon-dashboard:before {
content: "\e141";
}
.glyphicon-paperclip:before {
content: "\e142";
}
.glyphicon-heart-empty:before {
content: "\e143";
}
.glyphicon-link:before {
content: "\e144";
}
.glyphicon-phone:before {
content: "\e145";
}
.glyphicon-pushpin:before {
content: "\e146";
}
.glyphicon-usd:before {
content: "\e148";
}
.glyphicon-gbp:before {
content: "\e149";
}
.glyphicon-sort:before {
content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
content: "\e152";
}
.glyphicon-sort-by-order:before {
content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
content: "\e154";
}
.glyphicon-sort-by-attributes:before {
content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
content: "\e156";
}
.glyphicon-unchecked:before {
content: "\e157";
}
.glyphicon-expand:before {
content: "\e158";
}
.glyphicon-collapse-down:before {
content: "\e159";
}
.glyphicon-collapse-up:before {
content: "\e160";
}
.glyphicon-log-in:before {
content: "\e161";
}
.glyphicon-flash:before {
content: "\e162";
}
.glyphicon-log-out:before {
content: "\e163";
}
.glyphicon-new-window:before {
content: "\e164";
}
.glyphicon-record:before {
content: "\e165";
}
.glyphicon-save:before {
content: "\e166";
}
.glyphicon-open:before {
content: "\e167";
}
.glyphicon-saved:before {
content: "\e168";
}
.glyphicon-import:before {
content: "\e169";
}
.glyphicon-export:before {
content: "\e170";
}
.glyphicon-send:before {
content: "\e171";
}
.glyphicon-floppy-disk:before {
content: "\e172";
}
.glyphicon-floppy-saved:before {
content: "\e173";
}
.glyphicon-floppy-remove:before {
content: "\e174";
}
.glyphicon-floppy-save:before {
content: "\e175";
}
.glyphicon-floppy-open:before {
content: "\e176";
}
.glyphicon-credit-card:before {
content: "\e177";
}
.glyphicon-transfer:before {
content: "\e178";
}
.glyphicon-cutlery:before {
content: "\e179";
}
.glyphicon-header:before {
content: "\e180";
}
.glyphicon-compressed:before {
content: "\e181";
}
.glyphicon-earphone:before {
content: "\e182";
}
.glyphicon-phone-alt:before {
content: "\e183";
}
.glyphicon-tower:before {
content: "\e184";
}
.glyphicon-stats:before {
content: "\e185";
}
.glyphicon-sd-video:before {
content: "\e186";
}
.glyphicon-hd-video:before {
content: "\e187";
}
.glyphicon-subtitles:before {
content: "\e188";
}
.glyphicon-sound-stereo:before {
content: "\e189";
}
.glyphicon-sound-dolby:before {
content: "\e190";
}
.glyphicon-sound-5-1:before {
content: "\e191";
}
.glyphicon-sound-6-1:before {
content: "\e192";
}
.glyphicon-sound-7-1:before {
content: "\e193";
}
.glyphicon-copyright-mark:before {
content: "\e194";
}
.glyphicon-registration-mark:before {
content: "\e195";
}
.glyphicon-cloud-download:before {
content: "\e197";
}
.glyphicon-cloud-upload:before {
content: "\e198";
}
.glyphicon-tree-conifer:before {
content: "\e199";
}
.glyphicon-tree-deciduous:before {
content: "\e200";
}
.glyphicon-cd:before {
content: "\e201";
}
.glyphicon-save-file:before {
content: "\e202";
}
.glyphicon-open-file:before {
content: "\e203";
}
.glyphicon-level-up:before {
content: "\e204";
}
.glyphicon-copy:before {
content: "\e205";
}
.glyphicon-paste:before {
content: "\e206";
}
.glyphicon-alert:before {
content: "\e209";
}
.glyphicon-equalizer:before {
content: "\e210";
}
.glyphicon-king:before {
content: "\e211";
}
.glyphicon-queen:before {
content: "\e212";
}
.glyphicon-pawn:before {
content: "\e213";
}
.glyphicon-bishop:before {
content: "\e214";
}
.glyphicon-knight:before {
content: "\e215";
}
.glyphicon-baby-formula:before {
content: "\e216";
}
.glyphicon-tent:before {
content: "\26fa";
}
.glyphicon-blackboard:before {
content: "\e218";
}
.glyphicon-bed:before {
content: "\e219";
}
.glyphicon-apple:before {
content: "\f8ff";
}
.glyphicon-erase:before {
content: "\e221";
}
.glyphicon-hourglass:before {
content: "\231b";
}
.glyphicon-lamp:before {
content: "\e223";
}
.glyphicon-duplicate:before {
content: "\e224";
}
.glyphicon-piggy-bank:before {
content: "\e225";
}
.glyphicon-scissors:before {
content: "\e226";
}
.glyphicon-bitcoin:before {
content: "\e227";
}
.glyphicon-btc:before {
content: "\e227";
}
.glyphicon-xbt:before {
content: "\e227";
}
.glyphicon-yen:before {
content: "\00a5";
}
.glyphicon-jpy:before {
content: "\00a5";
}
.glyphicon-ruble:before {
content: "\20bd";
}
.glyphicon-rub:before {
content: "\20bd";
}
.glyphicon-scale:before {
content: "\e230";
}
.glyphicon-ice-lolly:before {
content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
content: "\e232";
}
.glyphicon-education:before {
content: "\e233";
}
.glyphicon-option-horizontal:before {
content: "\e234";
}
.glyphicon-option-vertical:before {
content: "\e235";
}
.glyphicon-menu-hamburger:before {
content: "\e236";
}
.glyphicon-modal-window:before {
content: "\e237";
}
.glyphicon-oil:before {
content: "\e238";
}
.glyphicon-grain:before {
content: "\e239";
}
.glyphicon-sunglasses:before {
content: "\e240";
}
.glyphicon-text-size:before {
content: "\e241";
}
.glyphicon-text-color:before {
content: "\e242";
}
.glyphicon-text-background:before {
content: "\e243";
}
.glyphicon-object-align-top:before {
content: "\e244";
}
.glyphicon-object-align-bottom:before {
content: "\e245";
}
.glyphicon-object-align-horizontal:before {
content: "\e246";
}
.glyphicon-object-align-left:before {
content: "\e247";
}
.glyphicon-object-align-vertical:before {
content: "\e248";
}
.glyphicon-object-align-right:before {
content: "\e249";
}
.glyphicon-triangle-right:before {
content: "\e250";
}
.glyphicon-triangle-left:before {
content: "\e251";
}
.glyphicon-triangle-bottom:before {
content: "\e252";
}
.glyphicon-triangle-top:before {
content: "\e253";
}
.glyphicon-console:before {
content: "\e254";
}
.glyphicon-superscript:before {
content: "\e255";
}
.glyphicon-subscript:before {
content: "\e256";
}
.glyphicon-menu-left:before {
content: "\e257";
}
.glyphicon-menu-right:before {
content: "\e258";
}
.glyphicon-menu-down:before {
content: "\e259";
}
.glyphicon-menu-up:before {
content: "\e260";
}
* {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
*:before,
*:after {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
html {
font-size: 10px;
-webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 13px;
line-height: 1.42857143;
color: #000;
background-color: #fff;
}
input,
button,
select,
textarea {
font-family: inherit;
font-size: inherit;
line-height: inherit;
}
a {
color: #337ab7;
text-decoration: none;
}
a:hover,
a:focus {
color: #23527c;
text-decoration: underline;
}
a:focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
figure {
margin: 0;
}
img {
vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
display: block;
max-width: 100%;
height: auto;
}
.img-rounded {
border-radius: 3px;
}
.img-thumbnail {
padding: 4px;
line-height: 1.42857143;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 2px;
-webkit-transition: all 0.2s ease-in-out;
-o-transition: all 0.2s ease-in-out;
transition: all 0.2s ease-in-out;
display: inline-block;
max-width: 100%;
height: auto;
}
.img-circle {
border-radius: 50%;
}
hr {
margin-top: 18px;
margin-bottom: 18px;
border: 0;
border-top: 1px solid #eeeeee;
}
.sr-only {
position: absolute;
width: 1px;
height: 1px;
margin: -1px;
padding: 0;
overflow: hidden;
clip: rect(0, 0, 0, 0);
border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
position: static;
width: auto;
height: auto;
margin: 0;
overflow: visible;
clip: auto;
}
[role="button"] {
cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
font-family: inherit;
font-weight: 500;
line-height: 1.1;
color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
font-weight: normal;
line-height: 1;
color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
margin-top: 18px;
margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
margin-top: 9px;
margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
font-size: 75%;
}
h1,
.h1 {
font-size: 33px;
}
h2,
.h2 {
font-size: 27px;
}
h3,
.h3 {
font-size: 23px;
}
h4,
.h4 {
font-size: 17px;
}
h5,
.h5 {
font-size: 13px;
}
h6,
.h6 {
font-size: 12px;
}
p {
margin: 0 0 9px;
}
.lead {
margin-bottom: 18px;
font-size: 14px;
font-weight: 300;
line-height: 1.4;
}
@media (min-width: 768px) {
.lead {
font-size: 19.5px;
}
}
small,
.small {
font-size: 92%;
}
mark,
.mark {
background-color: #fcf8e3;
padding: .2em;
}
.text-left {
text-align: left;
}
.text-right {
text-align: right;
}
.text-center {
text-align: center;
}
.text-justify {
text-align: justify;
}
.text-nowrap {
white-space: nowrap;
}
.text-lowercase {
text-transform: lowercase;
}
.text-uppercase {
text-transform: uppercase;
}
.text-capitalize {
text-transform: capitalize;
}
.text-muted {
color: #777777;
}
.text-primary {
color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
color: #286090;
}
.text-success {
color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
color: #2b542c;
}
.text-info {
color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
color: #245269;
}
.text-warning {
color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
color: #66512c;
}
.text-danger {
color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
color: #843534;
}
.bg-primary {
color: #fff;
background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
background-color: #286090;
}
.bg-success {
background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
background-color: #c1e2b3;
}
.bg-info {
background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
background-color: #afd9ee;
}
.bg-warning {
background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
background-color: #f7ecb5;
}
.bg-danger {
background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
background-color: #e4b9b9;
}
.page-header {
padding-bottom: 8px;
margin: 36px 0 18px;
border-bottom: 1px solid #eeeeee;
}
ul,
ol {
margin-top: 0;
margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
margin-bottom: 0;
}
.list-unstyled {
padding-left: 0;
list-style: none;
}
.list-inline {
padding-left: 0;
list-style: none;
margin-left: -5px;
}
.list-inline > li {
display: inline-block;
padding-left: 5px;
padding-right: 5px;
}
dl {
margin-top: 0;
margin-bottom: 18px;
}
dt,
dd {
line-height: 1.42857143;
}
dt {
font-weight: bold;
}
dd {
margin-left: 0;
}
@media (min-width: 541px) {
.dl-horizontal dt {
float: left;
width: 160px;
clear: left;
text-align: right;
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
.dl-horizontal dd {
margin-left: 180px;
}
}
abbr[title],
abbr[data-original-title] {
cursor: help;
border-bottom: 1px dotted #777777;
}
.initialism {
font-size: 90%;
text-transform: uppercase;
}
blockquote {
padding: 9px 18px;
margin: 0 0 18px;
font-size: inherit;
border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
display: block;
font-size: 80%;
line-height: 1.42857143;
color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
padding-right: 15px;
padding-left: 0;
border-right: 5px solid #eeeeee;
border-left: 0;
text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
content: '\00A0 \2014';
}
address {
margin-bottom: 18px;
font-style: normal;
line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
font-family: monospace;
}
code {
padding: 2px 4px;
font-size: 90%;
color: #c7254e;
background-color: #f9f2f4;
border-radius: 2px;
}
kbd {
padding: 2px 4px;
font-size: 90%;
color: #888;
background-color: transparent;
border-radius: 1px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
padding: 0;
font-size: 100%;
font-weight: bold;
box-shadow: none;
}
pre {
display: block;
padding: 8.5px;
margin: 0 0 9px;
font-size: 12px;
line-height: 1.42857143;
word-break: break-all;
word-wrap: break-word;
color: #333333;
background-color: #f5f5f5;
border: 1px solid #ccc;
border-radius: 2px;
}
pre code {
padding: 0;
font-size: inherit;
color: inherit;
white-space: pre-wrap;
background-color: transparent;
border-radius: 0;
}
.pre-scrollable {
max-height: 340px;
overflow-y: scroll;
}
.container {
margin-right: auto;
margin-left: auto;
padding-left: 0px;
padding-right: 0px;
}
@media (min-width: 768px) {
.container {
width: 768px;
}
}
@media (min-width: 992px) {
.container {
width: 940px;
}
}
@media (min-width: 1200px) {
.container {
width: 1140px;
}
}
.container-fluid {
margin-right: auto;
margin-left: auto;
padding-left: 0px;
padding-right: 0px;
}
.row {
margin-left: 0px;
margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
position: relative;
min-height: 1px;
padding-left: 0px;
padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
float: left;
}
.col-xs-12 {
width: 100%;
}
.col-xs-11 {
width: 91.66666667%;
}
.col-xs-10 {
width: 83.33333333%;
}
.col-xs-9 {
width: 75%;
}
.col-xs-8 {
width: 66.66666667%;
}
.col-xs-7 {
width: 58.33333333%;
}
.col-xs-6 {
width: 50%;
}
.col-xs-5 {
width: 41.66666667%;
}
.col-xs-4 {
width: 33.33333333%;
}
.col-xs-3 {
width: 25%;
}
.col-xs-2 {
width: 16.66666667%;
}
.col-xs-1 {
width: 8.33333333%;
}
.col-xs-pull-12 {
right: 100%;
}
.col-xs-pull-11 {
right: 91.66666667%;
}
.col-xs-pull-10 {
right: 83.33333333%;
}
.col-xs-pull-9 {
right: 75%;
}
.col-xs-pull-8 {
right: 66.66666667%;
}
.col-xs-pull-7 {
right: 58.33333333%;
}
.col-xs-pull-6 {
right: 50%;
}
.col-xs-pull-5 {
right: 41.66666667%;
}
.col-xs-pull-4 {
right: 33.33333333%;
}
.col-xs-pull-3 {
right: 25%;
}
.col-xs-pull-2 {
right: 16.66666667%;
}
.col-xs-pull-1 {
right: 8.33333333%;
}
.col-xs-pull-0 {
right: auto;
}
.col-xs-push-12 {
left: 100%;
}
.col-xs-push-11 {
left: 91.66666667%;
}
.col-xs-push-10 {
left: 83.33333333%;
}
.col-xs-push-9 {
left: 75%;
}
.col-xs-push-8 {
left: 66.66666667%;
}
.col-xs-push-7 {
left: 58.33333333%;
}
.col-xs-push-6 {
left: 50%;
}
.col-xs-push-5 {
left: 41.66666667%;
}
.col-xs-push-4 {
left: 33.33333333%;
}
.col-xs-push-3 {
left: 25%;
}
.col-xs-push-2 {
left: 16.66666667%;
}
.col-xs-push-1 {
left: 8.33333333%;
}
.col-xs-push-0 {
left: auto;
}
.col-xs-offset-12 {
margin-left: 100%;
}
.col-xs-offset-11 {
margin-left: 91.66666667%;
}
.col-xs-offset-10 {
margin-left: 83.33333333%;
}
.col-xs-offset-9 {
margin-left: 75%;
}
.col-xs-offset-8 {
margin-left: 66.66666667%;
}
.col-xs-offset-7 {
margin-left: 58.33333333%;
}
.col-xs-offset-6 {
margin-left: 50%;
}
.col-xs-offset-5 {
margin-left: 41.66666667%;
}
.col-xs-offset-4 {
margin-left: 33.33333333%;
}
.col-xs-offset-3 {
margin-left: 25%;
}
.col-xs-offset-2 {
margin-left: 16.66666667%;
}
.col-xs-offset-1 {
margin-left: 8.33333333%;
}
.col-xs-offset-0 {
margin-left: 0%;
}
@media (min-width: 768px) {
.col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
float: left;
}
.col-sm-12 {
width: 100%;
}
.col-sm-11 {
width: 91.66666667%;
}
.col-sm-10 {
width: 83.33333333%;
}
.col-sm-9 {
width: 75%;
}
.col-sm-8 {
width: 66.66666667%;
}
.col-sm-7 {
width: 58.33333333%;
}
.col-sm-6 {
width: 50%;
}
.col-sm-5 {
width: 41.66666667%;
}
.col-sm-4 {
width: 33.33333333%;
}
.col-sm-3 {
width: 25%;
}
.col-sm-2 {
width: 16.66666667%;
}
.col-sm-1 {
width: 8.33333333%;
}
.col-sm-pull-12 {
right: 100%;
}
.col-sm-pull-11 {
right: 91.66666667%;
}
.col-sm-pull-10 {
right: 83.33333333%;
}
.col-sm-pull-9 {
right: 75%;
}
.col-sm-pull-8 {
right: 66.66666667%;
}
.col-sm-pull-7 {
right: 58.33333333%;
}
.col-sm-pull-6 {
right: 50%;
}
.col-sm-pull-5 {
right: 41.66666667%;
}
.col-sm-pull-4 {
right: 33.33333333%;
}
.col-sm-pull-3 {
right: 25%;
}
.col-sm-pull-2 {
right: 16.66666667%;
}
.col-sm-pull-1 {
right: 8.33333333%;
}
.col-sm-pull-0 {
right: auto;
}
.col-sm-push-12 {
left: 100%;
}
.col-sm-push-11 {
left: 91.66666667%;
}
.col-sm-push-10 {
left: 83.33333333%;
}
.col-sm-push-9 {
left: 75%;
}
.col-sm-push-8 {
left: 66.66666667%;
}
.col-sm-push-7 {
left: 58.33333333%;
}
.col-sm-push-6 {
left: 50%;
}
.col-sm-push-5 {
left: 41.66666667%;
}
.col-sm-push-4 {
left: 33.33333333%;
}
.col-sm-push-3 {
left: 25%;
}
.col-sm-push-2 {
left: 16.66666667%;
}
.col-sm-push-1 {
left: 8.33333333%;
}
.col-sm-push-0 {
left: auto;
}
.col-sm-offset-12 {
margin-left: 100%;
}
.col-sm-offset-11 {
margin-left: 91.66666667%;
}
.col-sm-offset-10 {
margin-left: 83.33333333%;
}
.col-sm-offset-9 {
margin-left: 75%;
}
.col-sm-offset-8 {
margin-left: 66.66666667%;
}
.col-sm-offset-7 {
margin-left: 58.33333333%;
}
.col-sm-offset-6 {
margin-left: 50%;
}
.col-sm-offset-5 {
margin-left: 41.66666667%;
}
.col-sm-offset-4 {
margin-left: 33.33333333%;
}
.col-sm-offset-3 {
margin-left: 25%;
}
.col-sm-offset-2 {
margin-left: 16.66666667%;
}
.col-sm-offset-1 {
margin-left: 8.33333333%;
}
.col-sm-offset-0 {
margin-left: 0%;
}
}
@media (min-width: 992px) {
.col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
float: left;
}
.col-md-12 {
width: 100%;
}
.col-md-11 {
width: 91.66666667%;
}
.col-md-10 {
width: 83.33333333%;
}
.col-md-9 {
width: 75%;
}
.col-md-8 {
width: 66.66666667%;
}
.col-md-7 {
width: 58.33333333%;
}
.col-md-6 {
width: 50%;
}
.col-md-5 {
width: 41.66666667%;
}
.col-md-4 {
width: 33.33333333%;
}
.col-md-3 {
width: 25%;
}
.col-md-2 {
width: 16.66666667%;
}
.col-md-1 {
width: 8.33333333%;
}
.col-md-pull-12 {
right: 100%;
}
.col-md-pull-11 {
right: 91.66666667%;
}
.col-md-pull-10 {
right: 83.33333333%;
}
.col-md-pull-9 {
right: 75%;
}
.col-md-pull-8 {
right: 66.66666667%;
}
.col-md-pull-7 {
right: 58.33333333%;
}
.col-md-pull-6 {
right: 50%;
}
.col-md-pull-5 {
right: 41.66666667%;
}
.col-md-pull-4 {
right: 33.33333333%;
}
.col-md-pull-3 {
right: 25%;
}
.col-md-pull-2 {
right: 16.66666667%;
}
.col-md-pull-1 {
right: 8.33333333%;
}
.col-md-pull-0 {
right: auto;
}
.col-md-push-12 {
left: 100%;
}
.col-md-push-11 {
left: 91.66666667%;
}
.col-md-push-10 {
left: 83.33333333%;
}
.col-md-push-9 {
left: 75%;
}
.col-md-push-8 {
left: 66.66666667%;
}
.col-md-push-7 {
left: 58.33333333%;
}
.col-md-push-6 {
left: 50%;
}
.col-md-push-5 {
left: 41.66666667%;
}
.col-md-push-4 {
left: 33.33333333%;
}
.col-md-push-3 {
left: 25%;
}
.col-md-push-2 {
left: 16.66666667%;
}
.col-md-push-1 {
left: 8.33333333%;
}
.col-md-push-0 {
left: auto;
}
.col-md-offset-12 {
margin-left: 100%;
}
.col-md-offset-11 {
margin-left: 91.66666667%;
}
.col-md-offset-10 {
margin-left: 83.33333333%;
}
.col-md-offset-9 {
margin-left: 75%;
}
.col-md-offset-8 {
margin-left: 66.66666667%;
}
.col-md-offset-7 {
margin-left: 58.33333333%;
}
.col-md-offset-6 {
margin-left: 50%;
}
.col-md-offset-5 {
margin-left: 41.66666667%;
}
.col-md-offset-4 {
margin-left: 33.33333333%;
}
.col-md-offset-3 {
margin-left: 25%;
}
.col-md-offset-2 {
margin-left: 16.66666667%;
}
.col-md-offset-1 {
margin-left: 8.33333333%;
}
.col-md-offset-0 {
margin-left: 0%;
}
}
@media (min-width: 1200px) {
.col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
float: left;
}
.col-lg-12 {
width: 100%;
}
.col-lg-11 {
width: 91.66666667%;
}
.col-lg-10 {
width: 83.33333333%;
}
.col-lg-9 {
width: 75%;
}
.col-lg-8 {
width: 66.66666667%;
}
.col-lg-7 {
width: 58.33333333%;
}
.col-lg-6 {
width: 50%;
}
.col-lg-5 {
width: 41.66666667%;
}
.col-lg-4 {
width: 33.33333333%;
}
.col-lg-3 {
width: 25%;
}
.col-lg-2 {
width: 16.66666667%;
}
.col-lg-1 {
width: 8.33333333%;
}
.col-lg-pull-12 {
right: 100%;
}
.col-lg-pull-11 {
right: 91.66666667%;
}
.col-lg-pull-10 {
right: 83.33333333%;
}
.col-lg-pull-9 {
right: 75%;
}
.col-lg-pull-8 {
right: 66.66666667%;
}
.col-lg-pull-7 {
right: 58.33333333%;
}
.col-lg-pull-6 {
right: 50%;
}
.col-lg-pull-5 {
right: 41.66666667%;
}
.col-lg-pull-4 {
right: 33.33333333%;
}
.col-lg-pull-3 {
right: 25%;
}
.col-lg-pull-2 {
right: 16.66666667%;
}
.col-lg-pull-1 {
right: 8.33333333%;
}
.col-lg-pull-0 {
right: auto;
}
.col-lg-push-12 {
left: 100%;
}
.col-lg-push-11 {
left: 91.66666667%;
}
.col-lg-push-10 {
left: 83.33333333%;
}
.col-lg-push-9 {
left: 75%;
}
.col-lg-push-8 {
left: 66.66666667%;
}
.col-lg-push-7 {
left: 58.33333333%;
}
.col-lg-push-6 {
left: 50%;
}
.col-lg-push-5 {
left: 41.66666667%;
}
.col-lg-push-4 {
left: 33.33333333%;
}
.col-lg-push-3 {
left: 25%;
}
.col-lg-push-2 {
left: 16.66666667%;
}
.col-lg-push-1 {
left: 8.33333333%;
}
.col-lg-push-0 {
left: auto;
}
.col-lg-offset-12 {
margin-left: 100%;
}
.col-lg-offset-11 {
margin-left: 91.66666667%;
}
.col-lg-offset-10 {
margin-left: 83.33333333%;
}
.col-lg-offset-9 {
margin-left: 75%;
}
.col-lg-offset-8 {
margin-left: 66.66666667%;
}
.col-lg-offset-7 {
margin-left: 58.33333333%;
}
.col-lg-offset-6 {
margin-left: 50%;
}
.col-lg-offset-5 {
margin-left: 41.66666667%;
}
.col-lg-offset-4 {
margin-left: 33.33333333%;
}
.col-lg-offset-3 {
margin-left: 25%;
}
.col-lg-offset-2 {
margin-left: 16.66666667%;
}
.col-lg-offset-1 {
margin-left: 8.33333333%;
}
.col-lg-offset-0 {
margin-left: 0%;
}
}
table {
background-color: transparent;
}
caption {
padding-top: 8px;
padding-bottom: 8px;
color: #777777;
text-align: left;
}
th {
text-align: left;
}
.table {
width: 100%;
max-width: 100%;
margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
padding: 8px;
line-height: 1.42857143;
vertical-align: top;
border-top: 1px solid #ddd;
}
.table > thead > tr > th {
vertical-align: bottom;
border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
border-top: 0;
}
.table > tbody + tbody {
border-top: 2px solid #ddd;
}
.table .table {
background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
padding: 5px;
}
.table-bordered {
border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
background-color: #f5f5f5;
}
table col[class*="col-"] {
position: static;
float: none;
display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
position: static;
float: none;
display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
background-color: #ebcccc;
}
.table-responsive {
overflow-x: auto;
min-height: 0.01%;
}
@media screen and (max-width: 767px) {
.table-responsive {
width: 100%;
margin-bottom: 13.5px;
overflow-y: hidden;
-ms-overflow-style: -ms-autohiding-scrollbar;
border: 1px solid #ddd;
}
.table-responsive > .table {
margin-bottom: 0;
}
.table-responsive > .table > thead > tr > th,
.table-responsive > .table > tbody > tr > th,
.table-responsive > .table > tfoot > tr > th,
.table-responsive > .table > thead > tr > td,
.table-responsive > .table > tbody > tr > td,
.table-responsive > .table > tfoot > tr > td {
white-space: nowrap;
}
.table-responsive > .table-bordered {
border: 0;
}
.table-responsive > .table-bordered > thead > tr > th:first-child,
.table-responsive > .table-bordered > tbody > tr > th:first-child,
.table-responsive > .table-bordered > tfoot > tr > th:first-child,
.table-responsive > .table-bordered > thead > tr > td:first-child,
.table-responsive > .table-bordered > tbody > tr > td:first-child,
.table-responsive > .table-bordered > tfoot > tr > td:first-child {
border-left: 0;
}
.table-responsive > .table-bordered > thead > tr > th:last-child,
.table-responsive > .table-bordered > tbody > tr > th:last-child,
.table-responsive > .table-bordered > tfoot > tr > th:last-child,
.table-responsive > .table-bordered > thead > tr > td:last-child,
.table-responsive > .table-bordered > tbody > tr > td:last-child,
.table-responsive > .table-bordered > tfoot > tr > td:last-child {
border-right: 0;
}
.table-responsive > .table-bordered > tbody > tr:last-child > th,
.table-responsive > .table-bordered > tfoot > tr:last-child > th,
.table-responsive > .table-bordered > tbody > tr:last-child > td,
.table-responsive > .table-bordered > tfoot > tr:last-child > td {
border-bottom: 0;
}
}
fieldset {
padding: 0;
margin: 0;
border: 0;
min-width: 0;
}
legend {
display: block;
width: 100%;
padding: 0;
margin-bottom: 18px;
font-size: 19.5px;
line-height: inherit;
color: #333333;
border: 0;
border-bottom: 1px solid #e5e5e5;
}
label {
display: inline-block;
max-width: 100%;
margin-bottom: 5px;
font-weight: bold;
}
input[type="search"] {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
margin: 4px 0 0;
margin-top: 1px \9;
line-height: normal;
}
input[type="file"] {
display: block;
}
input[type="range"] {
display: block;
width: 100%;
}
select[multiple],
select[size] {
height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
output {
display: block;
padding-top: 7px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
}
.form-control {
display: block;
width: 100%;
height: 32px;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
background-color: #fff;
background-image: none;
border: 1px solid #ccc;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
border-color: #66afe9;
outline: 0;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
color: #999;
opacity: 1;
}
.form-control:-ms-input-placeholder {
color: #999;
}
.form-control::-webkit-input-placeholder {
color: #999;
}
.form-control::-ms-expand {
border: 0;
background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
background-color: #eeeeee;
opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
cursor: not-allowed;
}
textarea.form-control {
height: auto;
}
input[type="search"] {
-webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
input[type="date"].form-control,
input[type="time"].form-control,
input[type="datetime-local"].form-control,
input[type="month"].form-control {
line-height: 32px;
}
input[type="date"].input-sm,
input[type="time"].input-sm,
input[type="datetime-local"].input-sm,
input[type="month"].input-sm,
.input-group-sm input[type="date"],
.input-group-sm input[type="time"],
.input-group-sm input[type="datetime-local"],
.input-group-sm input[type="month"] {
line-height: 30px;
}
input[type="date"].input-lg,
input[type="time"].input-lg,
input[type="datetime-local"].input-lg,
input[type="month"].input-lg,
.input-group-lg input[type="date"],
.input-group-lg input[type="time"],
.input-group-lg input[type="datetime-local"],
.input-group-lg input[type="month"] {
line-height: 45px;
}
}
.form-group {
margin-bottom: 15px;
}
.radio,
.checkbox {
position: relative;
display: block;
margin-top: 10px;
margin-bottom: 10px;
}
.radio label,
.checkbox label {
min-height: 18px;
padding-left: 20px;
margin-bottom: 0;
font-weight: normal;
cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
position: absolute;
margin-left: -20px;
margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
position: relative;
display: inline-block;
padding-left: 20px;
margin-bottom: 0;
vertical-align: middle;
font-weight: normal;
cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
margin-top: 0;
margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
cursor: not-allowed;
}
.form-control-static {
padding-top: 7px;
padding-bottom: 7px;
margin-bottom: 0;
min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
padding-left: 0;
padding-right: 0;
}
.input-sm {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
select.input-sm {
height: 30px;
line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
height: auto;
}
.form-group-sm .form-control {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.form-group-sm select.form-control {
height: 30px;
line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
height: auto;
}
.form-group-sm .form-control-static {
height: 30px;
min-height: 30px;
padding: 6px 10px;
font-size: 12px;
line-height: 1.5;
}
.input-lg {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
select.input-lg {
height: 45px;
line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
height: auto;
}
.form-group-lg .form-control {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
.form-group-lg select.form-control {
height: 45px;
line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
height: auto;
}
.form-group-lg .form-control-static {
height: 45px;
min-height: 35px;
padding: 11px 16px;
font-size: 17px;
line-height: 1.3333333;
}
.has-feedback {
position: relative;
}
.has-feedback .form-control {
padding-right: 40px;
}
.form-control-feedback {
position: absolute;
top: 0;
right: 0;
z-index: 2;
display: block;
width: 32px;
height: 32px;
line-height: 32px;
text-align: center;
pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
width: 45px;
height: 45px;
line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
width: 30px;
height: 30px;
line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
color: #3c763d;
}
.has-success .form-control {
border-color: #3c763d;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
border-color: #2b542c;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
color: #3c763d;
border-color: #3c763d;
background-color: #dff0d8;
}
.has-success .form-control-feedback {
color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
color: #8a6d3b;
}
.has-warning .form-control {
border-color: #8a6d3b;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
border-color: #66512c;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
color: #8a6d3b;
border-color: #8a6d3b;
background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
color: #a94442;
}
.has-error .form-control {
border-color: #a94442;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
border-color: #843534;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
color: #a94442;
border-color: #a94442;
background-color: #f2dede;
}
.has-error .form-control-feedback {
color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
top: 0;
}
.help-block {
display: block;
margin-top: 5px;
margin-bottom: 10px;
color: #404040;
}
@media (min-width: 768px) {
.form-inline .form-group {
display: inline-block;
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .form-control {
display: inline-block;
width: auto;
vertical-align: middle;
}
.form-inline .form-control-static {
display: inline-block;
}
.form-inline .input-group {
display: inline-table;
vertical-align: middle;
}
.form-inline .input-group .input-group-addon,
.form-inline .input-group .input-group-btn,
.form-inline .input-group .form-control {
width: auto;
}
.form-inline .input-group > .form-control {
width: 100%;
}
.form-inline .control-label {
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .radio,
.form-inline .checkbox {
display: inline-block;
margin-top: 0;
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .radio label,
.form-inline .checkbox label {
padding-left: 0;
}
.form-inline .radio input[type="radio"],
.form-inline .checkbox input[type="checkbox"] {
position: relative;
margin-left: 0;
}
.form-inline .has-feedback .form-control-feedback {
top: 0;
}
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
margin-top: 0;
margin-bottom: 0;
padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
min-height: 25px;
}
.form-horizontal .form-group {
margin-left: 0px;
margin-right: 0px;
}
@media (min-width: 768px) {
.form-horizontal .control-label {
text-align: right;
margin-bottom: 0;
padding-top: 7px;
}
}
.form-horizontal .has-feedback .form-control-feedback {
right: 0px;
}
@media (min-width: 768px) {
.form-horizontal .form-group-lg .control-label {
padding-top: 11px;
font-size: 17px;
}
}
@media (min-width: 768px) {
.form-horizontal .form-group-sm .control-label {
padding-top: 6px;
font-size: 12px;
}
}
.btn {
display: inline-block;
margin-bottom: 0;
font-weight: normal;
text-align: center;
vertical-align: middle;
touch-action: manipulation;
cursor: pointer;
background-image: none;
border: 1px solid transparent;
white-space: nowrap;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
border-radius: 2px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
color: #333;
text-decoration: none;
}
.btn:active,
.btn.active {
outline: 0;
background-image: none;
-webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
cursor: not-allowed;
opacity: 0.65;
filter: alpha(opacity=65);
-webkit-box-shadow: none;
box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
pointer-events: none;
}
.btn-default {
color: #333;
background-color: #fff;
border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
.btn-default:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
background-color: #fff;
border-color: #ccc;
}
.btn-default .badge {
color: #fff;
background-color: #333;
}
.btn-primary {
color: #fff;
background-color: #337ab7;
border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
color: #fff;
background-color: #286090;
border-color: #122b40;
}
.btn-primary:hover {
color: #fff;
background-color: #286090;
border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
color: #fff;
background-color: #286090;
border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
color: #fff;
background-color: #204d74;
border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
background-color: #337ab7;
border-color: #2e6da4;
}
.btn-primary .badge {
color: #337ab7;
background-color: #fff;
}
.btn-success {
color: #fff;
background-color: #5cb85c;
border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
color: #fff;
background-color: #449d44;
border-color: #255625;
}
.btn-success:hover {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
color: #fff;
background-color: #398439;
border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
background-color: #5cb85c;
border-color: #4cae4c;
}
.btn-success .badge {
color: #5cb85c;
background-color: #fff;
}
.btn-info {
color: #fff;
background-color: #5bc0de;
border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
color: #fff;
background-color: #31b0d5;
border-color: #1b6d85;
}
.btn-info:hover {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
color: #fff;
background-color: #269abc;
border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
background-color: #5bc0de;
border-color: #46b8da;
}
.btn-info .badge {
color: #5bc0de;
background-color: #fff;
}
.btn-warning {
color: #fff;
background-color: #f0ad4e;
border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
color: #fff;
background-color: #ec971f;
border-color: #985f0d;
}
.btn-warning:hover {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
color: #fff;
background-color: #d58512;
border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
background-color: #f0ad4e;
border-color: #eea236;
}
.btn-warning .badge {
color: #f0ad4e;
background-color: #fff;
}
.btn-danger {
color: #fff;
background-color: #d9534f;
border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
color: #fff;
background-color: #c9302c;
border-color: #761c19;
}
.btn-danger:hover {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
color: #fff;
background-color: #ac2925;
border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
background-color: #d9534f;
border-color: #d43f3a;
}
.btn-danger .badge {
color: #d9534f;
background-color: #fff;
}
.btn-link {
color: #337ab7;
font-weight: normal;
border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
background-color: transparent;
-webkit-box-shadow: none;
box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
color: #23527c;
text-decoration: underline;
background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
color: #777777;
text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
padding: 1px 5px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.btn-block {
display: block;
width: 100%;
}
.btn-block + .btn-block {
margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
width: 100%;
}
.fade {
opacity: 0;
-webkit-transition: opacity 0.15s linear;
-o-transition: opacity 0.15s linear;
transition: opacity 0.15s linear;
}
.fade.in {
opacity: 1;
}
.collapse {
display: none;
}
.collapse.in {
display: block;
}
tr.collapse.in {
display: table-row;
}
tbody.collapse.in {
display: table-row-group;
}
.collapsing {
position: relative;
height: 0;
overflow: hidden;
-webkit-transition-property: height, visibility;
transition-property: height, visibility;
-webkit-transition-duration: 0.35s;
transition-duration: 0.35s;
-webkit-transition-timing-function: ease;
transition-timing-function: ease;
}
.caret {
display: inline-block;
width: 0;
height: 0;
margin-left: 2px;
vertical-align: middle;
border-top: 4px dashed;
border-top: 4px solid \9;
border-right: 4px solid transparent;
border-left: 4px solid transparent;
}
.dropup,
.dropdown {
position: relative;
}
.dropdown-toggle:focus {
outline: 0;
}
.dropdown-menu {
position: absolute;
top: 100%;
left: 0;
z-index: 1000;
display: none;
float: left;
min-width: 160px;
padding: 5px 0;
margin: 2px 0 0;
list-style: none;
font-size: 13px;
text-align: left;
background-color: #fff;
border: 1px solid #ccc;
border: 1px solid rgba(0, 0, 0, 0.15);
border-radius: 2px;
-webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
background-clip: padding-box;
}
.dropdown-menu.pull-right {
right: 0;
left: auto;
}
.dropdown-menu .divider {
height: 1px;
margin: 8px 0;
overflow: hidden;
background-color: #e5e5e5;
}
.dropdown-menu > li > a {
display: block;
padding: 3px 20px;
clear: both;
font-weight: normal;
line-height: 1.42857143;
color: #333333;
white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
text-decoration: none;
color: #262626;
background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
color: #fff;
text-decoration: none;
outline: 0;
background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
text-decoration: none;
background-color: transparent;
background-image: none;
filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
cursor: not-allowed;
}
.open > .dropdown-menu {
display: block;
}
.open > a {
outline: 0;
}
.dropdown-menu-right {
left: auto;
right: 0;
}
.dropdown-menu-left {
left: 0;
right: auto;
}
.dropdown-header {
display: block;
padding: 3px 20px;
font-size: 12px;
line-height: 1.42857143;
color: #777777;
white-space: nowrap;
}
.dropdown-backdrop {
position: fixed;
left: 0;
right: 0;
bottom: 0;
top: 0;
z-index: 990;
}
.pull-right > .dropdown-menu {
right: 0;
left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
border-top: 0;
border-bottom: 4px dashed;
border-bottom: 4px solid \9;
content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
top: auto;
bottom: 100%;
margin-bottom: 2px;
}
@media (min-width: 541px) {
.navbar-right .dropdown-menu {
left: auto;
right: 0;
}
.navbar-right .dropdown-menu-left {
left: 0;
right: auto;
}
}
.btn-group,
.btn-group-vertical {
position: relative;
display: inline-block;
vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
position: relative;
float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
margin-left: -1px;
}
.btn-toolbar {
margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
border-radius: 0;
}
.btn-group > .btn:first-child {
margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.btn-group > .btn-group {
float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
padding-left: 8px;
padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
padding-left: 12px;
padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
-webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
-webkit-box-shadow: none;
box-shadow: none;
}
.btn .caret {
margin-left: 0;
}
.btn-lg .caret {
border-width: 5px 5px 0;
border-bottom-width: 0;
}
.dropup .btn-lg .caret {
border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
display: block;
float: none;
width: 100%;
max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
margin-top: -1px;
margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
border-top-right-radius: 2px;
border-top-left-radius: 2px;
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
border-top-right-radius: 0;
border-top-left-radius: 0;
border-bottom-right-radius: 2px;
border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.btn-group-justified {
display: table;
width: 100%;
table-layout: fixed;
border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
float: none;
display: table-cell;
width: 1%;
}
.btn-group-justified > .btn-group .btn {
width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
position: absolute;
clip: rect(0, 0, 0, 0);
pointer-events: none;
}
.input-group {
position: relative;
display: table;
border-collapse: separate;
}
.input-group[class*="col-"] {
float: none;
padding-left: 0;
padding-right: 0;
}
.input-group .form-control {
position: relative;
z-index: 2;
float: left;
width: 100%;
margin-bottom: 0;
}
.input-group .form-control:focus {
z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
height: 45px;
line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
height: 30px;
line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
border-radius: 0;
}
.input-group-addon,
.input-group-btn {
width: 1%;
white-space: nowrap;
vertical-align: middle;
}
.input-group-addon {
padding: 6px 12px;
font-size: 13px;
font-weight: normal;
line-height: 1;
color: #555555;
text-align: center;
background-color: #eeeeee;
border: 1px solid #ccc;
border-radius: 2px;
}
.input-group-addon.input-sm {
padding: 5px 10px;
font-size: 12px;
border-radius: 1px;
}
.input-group-addon.input-lg {
padding: 10px 16px;
font-size: 17px;
border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.input-group-addon:first-child {
border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.input-group-addon:last-child {
border-left: 0;
}
.input-group-btn {
position: relative;
font-size: 0;
white-space: nowrap;
}
.input-group-btn > .btn {
position: relative;
}
.input-group-btn > .btn + .btn {
margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
z-index: 2;
margin-left: -1px;
}
.nav {
margin-bottom: 0;
padding-left: 0;
list-style: none;
}
.nav > li {
position: relative;
display: block;
}
.nav > li > a {
position: relative;
display: block;
padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
text-decoration: none;
background-color: #eeeeee;
}
.nav > li.disabled > a {
color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
color: #777777;
text-decoration: none;
background-color: transparent;
cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
background-color: #eeeeee;
border-color: #337ab7;
}
.nav .nav-divider {
height: 1px;
margin: 8px 0;
overflow: hidden;
background-color: #e5e5e5;
}
.nav > li > a > img {
max-width: none;
}
.nav-tabs {
border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
float: left;
margin-bottom: -1px;
}
.nav-tabs > li > a {
margin-right: 2px;
line-height: 1.42857143;
border: 1px solid transparent;
border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
color: #555555;
background-color: #fff;
border: 1px solid #ddd;
border-bottom-color: transparent;
cursor: default;
}
.nav-tabs.nav-justified {
width: 100%;
border-bottom: 0;
}
.nav-tabs.nav-justified > li {
float: none;
}
.nav-tabs.nav-justified > li > a {
text-align: center;
margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
top: auto;
left: auto;
}
@media (min-width: 768px) {
.nav-tabs.nav-justified > li {
display: table-cell;
width: 1%;
}
.nav-tabs.nav-justified > li > a {
margin-bottom: 0;
}
}
.nav-tabs.nav-justified > li > a {
margin-right: 0;
border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
border: 1px solid #ddd;
}
@media (min-width: 768px) {
.nav-tabs.nav-justified > li > a {
border-bottom: 1px solid #ddd;
border-radius: 2px 2px 0 0;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
border-bottom-color: #fff;
}
}
.nav-pills > li {
float: left;
}
.nav-pills > li > a {
border-radius: 2px;
}
.nav-pills > li + li {
margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
color: #fff;
background-color: #337ab7;
}
.nav-stacked > li {
float: none;
}
.nav-stacked > li + li {
margin-top: 2px;
margin-left: 0;
}
.nav-justified {
width: 100%;
}
.nav-justified > li {
float: none;
}
.nav-justified > li > a {
text-align: center;
margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
top: auto;
left: auto;
}
@media (min-width: 768px) {
.nav-justified > li {
display: table-cell;
width: 1%;
}
.nav-justified > li > a {
margin-bottom: 0;
}
}
.nav-tabs-justified {
border-bottom: 0;
}
.nav-tabs-justified > li > a {
margin-right: 0;
border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
border: 1px solid #ddd;
}
@media (min-width: 768px) {
.nav-tabs-justified > li > a {
border-bottom: 1px solid #ddd;
border-radius: 2px 2px 0 0;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
border-bottom-color: #fff;
}
}
.tab-content > .tab-pane {
display: none;
}
.tab-content > .active {
display: block;
}
.nav-tabs .dropdown-menu {
margin-top: -1px;
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.navbar {
position: relative;
min-height: 30px;
margin-bottom: 18px;
border: 1px solid transparent;
}
@media (min-width: 541px) {
.navbar {
border-radius: 2px;
}
}
@media (min-width: 541px) {
.navbar-header {
float: left;
}
}
.navbar-collapse {
overflow-x: visible;
padding-right: 0px;
padding-left: 0px;
border-top: 1px solid transparent;
box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
-webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
overflow-y: auto;
}
@media (min-width: 541px) {
.navbar-collapse {
width: auto;
border-top: 0;
box-shadow: none;
}
.navbar-collapse.collapse {
display: block !important;
height: auto !important;
padding-bottom: 0;
overflow: visible !important;
}
.navbar-collapse.in {
overflow-y: visible;
}
.navbar-fixed-top .navbar-collapse,
.navbar-static-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
padding-left: 0;
padding-right: 0;
}
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
max-height: 200px;
}
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
margin-right: 0px;
margin-left: 0px;
}
@media (min-width: 541px) {
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
margin-right: 0;
margin-left: 0;
}
}
.navbar-static-top {
z-index: 1000;
border-width: 0 0 1px;
}
@media (min-width: 541px) {
.navbar-static-top {
border-radius: 0;
}
}
.navbar-fixed-top,
.navbar-fixed-bottom {
position: fixed;
right: 0;
left: 0;
z-index: 1030;
}
@media (min-width: 541px) {
.navbar-fixed-top,
.navbar-fixed-bottom {
border-radius: 0;
}
}
.navbar-fixed-top {
top: 0;
border-width: 0 0 1px;
}
.navbar-fixed-bottom {
bottom: 0;
margin-bottom: 0;
border-width: 1px 0 0;
}
.navbar-brand {
float: left;
padding: 6px 0px;
font-size: 17px;
line-height: 18px;
height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
text-decoration: none;
}
.navbar-brand > img {
display: block;
}
@media (min-width: 541px) {
.navbar > .container .navbar-brand,
.navbar > .container-fluid .navbar-brand {
margin-left: 0px;
}
}
.navbar-toggle {
position: relative;
float: right;
margin-right: 0px;
padding: 9px 10px;
margin-top: -2px;
margin-bottom: -2px;
background-color: transparent;
background-image: none;
border: 1px solid transparent;
border-radius: 2px;
}
.navbar-toggle:focus {
outline: 0;
}
.navbar-toggle .icon-bar {
display: block;
width: 22px;
height: 2px;
border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
margin-top: 4px;
}
@media (min-width: 541px) {
.navbar-toggle {
display: none;
}
}
.navbar-nav {
margin: 3px 0px;
}
.navbar-nav > li > a {
padding-top: 10px;
padding-bottom: 10px;
line-height: 18px;
}
@media (max-width: 540px) {
.navbar-nav .open .dropdown-menu {
position: static;
float: none;
width: auto;
margin-top: 0;
background-color: transparent;
border: 0;
box-shadow: none;
}
.navbar-nav .open .dropdown-menu > li > a,
.navbar-nav .open .dropdown-menu .dropdown-header {
padding: 5px 15px 5px 25px;
}
.navbar-nav .open .dropdown-menu > li > a {
line-height: 18px;
}
.navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-nav .open .dropdown-menu > li > a:focus {
background-image: none;
}
}
@media (min-width: 541px) {
.navbar-nav {
float: left;
margin: 0;
}
.navbar-nav > li {
float: left;
}
.navbar-nav > li > a {
padding-top: 6px;
padding-bottom: 6px;
}
}
.navbar-form {
margin-left: 0px;
margin-right: 0px;
padding: 10px 0px;
border-top: 1px solid transparent;
border-bottom: 1px solid transparent;
-webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
margin-top: -1px;
margin-bottom: -1px;
}
@media (min-width: 768px) {
.navbar-form .form-group {
display: inline-block;
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .form-control {
display: inline-block;
width: auto;
vertical-align: middle;
}
.navbar-form .form-control-static {
display: inline-block;
}
.navbar-form .input-group {
display: inline-table;
vertical-align: middle;
}
.navbar-form .input-group .input-group-addon,
.navbar-form .input-group .input-group-btn,
.navbar-form .input-group .form-control {
width: auto;
}
.navbar-form .input-group > .form-control {
width: 100%;
}
.navbar-form .control-label {
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .radio,
.navbar-form .checkbox {
display: inline-block;
margin-top: 0;
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .radio label,
.navbar-form .checkbox label {
padding-left: 0;
}
.navbar-form .radio input[type="radio"],
.navbar-form .checkbox input[type="checkbox"] {
position: relative;
margin-left: 0;
}
.navbar-form .has-feedback .form-control-feedback {
top: 0;
}
}
@media (max-width: 540px) {
.navbar-form .form-group {
margin-bottom: 5px;
}
.navbar-form .form-group:last-child {
margin-bottom: 0;
}
}
@media (min-width: 541px) {
.navbar-form {
width: auto;
border: 0;
margin-left: 0;
margin-right: 0;
padding-top: 0;
padding-bottom: 0;
-webkit-box-shadow: none;
box-shadow: none;
}
}
.navbar-nav > li > .dropdown-menu {
margin-top: 0;
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
margin-bottom: 0;
border-top-right-radius: 2px;
border-top-left-radius: 2px;
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.navbar-btn {
margin-top: -1px;
margin-bottom: -1px;
}
.navbar-btn.btn-sm {
margin-top: 0px;
margin-bottom: 0px;
}
.navbar-btn.btn-xs {
margin-top: 4px;
margin-bottom: 4px;
}
.navbar-text {
margin-top: 6px;
margin-bottom: 6px;
}
@media (min-width: 541px) {
.navbar-text {
float: left;
margin-left: 0px;
margin-right: 0px;
}
}
@media (min-width: 541px) {
.navbar-left {
float: left !important;
float: left;
}
.navbar-right {
float: right !important;
float: right;
margin-right: 0px;
}
.navbar-right ~ .navbar-right {
margin-right: 0;
}
}
.navbar-default {
background-color: #f8f8f8;
border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
color: #5e5e5e;
background-color: transparent;
}
.navbar-default .navbar-text {
color: #777;
}
.navbar-default .navbar-nav > li > a {
color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
color: #333;
background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
color: #555;
background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
color: #ccc;
background-color: transparent;
}
.navbar-default .navbar-toggle {
border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
background-color: #e7e7e7;
color: #555;
}
@media (max-width: 540px) {
.navbar-default .navbar-nav .open .dropdown-menu > li > a {
color: #777;
}
.navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
color: #333;
background-color: transparent;
}
.navbar-default .navbar-nav .open .dropdown-menu > .active > a,
.navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
color: #555;
background-color: #e7e7e7;
}
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
color: #ccc;
background-color: transparent;
}
}
.navbar-default .navbar-link {
color: #777;
}
.navbar-default .navbar-link:hover {
color: #333;
}
.navbar-default .btn-link {
color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
color: #ccc;
}
.navbar-inverse {
background-color: #222;
border-color: #080808;
}
.navbar-inverse .navbar-brand {
color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-text {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
color: #fff;
background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
color: #444;
background-color: transparent;
}
.navbar-inverse .navbar-toggle {
border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
background-color: #080808;
color: #fff;
}
@media (max-width: 540px) {
.navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
border-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu .divider {
background-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
color: #fff;
background-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
color: #444;
background-color: transparent;
}
}
.navbar-inverse .navbar-link {
color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
color: #fff;
}
.navbar-inverse .btn-link {
color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
color: #444;
}
.breadcrumb {
padding: 8px 15px;
margin-bottom: 18px;
list-style: none;
background-color: #f5f5f5;
border-radius: 2px;
}
.breadcrumb > li {
display: inline-block;
}
.breadcrumb > li + li:before {
content: "/\00a0";
padding: 0 5px;
color: #5e5e5e;
}
.breadcrumb > .active {
color: #777777;
}
.pagination {
display: inline-block;
padding-left: 0;
margin: 18px 0;
border-radius: 2px;
}
.pagination > li {
display: inline;
}
.pagination > li > a,
.pagination > li > span {
position: relative;
float: left;
padding: 6px 12px;
line-height: 1.42857143;
text-decoration: none;
color: #337ab7;
background-color: #fff;
border: 1px solid #ddd;
margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
margin-left: 0;
border-bottom-left-radius: 2px;
border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
border-bottom-right-radius: 2px;
border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
z-index: 2;
color: #23527c;
background-color: #eeeeee;
border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
z-index: 3;
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
color: #777777;
background-color: #fff;
border-color: #ddd;
cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
border-bottom-left-radius: 3px;
border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
border-bottom-right-radius: 3px;
border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
border-bottom-left-radius: 1px;
border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
border-bottom-right-radius: 1px;
border-top-right-radius: 1px;
}
.pager {
padding-left: 0;
margin: 18px 0;
list-style: none;
text-align: center;
}
.pager li {
display: inline;
}
.pager li > a,
.pager li > span {
display: inline-block;
padding: 5px 14px;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
text-decoration: none;
background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
float: right;
}
.pager .previous > a,
.pager .previous > span {
float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
color: #777777;
background-color: #fff;
cursor: not-allowed;
}
.label {
display: inline;
padding: .2em .6em .3em;
font-size: 75%;
font-weight: bold;
line-height: 1;
color: #fff;
text-align: center;
white-space: nowrap;
vertical-align: baseline;
border-radius: .25em;
}
a.label:hover,
a.label:focus {
color: #fff;
text-decoration: none;
cursor: pointer;
}
.label:empty {
display: none;
}
.btn .label {
position: relative;
top: -1px;
}
.label-default {
background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
background-color: #5e5e5e;
}
.label-primary {
background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
background-color: #286090;
}
.label-success {
background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
background-color: #449d44;
}
.label-info {
background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
background-color: #31b0d5;
}
.label-warning {
background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
background-color: #ec971f;
}
.label-danger {
background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
background-color: #c9302c;
}
.badge {
display: inline-block;
min-width: 10px;
padding: 3px 7px;
font-size: 12px;
font-weight: bold;
color: #fff;
line-height: 1;
vertical-align: middle;
white-space: nowrap;
text-align: center;
background-color: #777777;
border-radius: 10px;
}
.badge:empty {
display: none;
}
.btn .badge {
position: relative;
top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
top: 0;
padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
color: #fff;
text-decoration: none;
cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
color: #337ab7;
background-color: #fff;
}
.list-group-item > .badge {
float: right;
}
.list-group-item > .badge + .badge {
margin-right: 5px;
}
.nav-pills > li > a > .badge {
margin-left: 3px;
}
.jumbotron {
padding-top: 30px;
padding-bottom: 30px;
margin-bottom: 30px;
color: inherit;
background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
color: inherit;
}
.jumbotron p {
margin-bottom: 15px;
font-size: 20px;
font-weight: 200;
}
.jumbotron > hr {
border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
border-radius: 3px;
padding-left: 0px;
padding-right: 0px;
}
.jumbotron .container {
max-width: 100%;
}
@media screen and (min-width: 768px) {
.jumbotron {
padding-top: 48px;
padding-bottom: 48px;
}
.container .jumbotron,
.container-fluid .jumbotron {
padding-left: 60px;
padding-right: 60px;
}
.jumbotron h1,
.jumbotron .h1 {
font-size: 59px;
}
}
.thumbnail {
display: block;
padding: 4px;
margin-bottom: 18px;
line-height: 1.42857143;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 2px;
-webkit-transition: border 0.2s ease-in-out;
-o-transition: border 0.2s ease-in-out;
transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
margin-left: auto;
margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
border-color: #337ab7;
}
.thumbnail .caption {
padding: 9px;
color: #000;
}
.alert {
padding: 15px;
margin-bottom: 18px;
border: 1px solid transparent;
border-radius: 2px;
}
.alert h4 {
margin-top: 0;
color: inherit;
}
.alert .alert-link {
font-weight: bold;
}
.alert > p,
.alert > ul {
margin-bottom: 0;
}
.alert > p + p {
margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
position: relative;
top: -2px;
right: -21px;
color: inherit;
}
.alert-success {
background-color: #dff0d8;
border-color: #d6e9c6;
color: #3c763d;
}
.alert-success hr {
border-top-color: #c9e2b3;
}
.alert-success .alert-link {
color: #2b542c;
}
.alert-info {
background-color: #d9edf7;
border-color: #bce8f1;
color: #31708f;
}
.alert-info hr {
border-top-color: #a6e1ec;
}
.alert-info .alert-link {
color: #245269;
}
.alert-warning {
background-color: #fcf8e3;
border-color: #faebcc;
color: #8a6d3b;
}
.alert-warning hr {
border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
color: #66512c;
}
.alert-danger {
background-color: #f2dede;
border-color: #ebccd1;
color: #a94442;
}
.alert-danger hr {
border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
from {
background-position: 40px 0;
}
to {
background-position: 0 0;
}
}
@keyframes progress-bar-stripes {
from {
background-position: 40px 0;
}
to {
background-position: 0 0;
}
}
.progress {
overflow: hidden;
height: 18px;
margin-bottom: 18px;
background-color: #f5f5f5;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 18px;
color: #fff;
text-align: center;
background-color: #337ab7;
-webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-webkit-transition: width 0.6s ease;
-o-transition: width 0.6s ease;
transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
-webkit-animation: progress-bar-stripes 2s linear infinite;
-o-animation: progress-bar-stripes 2s linear infinite;
animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
margin-top: 15px;
}
.media:first-child {
margin-top: 0;
}
.media,
.media-body {
zoom: 1;
overflow: hidden;
}
.media-body {
width: 10000px;
}
.media-object {
display: block;
}
.media-object.img-thumbnail {
max-width: none;
}
.media-right,
.media > .pull-right {
padding-left: 10px;
}
.media-left,
.media > .pull-left {
padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
display: table-cell;
vertical-align: top;
}
.media-middle {
vertical-align: middle;
}
.media-bottom {
vertical-align: bottom;
}
.media-heading {
margin-top: 0;
margin-bottom: 5px;
}
.media-list {
padding-left: 0;
list-style: none;
}
.list-group {
margin-bottom: 20px;
padding-left: 0;
}
.list-group-item {
position: relative;
display: block;
padding: 10px 15px;
margin-bottom: -1px;
background-color: #fff;
border: 1px solid #ddd;
}
.list-group-item:first-child {
border-top-right-radius: 2px;
border-top-left-radius: 2px;
}
.list-group-item:last-child {
margin-bottom: 0;
border-bottom-right-radius: 2px;
border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
text-decoration: none;
color: #555;
background-color: #f5f5f5;
}
button.list-group-item {
width: 100%;
text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
background-color: #eeeeee;
color: #777777;
cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
z-index: 2;
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
color: #c7ddef;
}
.list-group-item-success {
color: #3c763d;
background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
color: #3c763d;
background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
color: #fff;
background-color: #3c763d;
border-color: #3c763d;
}
.list-group-item-info {
color: #31708f;
background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
color: #31708f;
background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
color: #fff;
background-color: #31708f;
border-color: #31708f;
}
.list-group-item-warning {
color: #8a6d3b;
background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
color: #8a6d3b;
background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
color: #fff;
background-color: #8a6d3b;
border-color: #8a6d3b;
}
.list-group-item-danger {
color: #a94442;
background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
color: #a94442;
background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
color: #fff;
background-color: #a94442;
border-color: #a94442;
}
.list-group-item-heading {
margin-top: 0;
margin-bottom: 5px;
}
.list-group-item-text {
margin-bottom: 0;
line-height: 1.3;
}
.panel {
margin-bottom: 18px;
background-color: #fff;
border: 1px solid transparent;
border-radius: 2px;
-webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
padding: 15px;
}
.panel-heading {
padding: 10px 15px;
border-bottom: 1px solid transparent;
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
color: inherit;
}
.panel-title {
margin-top: 0;
margin-bottom: 0;
font-size: 15px;
color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
color: inherit;
}
.panel-footer {
padding: 10px 15px;
background-color: #f5f5f5;
border-top: 1px solid #ddd;
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
border-width: 1px 0;
border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
border-top: 0;
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
border-bottom: 0;
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
border-top-width: 0;
}
.list-group + .panel-footer {
border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
padding-left: 15px;
padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
border-top-left-radius: 1px;
border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
border-bottom-left-radius: 1px;
border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
border-bottom: 0;
}
.panel > .table-responsive {
border: 0;
margin-bottom: 0;
}
.panel-group {
margin-bottom: 18px;
}
.panel-group .panel {
margin-bottom: 0;
border-radius: 2px;
}
.panel-group .panel + .panel {
margin-top: 5px;
}
.panel-group .panel-heading {
border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
border-bottom: 1px solid #ddd;
}
.panel-default {
border-color: #ddd;
}
.panel-default > .panel-heading {
color: #333333;
background-color: #f5f5f5;
border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
color: #f5f5f5;
background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #ddd;
}
.panel-primary {
border-color: #337ab7;
}
.panel-primary > .panel-heading {
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
color: #337ab7;
background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #337ab7;
}
.panel-success {
border-color: #d6e9c6;
}
.panel-success > .panel-heading {
color: #3c763d;
background-color: #dff0d8;
border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
color: #dff0d8;
background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #d6e9c6;
}
.panel-info {
border-color: #bce8f1;
}
.panel-info > .panel-heading {
color: #31708f;
background-color: #d9edf7;
border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
color: #d9edf7;
background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #bce8f1;
}
.panel-warning {
border-color: #faebcc;
}
.panel-warning > .panel-heading {
color: #8a6d3b;
background-color: #fcf8e3;
border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
color: #fcf8e3;
background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #faebcc;
}
.panel-danger {
border-color: #ebccd1;
}
.panel-danger > .panel-heading {
color: #a94442;
background-color: #f2dede;
border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
color: #f2dede;
background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #ebccd1;
}
.embed-responsive {
position: relative;
display: block;
height: 0;
padding: 0;
overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
position: absolute;
top: 0;
left: 0;
bottom: 0;
height: 100%;
width: 100%;
border: 0;
}
.embed-responsive-16by9 {
padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
padding-bottom: 75%;
}
.well {
min-height: 20px;
padding: 19px;
margin-bottom: 20px;
background-color: #f5f5f5;
border: 1px solid #e3e3e3;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
border-color: #ddd;
border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
padding: 24px;
border-radius: 3px;
}
.well-sm {
padding: 9px;
border-radius: 1px;
}
.close {
float: right;
font-size: 19.5px;
font-weight: bold;
line-height: 1;
color: #000;
text-shadow: 0 1px 0 #fff;
opacity: 0.2;
filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
color: #000;
text-decoration: none;
cursor: pointer;
opacity: 0.5;
filter: alpha(opacity=50);
}
button.close {
padding: 0;
cursor: pointer;
background: transparent;
border: 0;
-webkit-appearance: none;
}
.modal-open {
overflow: hidden;
}
.modal {
display: none;
overflow: hidden;
position: fixed;
top: 0;
right: 0;
bottom: 0;
left: 0;
z-index: 1050;
-webkit-overflow-scrolling: touch;
outline: 0;
}
.modal.fade .modal-dialog {
-webkit-transform: translate(0, -25%);
-ms-transform: translate(0, -25%);
-o-transform: translate(0, -25%);
transform: translate(0, -25%);
-webkit-transition: -webkit-transform 0.3s ease-out;
-moz-transition: -moz-transform 0.3s ease-out;
-o-transition: -o-transform 0.3s ease-out;
transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
-webkit-transform: translate(0, 0);
-ms-transform: translate(0, 0);
-o-transform: translate(0, 0);
transform: translate(0, 0);
}
.modal-open .modal {
overflow-x: hidden;
overflow-y: auto;
}
.modal-dialog {
position: relative;
width: auto;
margin: 10px;
}
.modal-content {
position: relative;
background-color: #fff;
border: 1px solid #999;
border: 1px solid rgba(0, 0, 0, 0.2);
border-radius: 3px;
-webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
background-clip: padding-box;
outline: 0;
}
.modal-backdrop {
position: fixed;
top: 0;
right: 0;
bottom: 0;
left: 0;
z-index: 1040;
background-color: #000;
}
.modal-backdrop.fade {
opacity: 0;
filter: alpha(opacity=0);
}
.modal-backdrop.in {
opacity: 0.5;
filter: alpha(opacity=50);
}
.modal-header {
padding: 15px;
border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
margin-top: -2px;
}
.modal-title {
margin: 0;
line-height: 1.42857143;
}
.modal-body {
position: relative;
padding: 15px;
}
.modal-footer {
padding: 15px;
text-align: right;
border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
margin-left: 5px;
margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
margin-left: 0;
}
.modal-scrollbar-measure {
position: absolute;
top: -9999px;
width: 50px;
height: 50px;
overflow: scroll;
}
@media (min-width: 768px) {
.modal-dialog {
width: 600px;
margin: 30px auto;
}
.modal-content {
-webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
}
.modal-sm {
width: 300px;
}
}
@media (min-width: 992px) {
.modal-lg {
width: 900px;
}
}
.tooltip {
position: absolute;
z-index: 1070;
display: block;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-style: normal;
font-weight: normal;
letter-spacing: normal;
line-break: auto;
line-height: 1.42857143;
text-align: left;
text-align: start;
text-decoration: none;
text-shadow: none;
text-transform: none;
white-space: normal;
word-break: normal;
word-spacing: normal;
word-wrap: normal;
font-size: 12px;
opacity: 0;
filter: alpha(opacity=0);
}
.tooltip.in {
opacity: 0.9;
filter: alpha(opacity=90);
}
.tooltip.top {
margin-top: -3px;
padding: 5px 0;
}
.tooltip.right {
margin-left: 3px;
padding: 0 5px;
}
.tooltip.bottom {
margin-top: 3px;
padding: 5px 0;
}
.tooltip.left {
margin-left: -3px;
padding: 0 5px;
}
.tooltip-inner {
max-width: 200px;
padding: 3px 8px;
color: #fff;
text-align: center;
background-color: #000;
border-radius: 2px;
}
.tooltip-arrow {
position: absolute;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
}
.tooltip.top .tooltip-arrow {
bottom: 0;
left: 50%;
margin-left: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
bottom: 0;
right: 5px;
margin-bottom: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
bottom: 0;
left: 5px;
margin-bottom: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
top: 50%;
left: 0;
margin-top: -5px;
border-width: 5px 5px 5px 0;
border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
top: 50%;
right: 0;
margin-top: -5px;
border-width: 5px 0 5px 5px;
border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
top: 0;
left: 50%;
margin-left: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
top: 0;
right: 5px;
margin-top: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
top: 0;
left: 5px;
margin-top: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.popover {
position: absolute;
top: 0;
left: 0;
z-index: 1060;
display: none;
max-width: 276px;
padding: 1px;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-style: normal;
font-weight: normal;
letter-spacing: normal;
line-break: auto;
line-height: 1.42857143;
text-align: left;
text-align: start;
text-decoration: none;
text-shadow: none;
text-transform: none;
white-space: normal;
word-break: normal;
word-spacing: normal;
word-wrap: normal;
font-size: 13px;
background-color: #fff;
background-clip: padding-box;
border: 1px solid #ccc;
border: 1px solid rgba(0, 0, 0, 0.2);
border-radius: 3px;
-webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
margin-top: -10px;
}
.popover.right {
margin-left: 10px;
}
.popover.bottom {
margin-top: 10px;
}
.popover.left {
margin-left: -10px;
}
.popover-title {
margin: 0;
padding: 8px 14px;
font-size: 13px;
background-color: #f7f7f7;
border-bottom: 1px solid #ebebeb;
border-radius: 2px 2px 0 0;
}
.popover-content {
padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
position: absolute;
display: block;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
}
.popover > .arrow {
border-width: 11px;
}
.popover > .arrow:after {
border-width: 10px;
content: "";
}
.popover.top > .arrow {
left: 50%;
margin-left: -11px;
border-bottom-width: 0;
border-top-color: #999999;
border-top-color: rgba(0, 0, 0, 0.25);
bottom: -11px;
}
.popover.top > .arrow:after {
content: " ";
bottom: 1px;
margin-left: -10px;
border-bottom-width: 0;
border-top-color: #fff;
}
.popover.right > .arrow {
top: 50%;
left: -11px;
margin-top: -11px;
border-left-width: 0;
border-right-color: #999999;
border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
content: " ";
left: 1px;
bottom: -10px;
border-left-width: 0;
border-right-color: #fff;
}
.popover.bottom > .arrow {
left: 50%;
margin-left: -11px;
border-top-width: 0;
border-bottom-color: #999999;
border-bottom-color: rgba(0, 0, 0, 0.25);
top: -11px;
}
.popover.bottom > .arrow:after {
content: " ";
top: 1px;
margin-left: -10px;
border-top-width: 0;
border-bottom-color: #fff;
}
.popover.left > .arrow {
top: 50%;
right: -11px;
margin-top: -11px;
border-right-width: 0;
border-left-color: #999999;
border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
content: " ";
right: 1px;
border-right-width: 0;
border-left-color: #fff;
bottom: -10px;
}
.carousel {
position: relative;
}
.carousel-inner {
position: relative;
overflow: hidden;
width: 100%;
}
.carousel-inner > .item {
display: none;
position: relative;
-webkit-transition: 0.6s ease-in-out left;
-o-transition: 0.6s ease-in-out left;
transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
.carousel-inner > .item {
-webkit-transition: -webkit-transform 0.6s ease-in-out;
-moz-transition: -moz-transform 0.6s ease-in-out;
-o-transition: -o-transform 0.6s ease-in-out;
transition: transform 0.6s ease-in-out;
-webkit-backface-visibility: hidden;
-moz-backface-visibility: hidden;
backface-visibility: hidden;
-webkit-perspective: 1000px;
-moz-perspective: 1000px;
perspective: 1000px;
}
.carousel-inner > .item.next,
.carousel-inner > .item.active.right {
-webkit-transform: translate3d(100%, 0, 0);
transform: translate3d(100%, 0, 0);
left: 0;
}
.carousel-inner > .item.prev,
.carousel-inner > .item.active.left {
-webkit-transform: translate3d(-100%, 0, 0);
transform: translate3d(-100%, 0, 0);
left: 0;
}
.carousel-inner > .item.next.left,
.carousel-inner > .item.prev.right,
.carousel-inner > .item.active {
-webkit-transform: translate3d(0, 0, 0);
transform: translate3d(0, 0, 0);
left: 0;
}
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
display: block;
}
.carousel-inner > .active {
left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
position: absolute;
top: 0;
width: 100%;
}
.carousel-inner > .next {
left: 100%;
}
.carousel-inner > .prev {
left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
left: 0;
}
.carousel-inner > .active.left {
left: -100%;
}
.carousel-inner > .active.right {
left: 100%;
}
.carousel-control {
position: absolute;
top: 0;
left: 0;
bottom: 0;
width: 15%;
opacity: 0.5;
filter: alpha(opacity=50);
font-size: 20px;
color: #fff;
text-align: center;
text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-repeat: repeat-x;
filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
left: auto;
right: 0;
background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-repeat: repeat-x;
filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
outline: 0;
color: #fff;
text-decoration: none;
opacity: 0.9;
filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
position: absolute;
top: 50%;
margin-top: -10px;
z-index: 5;
display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
left: 50%;
margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
right: 50%;
margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
width: 20px;
height: 20px;
line-height: 1;
font-family: serif;
}
.carousel-control .icon-prev:before {
content: '\2039';
}
.carousel-control .icon-next:before {
content: '\203a';
}
.carousel-indicators {
position: absolute;
bottom: 10px;
left: 50%;
z-index: 15;
width: 60%;
margin-left: -30%;
padding-left: 0;
list-style: none;
text-align: center;
}
.carousel-indicators li {
display: inline-block;
width: 10px;
height: 10px;
margin: 1px;
text-indent: -999px;
border: 1px solid #fff;
border-radius: 10px;
cursor: pointer;
background-color: #000 \9;
background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
margin: 0;
width: 12px;
height: 12px;
background-color: #fff;
}
.carousel-caption {
position: absolute;
left: 15%;
right: 15%;
bottom: 20px;
z-index: 10;
padding-top: 20px;
padding-bottom: 20px;
color: #fff;
text-align: center;
text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
text-shadow: none;
}
@media screen and (min-width: 768px) {
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right,
.carousel-control .icon-prev,
.carousel-control .icon-next {
width: 30px;
height: 30px;
margin-top: -10px;
font-size: 30px;
}
.carousel-control .glyphicon-chevron-left,
.carousel-control .icon-prev {
margin-left: -10px;
}
.carousel-control .glyphicon-chevron-right,
.carousel-control .icon-next {
margin-right: -10px;
}
.carousel-caption {
left: 20%;
right: 20%;
padding-bottom: 30px;
}
.carousel-indicators {
bottom: 20px;
}
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
content: " ";
display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
clear: both;
}
.center-block {
display: block;
margin-left: auto;
margin-right: auto;
}
.pull-right {
float: right !important;
}
.pull-left {
float: left !important;
}
.hide {
display: none !important;
}
.show {
display: block !important;
}
.invisible {
visibility: hidden;
}
.text-hide {
font: 0/0 a;
color: transparent;
text-shadow: none;
background-color: transparent;
border: 0;
}
.hidden {
display: none !important;
}
.affix {
position: fixed;
}
@-ms-viewport {
width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
display: none !important;
}
@media (max-width: 767px) {
.visible-xs {
display: block !important;
}
table.visible-xs {
display: table !important;
}
tr.visible-xs {
display: table-row !important;
}
th.visible-xs,
td.visible-xs {
display: table-cell !important;
}
}
@media (max-width: 767px) {
.visible-xs-block {
display: block !important;
}
}
@media (max-width: 767px) {
.visible-xs-inline {
display: inline !important;
}
}
@media (max-width: 767px) {
.visible-xs-inline-block {
display: inline-block !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm {
display: block !important;
}
table.visible-sm {
display: table !important;
}
tr.visible-sm {
display: table-row !important;
}
th.visible-sm,
td.visible-sm {
display: table-cell !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-block {
display: block !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-inline {
display: inline !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-inline-block {
display: inline-block !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md {
display: block !important;
}
table.visible-md {
display: table !important;
}
tr.visible-md {
display: table-row !important;
}
th.visible-md,
td.visible-md {
display: table-cell !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-block {
display: block !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-inline {
display: inline !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-inline-block {
display: inline-block !important;
}
}
@media (min-width: 1200px) {
.visible-lg {
display: block !important;
}
table.visible-lg {
display: table !important;
}
tr.visible-lg {
display: table-row !important;
}
th.visible-lg,
td.visible-lg {
display: table-cell !important;
}
}
@media (min-width: 1200px) {
.visible-lg-block {
display: block !important;
}
}
@media (min-width: 1200px) {
.visible-lg-inline {
display: inline !important;
}
}
@media (min-width: 1200px) {
.visible-lg-inline-block {
display: inline-block !important;
}
}
@media (max-width: 767px) {
.hidden-xs {
display: none !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.hidden-sm {
display: none !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.hidden-md {
display: none !important;
}
}
@media (min-width: 1200px) {
.hidden-lg {
display: none !important;
}
}
.visible-print {
display: none !important;
}
@media print {
.visible-print {
display: block !important;
}
table.visible-print {
display: table !important;
}
tr.visible-print {
display: table-row !important;
}
th.visible-print,
td.visible-print {
display: table-cell !important;
}
}
.visible-print-block {
display: none !important;
}
@media print {
.visible-print-block {
display: block !important;
}
}
.visible-print-inline {
display: none !important;
}
@media print {
.visible-print-inline {
display: inline !important;
}
}
.visible-print-inline-block {
display: none !important;
}
@media print {
.visible-print-inline-block {
display: inline-block !important;
}
}
@media print {
.hidden-print {
display: none !important;
}
}
/*!
*
* Font Awesome
*
*/
/*!
* Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
*/
/* FONT PATH
* -------------------------- */
@font-face {
font-family: 'FontAwesome';
src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
font-weight: normal;
font-style: normal;
}
.fa {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
font-size: 1.33333333em;
line-height: 0.75em;
vertical-align: -15%;
}
.fa-2x {
font-size: 2em;
}
.fa-3x {
font-size: 3em;
}
.fa-4x {
font-size: 4em;
}
.fa-5x {
font-size: 5em;
}
.fa-fw {
width: 1.28571429em;
text-align: center;
}
.fa-ul {
padding-left: 0;
margin-left: 2.14285714em;
list-style-type: none;
}
.fa-ul > li {
position: relative;
}
.fa-li {
position: absolute;
left: -2.14285714em;
width: 2.14285714em;
top: 0.14285714em;
text-align: center;
}
.fa-li.fa-lg {
left: -1.85714286em;
}
.fa-border {
padding: .2em .25em .15em;
border: solid 0.08em #eee;
border-radius: .1em;
}
.pull-right {
float: right;
}
.pull-left {
float: left;
}
.fa.pull-left {
margin-right: .3em;
}
.fa.pull-right {
margin-left: .3em;
}
.fa-spin {
-webkit-animation: fa-spin 2s infinite linear;
animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(359deg);
transform: rotate(359deg);
}
}
@keyframes fa-spin {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(359deg);
transform: rotate(359deg);
}
}
.fa-rotate-90 {
filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
-webkit-transform: rotate(90deg);
-ms-transform: rotate(90deg);
transform: rotate(90deg);
}
.fa-rotate-180 {
filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
-webkit-transform: rotate(180deg);
-ms-transform: rotate(180deg);
transform: rotate(180deg);
}
.fa-rotate-270 {
filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
-webkit-transform: rotate(270deg);
-ms-transform: rotate(270deg);
transform: rotate(270deg);
}
.fa-flip-horizontal {
filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
-webkit-transform: scale(-1, 1);
-ms-transform: scale(-1, 1);
transform: scale(-1, 1);
}
.fa-flip-vertical {
filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
-webkit-transform: scale(1, -1);
-ms-transform: scale(1, -1);
transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
filter: none;
}
.fa-stack {
position: relative;
display: inline-block;
width: 2em;
height: 2em;
line-height: 2em;
vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
position: absolute;
left: 0;
width: 100%;
text-align: center;
}
.fa-stack-1x {
line-height: inherit;
}
.fa-stack-2x {
font-size: 2em;
}
.fa-inverse {
color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
readers do not read off random characters that represent icons */
.fa-glass:before {
content: "\f000";
}
.fa-music:before {
content: "\f001";
}
.fa-search:before {
content: "\f002";
}
.fa-envelope-o:before {
content: "\f003";
}
.fa-heart:before {
content: "\f004";
}
.fa-star:before {
content: "\f005";
}
.fa-star-o:before {
content: "\f006";
}
.fa-user:before {
content: "\f007";
}
.fa-film:before {
content: "\f008";
}
.fa-th-large:before {
content: "\f009";
}
.fa-th:before {
content: "\f00a";
}
.fa-th-list:before {
content: "\f00b";
}
.fa-check:before {
content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
content: "\f00d";
}
.fa-search-plus:before {
content: "\f00e";
}
.fa-search-minus:before {
content: "\f010";
}
.fa-power-off:before {
content: "\f011";
}
.fa-signal:before {
content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
content: "\f013";
}
.fa-trash-o:before {
content: "\f014";
}
.fa-home:before {
content: "\f015";
}
.fa-file-o:before {
content: "\f016";
}
.fa-clock-o:before {
content: "\f017";
}
.fa-road:before {
content: "\f018";
}
.fa-download:before {
content: "\f019";
}
.fa-arrow-circle-o-down:before {
content: "\f01a";
}
.fa-arrow-circle-o-up:before {
content: "\f01b";
}
.fa-inbox:before {
content: "\f01c";
}
.fa-play-circle-o:before {
content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
content: "\f01e";
}
.fa-refresh:before {
content: "\f021";
}
.fa-list-alt:before {
content: "\f022";
}
.fa-lock:before {
content: "\f023";
}
.fa-flag:before {
content: "\f024";
}
.fa-headphones:before {
content: "\f025";
}
.fa-volume-off:before {
content: "\f026";
}
.fa-volume-down:before {
content: "\f027";
}
.fa-volume-up:before {
content: "\f028";
}
.fa-qrcode:before {
content: "\f029";
}
.fa-barcode:before {
content: "\f02a";
}
.fa-tag:before {
content: "\f02b";
}
.fa-tags:before {
content: "\f02c";
}
.fa-book:before {
content: "\f02d";
}
.fa-bookmark:before {
content: "\f02e";
}
.fa-print:before {
content: "\f02f";
}
.fa-camera:before {
content: "\f030";
}
.fa-font:before {
content: "\f031";
}
.fa-bold:before {
content: "\f032";
}
.fa-italic:before {
content: "\f033";
}
.fa-text-height:before {
content: "\f034";
}
.fa-text-width:before {
content: "\f035";
}
.fa-align-left:before {
content: "\f036";
}
.fa-align-center:before {
content: "\f037";
}
.fa-align-right:before {
content: "\f038";
}
.fa-align-justify:before {
content: "\f039";
}
.fa-list:before {
content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
content: "\f03b";
}
.fa-indent:before {
content: "\f03c";
}
.fa-video-camera:before {
content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
content: "\f03e";
}
.fa-pencil:before {
content: "\f040";
}
.fa-map-marker:before {
content: "\f041";
}
.fa-adjust:before {
content: "\f042";
}
.fa-tint:before {
content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
content: "\f044";
}
.fa-share-square-o:before {
content: "\f045";
}
.fa-check-square-o:before {
content: "\f046";
}
.fa-arrows:before {
content: "\f047";
}
.fa-step-backward:before {
content: "\f048";
}
.fa-fast-backward:before {
content: "\f049";
}
.fa-backward:before {
content: "\f04a";
}
.fa-play:before {
content: "\f04b";
}
.fa-pause:before {
content: "\f04c";
}
.fa-stop:before {
content: "\f04d";
}
.fa-forward:before {
content: "\f04e";
}
.fa-fast-forward:before {
content: "\f050";
}
.fa-step-forward:before {
content: "\f051";
}
.fa-eject:before {
content: "\f052";
}
.fa-chevron-left:before {
content: "\f053";
}
.fa-chevron-right:before {
content: "\f054";
}
.fa-plus-circle:before {
content: "\f055";
}
.fa-minus-circle:before {
content: "\f056";
}
.fa-times-circle:before {
content: "\f057";
}
.fa-check-circle:before {
content: "\f058";
}
.fa-question-circle:before {
content: "\f059";
}
.fa-info-circle:before {
content: "\f05a";
}
.fa-crosshairs:before {
content: "\f05b";
}
.fa-times-circle-o:before {
content: "\f05c";
}
.fa-check-circle-o:before {
content: "\f05d";
}
.fa-ban:before {
content: "\f05e";
}
.fa-arrow-left:before {
content: "\f060";
}
.fa-arrow-right:before {
content: "\f061";
}
.fa-arrow-up:before {
content: "\f062";
}
.fa-arrow-down:before {
content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
content: "\f064";
}
.fa-expand:before {
content: "\f065";
}
.fa-compress:before {
content: "\f066";
}
.fa-plus:before {
content: "\f067";
}
.fa-minus:before {
content: "\f068";
}
.fa-asterisk:before {
content: "\f069";
}
.fa-exclamation-circle:before {
content: "\f06a";
}
.fa-gift:before {
content: "\f06b";
}
.fa-leaf:before {
content: "\f06c";
}
.fa-fire:before {
content: "\f06d";
}
.fa-eye:before {
content: "\f06e";
}
.fa-eye-slash:before {
content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
content: "\f071";
}
.fa-plane:before {
content: "\f072";
}
.fa-calendar:before {
content: "\f073";
}
.fa-random:before {
content: "\f074";
}
.fa-comment:before {
content: "\f075";
}
.fa-magnet:before {
content: "\f076";
}
.fa-chevron-up:before {
content: "\f077";
}
.fa-chevron-down:before {
content: "\f078";
}
.fa-retweet:before {
content: "\f079";
}
.fa-shopping-cart:before {
content: "\f07a";
}
.fa-folder:before {
content: "\f07b";
}
.fa-folder-open:before {
content: "\f07c";
}
.fa-arrows-v:before {
content: "\f07d";
}
.fa-arrows-h:before {
content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
content: "\f080";
}
.fa-twitter-square:before {
content: "\f081";
}
.fa-facebook-square:before {
content: "\f082";
}
.fa-camera-retro:before {
content: "\f083";
}
.fa-key:before {
content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
content: "\f085";
}
.fa-comments:before {
content: "\f086";
}
.fa-thumbs-o-up:before {
content: "\f087";
}
.fa-thumbs-o-down:before {
content: "\f088";
}
.fa-star-half:before {
content: "\f089";
}
.fa-heart-o:before {
content: "\f08a";
}
.fa-sign-out:before {
content: "\f08b";
}
.fa-linkedin-square:before {
content: "\f08c";
}
.fa-thumb-tack:before {
content: "\f08d";
}
.fa-external-link:before {
content: "\f08e";
}
.fa-sign-in:before {
content: "\f090";
}
.fa-trophy:before {
content: "\f091";
}
.fa-github-square:before {
content: "\f092";
}
.fa-upload:before {
content: "\f093";
}
.fa-lemon-o:before {
content: "\f094";
}
.fa-phone:before {
content: "\f095";
}
.fa-square-o:before {
content: "\f096";
}
.fa-bookmark-o:before {
content: "\f097";
}
.fa-phone-square:before {
content: "\f098";
}
.fa-twitter:before {
content: "\f099";
}
.fa-facebook:before {
content: "\f09a";
}
.fa-github:before {
content: "\f09b";
}
.fa-unlock:before {
content: "\f09c";
}
.fa-credit-card:before {
content: "\f09d";
}
.fa-rss:before {
content: "\f09e";
}
.fa-hdd-o:before {
content: "\f0a0";
}
.fa-bullhorn:before {
content: "\f0a1";
}
.fa-bell:before {
content: "\f0f3";
}
.fa-certificate:before {
content: "\f0a3";
}
.fa-hand-o-right:before {
content: "\f0a4";
}
.fa-hand-o-left:before {
content: "\f0a5";
}
.fa-hand-o-up:before {
content: "\f0a6";
}
.fa-hand-o-down:before {
content: "\f0a7";
}
.fa-arrow-circle-left:before {
content: "\f0a8";
}
.fa-arrow-circle-right:before {
content: "\f0a9";
}
.fa-arrow-circle-up:before {
content: "\f0aa";
}
.fa-arrow-circle-down:before {
content: "\f0ab";
}
.fa-globe:before {
content: "\f0ac";
}
.fa-wrench:before {
content: "\f0ad";
}
.fa-tasks:before {
content: "\f0ae";
}
.fa-filter:before {
content: "\f0b0";
}
.fa-briefcase:before {
content: "\f0b1";
}
.fa-arrows-alt:before {
content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
content: "\f0c1";
}
.fa-cloud:before {
content: "\f0c2";
}
.fa-flask:before {
content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
content: "\f0c5";
}
.fa-paperclip:before {
content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
content: "\f0c7";
}
.fa-square:before {
content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
content: "\f0c9";
}
.fa-list-ul:before {
content: "\f0ca";
}
.fa-list-ol:before {
content: "\f0cb";
}
.fa-strikethrough:before {
content: "\f0cc";
}
.fa-underline:before {
content: "\f0cd";
}
.fa-table:before {
content: "\f0ce";
}
.fa-magic:before {
content: "\f0d0";
}
.fa-truck:before {
content: "\f0d1";
}
.fa-pinterest:before {
content: "\f0d2";
}
.fa-pinterest-square:before {
content: "\f0d3";
}
.fa-google-plus-square:before {
content: "\f0d4";
}
.fa-google-plus:before {
content: "\f0d5";
}
.fa-money:before {
content: "\f0d6";
}
.fa-caret-down:before {
content: "\f0d7";
}
.fa-caret-up:before {
content: "\f0d8";
}
.fa-caret-left:before {
content: "\f0d9";
}
.fa-caret-right:before {
content: "\f0da";
}
.fa-columns:before {
content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
content: "\f0de";
}
.fa-envelope:before {
content: "\f0e0";
}
.fa-linkedin:before {
content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
content: "\f0e4";
}
.fa-comment-o:before {
content: "\f0e5";
}
.fa-comments-o:before {
content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
content: "\f0e7";
}
.fa-sitemap:before {
content: "\f0e8";
}
.fa-umbrella:before {
content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
content: "\f0ea";
}
.fa-lightbulb-o:before {
content: "\f0eb";
}
.fa-exchange:before {
content: "\f0ec";
}
.fa-cloud-download:before {
content: "\f0ed";
}
.fa-cloud-upload:before {
content: "\f0ee";
}
.fa-user-md:before {
content: "\f0f0";
}
.fa-stethoscope:before {
content: "\f0f1";
}
.fa-suitcase:before {
content: "\f0f2";
}
.fa-bell-o:before {
content: "\f0a2";
}
.fa-coffee:before {
content: "\f0f4";
}
.fa-cutlery:before {
content: "\f0f5";
}
.fa-file-text-o:before {
content: "\f0f6";
}
.fa-building-o:before {
content: "\f0f7";
}
.fa-hospital-o:before {
content: "\f0f8";
}
.fa-ambulance:before {
content: "\f0f9";
}
.fa-medkit:before {
content: "\f0fa";
}
.fa-fighter-jet:before {
content: "\f0fb";
}
.fa-beer:before {
content: "\f0fc";
}
.fa-h-square:before {
content: "\f0fd";
}
.fa-plus-square:before {
content: "\f0fe";
}
.fa-angle-double-left:before {
content: "\f100";
}
.fa-angle-double-right:before {
content: "\f101";
}
.fa-angle-double-up:before {
content: "\f102";
}
.fa-angle-double-down:before {
content: "\f103";
}
.fa-angle-left:before {
content: "\f104";
}
.fa-angle-right:before {
content: "\f105";
}
.fa-angle-up:before {
content: "\f106";
}
.fa-angle-down:before {
content: "\f107";
}
.fa-desktop:before {
content: "\f108";
}
.fa-laptop:before {
content: "\f109";
}
.fa-tablet:before {
content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
content: "\f10b";
}
.fa-circle-o:before {
content: "\f10c";
}
.fa-quote-left:before {
content: "\f10d";
}
.fa-quote-right:before {
content: "\f10e";
}
.fa-spinner:before {
content: "\f110";
}
.fa-circle:before {
content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
content: "\f112";
}
.fa-github-alt:before {
content: "\f113";
}
.fa-folder-o:before {
content: "\f114";
}
.fa-folder-open-o:before {
content: "\f115";
}
.fa-smile-o:before {
content: "\f118";
}
.fa-frown-o:before {
content: "\f119";
}
.fa-meh-o:before {
content: "\f11a";
}
.fa-gamepad:before {
content: "\f11b";
}
.fa-keyboard-o:before {
content: "\f11c";
}
.fa-flag-o:before {
content: "\f11d";
}
.fa-flag-checkered:before {
content: "\f11e";
}
.fa-terminal:before {
content: "\f120";
}
.fa-code:before {
content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
content: "\f123";
}
.fa-location-arrow:before {
content: "\f124";
}
.fa-crop:before {
content: "\f125";
}
.fa-code-fork:before {
content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
content: "\f127";
}
.fa-question:before {
content: "\f128";
}
.fa-info:before {
content: "\f129";
}
.fa-exclamation:before {
content: "\f12a";
}
.fa-superscript:before {
content: "\f12b";
}
.fa-subscript:before {
content: "\f12c";
}
.fa-eraser:before {
content: "\f12d";
}
.fa-puzzle-piece:before {
content: "\f12e";
}
.fa-microphone:before {
content: "\f130";
}
.fa-microphone-slash:before {
content: "\f131";
}
.fa-shield:before {
content: "\f132";
}
.fa-calendar-o:before {
content: "\f133";
}
.fa-fire-extinguisher:before {
content: "\f134";
}
.fa-rocket:before {
content: "\f135";
}
.fa-maxcdn:before {
content: "\f136";
}
.fa-chevron-circle-left:before {
content: "\f137";
}
.fa-chevron-circle-right:before {
content: "\f138";
}
.fa-chevron-circle-up:before {
content: "\f139";
}
.fa-chevron-circle-down:before {
content: "\f13a";
}
.fa-html5:before {
content: "\f13b";
}
.fa-css3:before {
content: "\f13c";
}
.fa-anchor:before {
content: "\f13d";
}
.fa-unlock-alt:before {
content: "\f13e";
}
.fa-bullseye:before {
content: "\f140";
}
.fa-ellipsis-h:before {
content: "\f141";
}
.fa-ellipsis-v:before {
content: "\f142";
}
.fa-rss-square:before {
content: "\f143";
}
.fa-play-circle:before {
content: "\f144";
}
.fa-ticket:before {
content: "\f145";
}
.fa-minus-square:before {
content: "\f146";
}
.fa-minus-square-o:before {
content: "\f147";
}
.fa-level-up:before {
content: "\f148";
}
.fa-level-down:before {
content: "\f149";
}
.fa-check-square:before {
content: "\f14a";
}
.fa-pencil-square:before {
content: "\f14b";
}
.fa-external-link-square:before {
content: "\f14c";
}
.fa-share-square:before {
content: "\f14d";
}
.fa-compass:before {
content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
content: "\f153";
}
.fa-gbp:before {
content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
content: "\f158";
}
.fa-won:before,
.fa-krw:before {
content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
content: "\f15a";
}
.fa-file:before {
content: "\f15b";
}
.fa-file-text:before {
content: "\f15c";
}
.fa-sort-alpha-asc:before {
content: "\f15d";
}
.fa-sort-alpha-desc:before {
content: "\f15e";
}
.fa-sort-amount-asc:before {
content: "\f160";
}
.fa-sort-amount-desc:before {
content: "\f161";
}
.fa-sort-numeric-asc:before {
content: "\f162";
}
.fa-sort-numeric-desc:before {
content: "\f163";
}
.fa-thumbs-up:before {
content: "\f164";
}
.fa-thumbs-down:before {
content: "\f165";
}
.fa-youtube-square:before {
content: "\f166";
}
.fa-youtube:before {
content: "\f167";
}
.fa-xing:before {
content: "\f168";
}
.fa-xing-square:before {
content: "\f169";
}
.fa-youtube-play:before {
content: "\f16a";
}
.fa-dropbox:before {
content: "\f16b";
}
.fa-stack-overflow:before {
content: "\f16c";
}
.fa-instagram:before {
content: "\f16d";
}
.fa-flickr:before {
content: "\f16e";
}
.fa-adn:before {
content: "\f170";
}
.fa-bitbucket:before {
content: "\f171";
}
.fa-bitbucket-square:before {
content: "\f172";
}
.fa-tumblr:before {
content: "\f173";
}
.fa-tumblr-square:before {
content: "\f174";
}
.fa-long-arrow-down:before {
content: "\f175";
}
.fa-long-arrow-up:before {
content: "\f176";
}
.fa-long-arrow-left:before {
content: "\f177";
}
.fa-long-arrow-right:before {
content: "\f178";
}
.fa-apple:before {
content: "\f179";
}
.fa-windows:before {
content: "\f17a";
}
.fa-android:before {
content: "\f17b";
}
.fa-linux:before {
content: "\f17c";
}
.fa-dribbble:before {
content: "\f17d";
}
.fa-skype:before {
content: "\f17e";
}
.fa-foursquare:before {
content: "\f180";
}
.fa-trello:before {
content: "\f181";
}
.fa-female:before {
content: "\f182";
}
.fa-male:before {
content: "\f183";
}
.fa-gittip:before {
content: "\f184";
}
.fa-sun-o:before {
content: "\f185";
}
.fa-moon-o:before {
content: "\f186";
}
.fa-archive:before {
content: "\f187";
}
.fa-bug:before {
content: "\f188";
}
.fa-vk:before {
content: "\f189";
}
.fa-weibo:before {
content: "\f18a";
}
.fa-renren:before {
content: "\f18b";
}
.fa-pagelines:before {
content: "\f18c";
}
.fa-stack-exchange:before {
content: "\f18d";
}
.fa-arrow-circle-o-right:before {
content: "\f18e";
}
.fa-arrow-circle-o-left:before {
content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
content: "\f191";
}
.fa-dot-circle-o:before {
content: "\f192";
}
.fa-wheelchair:before {
content: "\f193";
}
.fa-vimeo-square:before {
content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
content: "\f195";
}
.fa-plus-square-o:before {
content: "\f196";
}
.fa-space-shuttle:before {
content: "\f197";
}
.fa-slack:before {
content: "\f198";
}
.fa-envelope-square:before {
content: "\f199";
}
.fa-wordpress:before {
content: "\f19a";
}
.fa-openid:before {
content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
content: "\f19d";
}
.fa-yahoo:before {
content: "\f19e";
}
.fa-google:before {
content: "\f1a0";
}
.fa-reddit:before {
content: "\f1a1";
}
.fa-reddit-square:before {
content: "\f1a2";
}
.fa-stumbleupon-circle:before {
content: "\f1a3";
}
.fa-stumbleupon:before {
content: "\f1a4";
}
.fa-delicious:before {
content: "\f1a5";
}
.fa-digg:before {
content: "\f1a6";
}
.fa-pied-piper:before {
content: "\f1a7";
}
.fa-pied-piper-alt:before {
content: "\f1a8";
}
.fa-drupal:before {
content: "\f1a9";
}
.fa-joomla:before {
content: "\f1aa";
}
.fa-language:before {
content: "\f1ab";
}
.fa-fax:before {
content: "\f1ac";
}
.fa-building:before {
content: "\f1ad";
}
.fa-child:before {
content: "\f1ae";
}
.fa-paw:before {
content: "\f1b0";
}
.fa-spoon:before {
content: "\f1b1";
}
.fa-cube:before {
content: "\f1b2";
}
.fa-cubes:before {
content: "\f1b3";
}
.fa-behance:before {
content: "\f1b4";
}
.fa-behance-square:before {
content: "\f1b5";
}
.fa-steam:before {
content: "\f1b6";
}
.fa-steam-square:before {
content: "\f1b7";
}
.fa-recycle:before {
content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
content: "\f1ba";
}
.fa-tree:before {
content: "\f1bb";
}
.fa-spotify:before {
content: "\f1bc";
}
.fa-deviantart:before {
content: "\f1bd";
}
.fa-soundcloud:before {
content: "\f1be";
}
.fa-database:before {
content: "\f1c0";
}
.fa-file-pdf-o:before {
content: "\f1c1";
}
.fa-file-word-o:before {
content: "\f1c2";
}
.fa-file-excel-o:before {
content: "\f1c3";
}
.fa-file-powerpoint-o:before {
content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
content: "\f1c8";
}
.fa-file-code-o:before {
content: "\f1c9";
}
.fa-vine:before {
content: "\f1ca";
}
.fa-codepen:before {
content: "\f1cb";
}
.fa-jsfiddle:before {
content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
content: "\f1cd";
}
.fa-circle-o-notch:before {
content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
content: "\f1d1";
}
.fa-git-square:before {
content: "\f1d2";
}
.fa-git:before {
content: "\f1d3";
}
.fa-hacker-news:before {
content: "\f1d4";
}
.fa-tencent-weibo:before {
content: "\f1d5";
}
.fa-qq:before {
content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
content: "\f1d9";
}
.fa-history:before {
content: "\f1da";
}
.fa-circle-thin:before {
content: "\f1db";
}
.fa-header:before {
content: "\f1dc";
}
.fa-paragraph:before {
content: "\f1dd";
}
.fa-sliders:before {
content: "\f1de";
}
.fa-share-alt:before {
content: "\f1e0";
}
.fa-share-alt-square:before {
content: "\f1e1";
}
.fa-bomb:before {
content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
content: "\f1e3";
}
.fa-tty:before {
content: "\f1e4";
}
.fa-binoculars:before {
content: "\f1e5";
}
.fa-plug:before {
content: "\f1e6";
}
.fa-slideshare:before {
content: "\f1e7";
}
.fa-twitch:before {
content: "\f1e8";
}
.fa-yelp:before {
content: "\f1e9";
}
.fa-newspaper-o:before {
content: "\f1ea";
}
.fa-wifi:before {
content: "\f1eb";
}
.fa-calculator:before {
content: "\f1ec";
}
.fa-paypal:before {
content: "\f1ed";
}
.fa-google-wallet:before {
content: "\f1ee";
}
.fa-cc-visa:before {
content: "\f1f0";
}
.fa-cc-mastercard:before {
content: "\f1f1";
}
.fa-cc-discover:before {
content: "\f1f2";
}
.fa-cc-amex:before {
content: "\f1f3";
}
.fa-cc-paypal:before {
content: "\f1f4";
}
.fa-cc-stripe:before {
content: "\f1f5";
}
.fa-bell-slash:before {
content: "\f1f6";
}
.fa-bell-slash-o:before {
content: "\f1f7";
}
.fa-trash:before {
content: "\f1f8";
}
.fa-copyright:before {
content: "\f1f9";
}
.fa-at:before {
content: "\f1fa";
}
.fa-eyedropper:before {
content: "\f1fb";
}
.fa-paint-brush:before {
content: "\f1fc";
}
.fa-birthday-cake:before {
content: "\f1fd";
}
.fa-area-chart:before {
content: "\f1fe";
}
.fa-pie-chart:before {
content: "\f200";
}
.fa-line-chart:before {
content: "\f201";
}
.fa-lastfm:before {
content: "\f202";
}
.fa-lastfm-square:before {
content: "\f203";
}
.fa-toggle-off:before {
content: "\f204";
}
.fa-toggle-on:before {
content: "\f205";
}
.fa-bicycle:before {
content: "\f206";
}
.fa-bus:before {
content: "\f207";
}
.fa-ioxhost:before {
content: "\f208";
}
.fa-angellist:before {
content: "\f209";
}
.fa-cc:before {
content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
content: "\f20b";
}
.fa-meanpath:before {
content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
-webkit-transform: translate(0, 0);
-ms-transform: translate(0, 0);
-o-transform: translate(0, 0);
transform: translate(0, 0);
}
code {
color: #000;
}
pre {
font-size: inherit;
line-height: inherit;
}
label {
font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
.corner-all {
border-radius: 2px;
}
.no-padding {
padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer. It allows the usage of flexible box
model layouts accross multiple browsers, including older browsers. The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below). Browsers that are known to implement this
new spec completely include:
Firefox 28.0+
Chrome 29.0+
Internet Explorer 11+
Opera 17.0+
Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
.hbox > * {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
}
.vbox {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
.vbox > * {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
/* Old browsers */
-webkit-box-direction: reverse;
-moz-box-direction: reverse;
box-direction: reverse;
/* Modern browsers */
flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
/* Old browsers */
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
/* Old browsers */
-webkit-box-flex: 2;
-moz-box-flex: 2;
box-flex: 2;
/* Modern browsers */
flex: 2;
}
.box-group1 {
/* Deprecated */
-webkit-box-flex-group: 1;
-moz-box-flex-group: 1;
box-flex-group: 1;
}
.box-group2 {
/* Deprecated */
-webkit-box-flex-group: 2;
-moz-box-flex-group: 2;
box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
/* Old browsers */
-webkit-box-pack: start;
-moz-box-pack: start;
box-pack: start;
/* Modern browsers */
justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
/* Old browsers */
-webkit-box-pack: end;
-moz-box-pack: end;
box-pack: end;
/* Modern browsers */
justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
/* Old browsers */
-webkit-box-pack: center;
-moz-box-pack: center;
box-pack: center;
/* Modern browsers */
justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
/* Old browsers */
-webkit-box-pack: baseline;
-moz-box-pack: baseline;
box-pack: baseline;
/* Modern browsers */
justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
/* Old browsers */
-webkit-box-pack: stretch;
-moz-box-pack: stretch;
box-pack: stretch;
/* Modern browsers */
justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
/* Old browsers */
-webkit-box-align: start;
-moz-box-align: start;
box-align: start;
/* Modern browsers */
align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
/* Old browsers */
-webkit-box-align: end;
-moz-box-align: end;
box-align: end;
/* Modern browsers */
align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
/* Old browsers */
-webkit-box-align: center;
-moz-box-align: center;
box-align: center;
/* Modern browsers */
align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
/* Old browsers */
-webkit-box-align: baseline;
-moz-box-align: baseline;
box-align: baseline;
/* Modern browsers */
align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
/* Old browsers */
-webkit-box-align: stretch;
-moz-box-align: stretch;
box-align: stretch;
/* Modern browsers */
align-items: stretch;
}
div.error {
margin: 2em;
text-align: center;
}
div.error > h1 {
font-size: 500%;
line-height: normal;
}
div.error > p {
font-size: 200%;
line-height: normal;
}
div.traceback-wrapper {
text-align: left;
max-width: 800px;
margin: auto;
}
/**
* Primary styles
*
* Author: Jupyter Development Team
*/
body {
background-color: #fff;
/* This makes sure that the body covers the entire window and needs to
be in a different element than the display: box in wrapper below */
position: absolute;
left: 0px;
right: 0px;
top: 0px;
bottom: 0px;
overflow: visible;
}
body > #header {
/* Initially hidden to prevent FLOUC */
display: none;
background-color: #fff;
/* Display over codemirror */
position: relative;
z-index: 100;
}
body > #header #header-container {
padding-bottom: 5px;
padding-top: 5px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
body > #header .header-bar {
width: 100%;
height: 1px;
background: #e7e7e7;
margin-bottom: -1px;
}
@media print {
body > #header {
display: none !important;
}
}
#header-spacer {
width: 100%;
visibility: hidden;
}
@media print {
#header-spacer {
display: none;
}
}
#ipython_notebook {
padding-left: 0px;
padding-top: 1px;
padding-bottom: 1px;
}
@media (max-width: 991px) {
#ipython_notebook {
margin-left: 10px;
}
}
[dir="rtl"] #ipython_notebook {
float: right !important;
}
#noscript {
width: auto;
padding-top: 16px;
padding-bottom: 16px;
text-align: center;
font-size: 22px;
color: red;
font-weight: bold;
}
#ipython_notebook img {
height: 28px;
}
#site {
width: 100%;
display: none;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
overflow: auto;
}
@media print {
#site {
height: auto !important;
}
}
/* Smaller buttons */
.ui-button .ui-button-text {
padding: 0.2em 0.8em;
font-size: 77%;
}
input.ui-button {
padding: 0.3em 0.9em;
}
span#login_widget {
float: right;
}
span#login_widget > .button,
#logout {
color: #333;
background-color: #fff;
border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
background-color: #fff;
border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
color: #fff;
background-color: #333;
}
.nav-header {
text-transform: none;
}
#header > span {
margin-top: 10px;
}
.modal_stretch .modal-dialog {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
max-height: calc(100vh - 200px);
overflow: auto;
flex: 1;
}
@media (min-width: 768px) {
.modal .modal-dialog {
width: 700px;
}
}
@media (min-width: 768px) {
select.form-control {
margin-left: 12px;
margin-right: 12px;
}
}
/*!
*
* IPython auth
*
*/
.center-nav {
display: inline-block;
margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
background-color: none;
display: inline;
}
.alternate_upload.form {
padding: 0;
margin: 0;
}
.alternate_upload input.fileinput {
text-align: center;
vertical-align: middle;
display: inline;
opacity: 0;
z-index: 2;
width: 12ex;
margin-right: -12ex;
}
.alternate_upload .btn-upload {
height: 22px;
}
/**
* Primary styles
*
* Author: Jupyter Development Team
*/
[dir="rtl"] #tabs li {
float: right;
}
ul#tabs {
margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
margin-right: 0px;
}
ul#tabs a {
padding-top: 6px;
padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
text-decoration: none;
}
ul.breadcrumb i.icon-home {
font-size: 16px;
margin-right: 4px;
}
ul.breadcrumb span {
color: #5e5e5e;
}
.list_toolbar {
padding: 4px 0 4px 0;
vertical-align: middle;
}
.list_toolbar .tree-buttons {
padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
padding-top: 1px;
float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
float: right !important;
}
.dynamic-buttons {
padding-top: 3px;
display: inline-block;
}
.list_toolbar [class*="span"] {
min-height: 24px;
}
.list_header {
font-weight: bold;
background-color: #EEE;
}
.list_placeholder {
font-weight: bold;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
}
.list_container {
margin-top: 4px;
margin-bottom: 20px;
border: 1px solid #ddd;
border-radius: 2px;
}
.list_container > div {
border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
background-color: red;
}
.list_container > div:last-child {
border: none;
}
.list_item:hover .list_item {
background-color: #ddd;
}
.list_item a {
text-decoration: none;
}
.list_item:hover {
background-color: #fafafa;
}
.list_header > div,
.list_item > div {
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
line-height: 22px;
}
.list_header > div input,
.list_item > div input {
margin-right: 7px;
margin-left: 14px;
vertical-align: baseline;
line-height: 22px;
position: relative;
top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
margin-left: -1px;
vertical-align: baseline;
line-height: 22px;
}
.new-file input[type=checkbox] {
visibility: hidden;
}
.item_name {
line-height: 22px;
height: 24px;
}
.item_icon {
font-size: 14px;
color: #5e5e5e;
margin-right: 7px;
margin-left: 7px;
line-height: 22px;
vertical-align: baseline;
}
.item_buttons {
line-height: 1em;
margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
margin-left: 5px;
}
.item_buttons .btn {
min-width: 13ex;
}
.item_buttons .running-indicator {
padding-top: 4px;
color: #5cb85c;
}
.item_buttons .kernel-name {
padding-top: 4px;
color: #5bc0de;
margin-right: 7px;
float: left;
}
.toolbar_info {
height: 24px;
line-height: 24px;
}
.list_item input:not([type=checkbox]) {
padding-top: 3px;
padding-bottom: 3px;
height: 22px;
line-height: 14px;
margin: 0px;
}
.highlight_text {
color: blue;
}
#project_name {
display: inline-block;
padding-left: 7px;
margin-left: -2px;
}
#project_name > .breadcrumb {
padding: 0px;
margin-bottom: 0px;
background-color: transparent;
font-weight: bold;
}
#tree-selector {
padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
float: right;
}
#button-select-all {
min-width: 50px;
}
#select-all {
margin-left: 7px;
margin-right: 2px;
}
.menu_icon {
margin-right: 2px;
}
.tab-content .row {
margin-left: 0px;
margin-right: 0px;
}
.folder_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f114";
}
.folder_icon:before.pull-left {
margin-right: .3em;
}
.folder_icon:before.pull-right {
margin-left: .3em;
}
.notebook_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f02d";
position: relative;
top: -1px;
}
.notebook_icon:before.pull-left {
margin-right: .3em;
}
.notebook_icon:before.pull-right {
margin-left: .3em;
}
.running_notebook_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f02d";
position: relative;
top: -1px;
color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
margin-left: .3em;
}
.file_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f016";
position: relative;
top: -2px;
}
.file_icon:before.pull-left {
margin-right: .3em;
}
.file_icon:before.pull-right {
margin-left: .3em;
}
#notebook_toolbar .pull-right {
padding-top: 0px;
margin-right: -1px;
}
ul#new-menu {
left: auto;
right: 0;
}
[dir="rtl"] #new-menu {
text-align: right;
}
.kernel-menu-icon {
padding-right: 12px;
width: 24px;
content: "\f096";
}
.kernel-menu-icon:before {
content: "\f096";
}
.kernel-menu-icon-current:before {
content: "\f00c";
}
#tab_content {
padding-top: 20px;
}
#running .panel-group .panel {
margin-top: 3px;
margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
background-color: #EEE;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
text-decoration: none;
}
#running .panel-group .panel .panel-body {
padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
margin-top: 0px;
margin-bottom: 0px;
border: 0px;
border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
float: right !important;
}
.delete-button {
display: none;
}
.duplicate-button {
display: none;
}
.rename-button {
display: none;
}
.shutdown-button {
display: none;
}
.dynamic-instructions {
display: inline-block;
padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
padding: 0px 5px;
}
.selected-keymap i.fa:before {
content: "\f00c";
}
#mode-menu {
overflow: auto;
max-height: 20em;
}
.edit_app #header {
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
/* Use a negative 1 bottom margin, so the border overlaps the border of the
header */
margin-bottom: -1px;
}
.dirty-indicator {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator.pull-left {
margin-right: .3em;
}
.dirty-indicator.pull-right {
margin-left: .3em;
}
.dirty-indicator-dirty {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator-dirty.pull-left {
margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
margin-left: .3em;
}
.dirty-indicator-clean {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator-clean.pull-left {
margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
margin-left: .3em;
}
.dirty-indicator-clean:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
margin-left: .3em;
}
#filename {
font-size: 16pt;
display: table;
padding: 0px 5px;
}
#current-mode {
padding-left: 5px;
padding-right: 5px;
}
#texteditor-backdrop {
padding-top: 20px;
padding-bottom: 20px;
}
@media not print {
#texteditor-backdrop {
background-color: #EEE;
}
}
@media print {
#texteditor-backdrop #texteditor-container .CodeMirror-gutter,
#texteditor-backdrop #texteditor-container .CodeMirror-gutters {
background-color: #fff;
}
}
@media not print {
#texteditor-backdrop #texteditor-container .CodeMirror-gutter,
#texteditor-backdrop #texteditor-container .CodeMirror-gutters {
background-color: #fff;
}
}
@media not print {
#texteditor-backdrop #texteditor-container {
padding: 0px;
background-color: #fff;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
color: black;
}
.ansired {
color: darkred;
}
.ansigreen {
color: darkgreen;
}
.ansiyellow {
color: #c4a000;
}
.ansiblue {
color: darkblue;
}
.ansipurple {
color: darkviolet;
}
.ansicyan {
color: steelblue;
}
.ansigray {
color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
background-color: black;
}
.ansibgred {
background-color: red;
}
.ansibggreen {
background-color: green;
}
.ansibgyellow {
background-color: yellow;
}
.ansibgblue {
background-color: blue;
}
.ansibgpurple {
background-color: magenta;
}
.ansibgcyan {
background-color: cyan;
}
.ansibggray {
background-color: gray;
}
div.cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
border-radius: 2px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
border-width: 1px;
border-style: solid;
border-color: transparent;
width: 100%;
padding: 5px;
/* This acts as a spacer between cells, that is outside the border */
margin: 0px;
outline: none;
border-left-width: 1px;
padding-left: 5px;
background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
border-left-color: #90CAF9;
border-left-color: #E3F2FD;
border-left-width: 1px;
padding-left: 5px;
border-right-color: #E3F2FD;
border-right-width: 1px;
background: #E3F2FD;
}
@media print {
div.cell.jupyter-soft-selected {
border-color: transparent;
}
}
div.cell.selected {
border-color: #ababab;
border-left-width: 0px;
padding-left: 6px;
background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
div.cell.selected {
border-color: transparent;
}
}
div.cell.selected.jupyter-soft-selected {
border-left-width: 0;
padding-left: 6px;
background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
border-color: #66BB6A;
border-left-width: 0px;
padding-left: 6px;
background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
.edit_mode div.cell.selected {
border-color: transparent;
}
}
.prompt {
/* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
min-width: 14ex;
/* This padding is tuned to match the padding on the CodeMirror editor. */
padding: 0.4em;
margin: 0px;
font-family: monospace;
text-align: right;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
/* Don't highlight prompt number selection */
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
/* Use default cursor */
cursor: default;
}
@media (max-width: 540px) {
.prompt {
text-align: left;
}
}
div.inner_cell {
min-width: 0;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
border: 1px solid #cfcfcf;
border-radius: 2px;
background: #f7f7f7;
line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
is no content in the output_subarea and the prompt. The main purpose of this is
to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
padding-top: 0;
padding-bottom: 0;
}
div.unrecognized_cell {
padding: 5px 5px 5px 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.unrecognized_cell .inner_cell {
border-radius: 2px;
padding: 5px;
font-weight: bold;
color: red;
border: 1px solid #cfcfcf;
background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
color: inherit;
text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
color: inherit;
text-decoration: none;
}
@media (max-width: 540px) {
div.unrecognized_cell > div.prompt {
display: none;
}
}
div.code_cell {
/* avoid page breaking on code cells when printing */
}
@media print {
div.code_cell {
page-break-inside: avoid;
}
}
/* any special styling for code cells that are currently running goes here */
div.input {
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.input {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
color: #303F9F;
border-top: 1px solid transparent;
}
div.input_area > div.highlight {
margin: 0.4em;
border: none;
padding: 0px;
background-color: transparent;
}
div.input_area > div.highlight > pre {
margin: 0px;
border: none;
padding: 0px;
background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
* monospace font with inconsistent normal/bold/italic height. See
* notebookmain.js. Such fonts will have keywords vertically offset with
* respect to the rest of the text. The user should select a better font.
* See: https://github.com/ipython/ipython/issues/1503
*
* .CodeMirror span {
* vertical-align: bottom;
* }
*/
.CodeMirror {
line-height: 1.21429em;
/* Changed from 1em to our global default */
font-size: 14px;
height: auto;
/* Changed to auto to autogrow */
background: none;
/* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
/* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
/* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
overflow-y: hidden;
overflow-x: auto;
}
.CodeMirror-lines {
/* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
/* we have set a different line-height and want this to scale with that. */
padding: 0.4em;
}
.CodeMirror-linenumber {
padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
border-bottom-left-radius: 2px;
border-top-left-radius: 2px;
}
.CodeMirror pre {
/* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
/* .CodeMirror-lines */
padding: 0;
border: 0;
border-radius: 0;
}
/*
Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme
*/
.highlight-base {
color: #000;
}
.highlight-variable {
color: #000;
}
.highlight-variable-2 {
color: #1a1a1a;
}
.highlight-variable-3 {
color: #333333;
}
.highlight-string {
color: #BA2121;
}
.highlight-comment {
color: #408080;
font-style: italic;
}
.highlight-number {
color: #080;
}
.highlight-atom {
color: #88F;
}
.highlight-keyword {
color: #008000;
font-weight: bold;
}
.highlight-builtin {
color: #008000;
}
.highlight-error {
color: #f00;
}
.highlight-operator {
color: #AA22FF;
font-weight: bold;
}
.highlight-meta {
color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
color: #00f;
}
.highlight-string-2 {
color: #f50;
}
.highlight-qualifier {
color: #555;
}
.highlight-bracket {
color: #997;
}
.highlight-tag {
color: #170;
}
.highlight-attribute {
color: #00c;
}
.highlight-header {
color: blue;
}
.highlight-quote {
color: #090;
}
.highlight-link {
color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
color: #008000;
font-weight: bold;
}
.cm-s-ipython span.cm-atom {
color: #88F;
}
.cm-s-ipython span.cm-number {
color: #080;
}
.cm-s-ipython span.cm-def {
color: #00f;
}
.cm-s-ipython span.cm-variable {
color: #000;
}
.cm-s-ipython span.cm-operator {
color: #AA22FF;
font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
color: #333333;
}
.cm-s-ipython span.cm-comment {
color: #408080;
font-style: italic;
}
.cm-s-ipython span.cm-string {
color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
color: #f50;
}
.cm-s-ipython span.cm-meta {
color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
color: #555;
}
.cm-s-ipython span.cm-builtin {
color: #008000;
}
.cm-s-ipython span.cm-bracket {
color: #997;
}
.cm-s-ipython span.cm-tag {
color: #170;
}
.cm-s-ipython span.cm-attribute {
color: #00c;
}
.cm-s-ipython span.cm-header {
color: blue;
}
.cm-s-ipython span.cm-quote {
color: #090;
}
.cm-s-ipython span.cm-link {
color: #00c;
}
.cm-s-ipython span.cm-error {
color: #f00;
}
.cm-s-ipython span.cm-tab {
background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
background-position: right;
background-repeat: no-repeat;
}
div.output_wrapper {
/* this position must be relative to enable descendents to be absolute within it */
position: relative;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
/* ideally, this would be max-height, but FF barfs all over that */
height: 24em;
/* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
width: 100%;
overflow: auto;
border-radius: 2px;
-webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
margin: 0px;
padding: 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
div.out_prompt_overlay {
height: 100%;
padding: 0px 0.4em;
position: absolute;
border-radius: 2px;
}
div.out_prompt_overlay:hover {
/* use inner shadow to get border that is computed the same on WebKit/FF */
-webkit-box-shadow: inset 0 0 1px #000;
box-shadow: inset 0 0 1px #000;
background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
padding: 0px;
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.output_area .MathJax_Display {
text-align: left !important;
}
div.output_area .rendered_html table {
margin-left: 0;
margin-right: 0;
}
div.output_area .rendered_html img {
margin-left: 0;
margin-right: 0;
}
div.output_area img,
div.output_area svg {
max-width: 100%;
height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
max-width: none;
}
/* This is needed to protect the pre formating from global settings such
as that of bootstrap */
.output {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
@media (max-width: 540px) {
div.output_area {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
div.output_area pre {
margin: 0;
padding: 0;
border: 0;
vertical-align: baseline;
color: black;
background-color: transparent;
border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
the prompt div. */
div.output_subarea {
overflow-x: auto;
padding: 0.4em;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
output types */
/* all text output has this class: */
div.output_text {
text-align: left;
color: #000;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
background: #fdd;
/* very light red background for stderr */
}
div.output_latex {
text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
padding: 0;
}
.js-error {
color: darkred;
}
/* raw_input styles */
div.raw_input_container {
line-height: 1.21429em;
padding-top: 5px;
}
pre.raw_input_prompt {
/* nothing needed here. */
}
input.raw_input {
font-family: monospace;
font-size: inherit;
color: inherit;
width: auto;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0em 0.25em;
margin: 0em 0.25em;
}
input.raw_input:focus {
box-shadow: none;
}
p.p-space {
margin-bottom: 10px;
}
div.output_unrecognized {
padding: 5px;
font-weight: bold;
color: red;
}
div.output_unrecognized a {
color: inherit;
text-decoration: none;
}
div.output_unrecognized a:hover {
color: inherit;
text-decoration: none;
}
.rendered_html {
color: #000;
/* any extras will just be numbers: */
}
.rendered_html em {
font-style: italic;
}
.rendered_html strong {
font-weight: bold;
}
.rendered_html u {
text-decoration: underline;
}
.rendered_html :link {
text-decoration: underline;
}
.rendered_html :visited {
text-decoration: underline;
}
.rendered_html h1 {
font-size: 185.7%;
margin: 1.08em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h2 {
font-size: 157.1%;
margin: 1.27em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h3 {
font-size: 128.6%;
margin: 1.55em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h4 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h5 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
font-style: italic;
}
.rendered_html h6 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
font-style: italic;
}
.rendered_html h1:first-child {
margin-top: 0.538em;
}
.rendered_html h2:first-child {
margin-top: 0.636em;
}
.rendered_html h3:first-child {
margin-top: 0.777em;
}
.rendered_html h4:first-child {
margin-top: 1em;
}
.rendered_html h5:first-child {
margin-top: 1em;
}
.rendered_html h6:first-child {
margin-top: 1em;
}
.rendered_html ul {
list-style: disc;
margin: 0em 2em;
padding-left: 0px;
}
.rendered_html ul ul {
list-style: square;
margin: 0em 2em;
}
.rendered_html ul ul ul {
list-style: circle;
margin: 0em 2em;
}
.rendered_html ol {
list-style: decimal;
margin: 0em 2em;
padding-left: 0px;
}
.rendered_html ol ol {
list-style: upper-alpha;
margin: 0em 2em;
}
.rendered_html ol ol ol {
list-style: lower-alpha;
margin: 0em 2em;
}
.rendered_html ol ol ol ol {
list-style: lower-roman;
margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
list-style: decimal;
margin: 0em 2em;
}
.rendered_html * + ul {
margin-top: 1em;
}
.rendered_html * + ol {
margin-top: 1em;
}
.rendered_html hr {
color: black;
background-color: black;
}
.rendered_html pre {
margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
border: 0;
background-color: #fff;
color: #000;
font-size: 100%;
padding: 0px;
}
.rendered_html blockquote {
margin: 1em 2em;
}
.rendered_html table {
margin-left: auto;
margin-right: auto;
border: 1px solid black;
border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
border: 1px solid black;
border-collapse: collapse;
margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
text-align: left;
vertical-align: middle;
padding: 4px;
}
.rendered_html th {
font-weight: bold;
}
.rendered_html * + table {
margin-top: 1em;
}
.rendered_html p {
text-align: left;
}
.rendered_html * + p {
margin-top: 1em;
}
.rendered_html img {
display: block;
margin-left: auto;
margin-right: auto;
}
.rendered_html * + img {
margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
max-width: 100%;
height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
max-width: none;
}
div.text_cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.text_cell > div.prompt {
display: none;
}
}
div.text_cell_render {
/*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
outline: none;
resize: none;
width: inherit;
border-style: none;
padding: 0.5em 0.5em 0.5em 0.4em;
color: #000;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
a.anchor-link:link {
text-decoration: none;
padding: 0px 20px;
visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
visibility: visible;
}
.text_cell.rendered .input_area {
display: none;
}
.text_cell.rendered .rendered_html {
overflow-x: auto;
overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
font-weight: bold;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
font-size: 185.7%;
}
.cm-header-2 {
font-size: 157.1%;
}
.cm-header-3 {
font-size: 128.6%;
}
.cm-header-4 {
font-size: 110%;
}
.cm-header-5 {
font-size: 100%;
font-style: italic;
}
.cm-header-6 {
font-size: 100%;
font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
.notebook_app {
padding-left: 0px;
padding-right: 0px;
}
}
#ipython-main-app {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
height: 100%;
}
div#notebook_panel {
margin: 0px;
padding: 0px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
height: 100%;
}
div#notebook {
font-size: 14px;
line-height: 20px;
overflow-y: hidden;
overflow-x: auto;
width: 100%;
/* This spaces the page away from the edge of the notebook area */
padding-top: 20px;
margin: 0px;
outline: none;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
min-height: 100%;
}
@media not print {
#notebook-container {
padding: 15px;
background-color: #fff;
min-height: 0;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
}
@media print {
#notebook-container {
width: 100%;
}
}
div.ui-widget-content {
border: 1px solid #ababab;
outline: none;
}
pre.dialog {
background-color: #f7f7f7;
border: 1px solid #ddd;
border-radius: 2px;
padding: 0.4em;
padding-left: 2em;
}
p.dialog {
padding: 0.2em;
}
/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems
to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do.
*/
pre,
code,
kbd,
samp {
white-space: pre-wrap;
}
#fonttest {
font-family: monospace;
}
p {
margin-bottom: 0;
}
.end_space {
min-height: 100px;
transition: height .2s ease;
}
.notebook_app > #header {
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
.notebook_app {
background-color: #EEE;
}
}
kbd {
border-style: solid;
border-width: 1px;
box-shadow: none;
margin: 2px;
padding-left: 2px;
padding-right: 2px;
padding-top: 1px;
padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
border: thin solid #CFCFCF;
border-bottom: none;
background: #EEE;
border-radius: 2px 2px 0px 0px;
width: 100%;
height: 29px;
padding-right: 4px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
/* Old browsers */
-webkit-box-pack: end;
-moz-box-pack: end;
box-pack: end;
/* Modern browsers */
justify-content: flex-end;
display: -webkit-flex;
}
@media print {
.celltoolbar {
display: none;
}
}
.ctb_hideshow {
display: none;
vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
border-top-right-radius: 0px;
border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
border: 1px solid #cfcfcf;
}
.celltoolbar {
font-size: 87%;
padding-top: 3px;
}
.celltoolbar select {
display: block;
width: 100%;
height: 32px;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
background-color: #fff;
background-image: none;
border: 1px solid #ccc;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
width: inherit;
font-size: inherit;
height: 22px;
padding: 0px;
display: inline-block;
}
.celltoolbar select:focus {
border-color: #66afe9;
outline: 0;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
color: #999;
opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
color: #999;
}
.celltoolbar select::-ms-expand {
border: 0;
background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
background-color: #eeeeee;
opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
cursor: not-allowed;
}
textarea.celltoolbar select {
height: auto;
}
select.celltoolbar select {
height: 30px;
line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
height: auto;
}
.celltoolbar label {
margin-left: 5px;
margin-right: 5px;
}
.completions {
position: absolute;
z-index: 110;
overflow: hidden;
border: 1px solid #ababab;
border-radius: 2px;
-webkit-box-shadow: 0px 6px 10px -1px #adadad;
box-shadow: 0px 6px 10px -1px #adadad;
line-height: 1;
}
.completions select {
background: white;
outline: none;
border: none;
padding: 0px;
margin: 0px;
overflow: auto;
font-family: monospace;
font-size: 110%;
color: #000;
width: auto;
}
.completions select option.context {
color: #286090;
}
#kernel_logo_widget {
float: right !important;
float: right;
}
#kernel_logo_widget .current_kernel_logo {
display: none;
margin-top: -1px;
margin-bottom: -1px;
width: 32px;
height: 32px;
}
#menubar {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
margin-top: 1px;
}
#menubar .navbar {
border-top: 1px;
border-radius: 0px 0px 2px 2px;
margin-bottom: 0px;
}
#menubar .navbar-toggle {
float: left;
padding-top: 7px;
padding-bottom: 7px;
border: none;
}
#menubar .navbar-collapse {
clear: left;
}
.nav-wrapper {
border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
padding-top: 4px;
}
ul#help_menu li a {
overflow: hidden;
padding-right: 2.2em;
}
ul#help_menu li a i {
margin-right: -1.2em;
}
.dropdown-submenu {
position: relative;
}
.dropdown-submenu > .dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
display: block;
}
.dropdown-submenu > a:after {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
display: block;
content: "\f0da";
float: right;
color: #333333;
margin-top: 2px;
margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
color: #262626;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
left: -100%;
margin-left: 10px;
}
#notification_area {
float: right !important;
float: right;
z-index: 10;
}
.indicator_area {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
}
#kernel_indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
padding-left: 5px;
padding-right: 5px;
}
#modal_indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
}
#readonly-indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
margin-top: 2px;
margin-bottom: 0px;
margin-left: 0px;
margin-right: 0px;
display: none;
}
.modal_indicator:before {
width: 1.28571429em;
text-align: center;
}
.edit_mode .modal_indicator:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
margin-left: .3em;
}
.command_mode .modal_indicator:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
margin-left: .3em;
}
.kernel_idle_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
margin-left: .3em;
}
.kernel_busy_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f111";
}
.kernel_busy_icon:before.pull-left {
margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
margin-left: .3em;
}
.kernel_dead_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
margin-left: .3em;
}
.kernel_disconnected_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
margin-left: .3em;
}
.notification_widget {
color: #777;
z-index: 10;
background: rgba(240, 240, 240, 0.5);
margin-right: 4px;
color: #333;
background-color: #fff;
border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
.notification_widget:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
background-color: #fff;
border-color: #ccc;
}
.notification_widget .badge {
color: #fff;
background-color: #333;
}
.notification_widget.warning {
color: #fff;
background-color: #f0ad4e;
border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
color: #fff;
background-color: #ec971f;
border-color: #985f0d;
}
.notification_widget.warning:hover {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
color: #fff;
background-color: #d58512;
border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
background-color: #f0ad4e;
border-color: #eea236;
}
.notification_widget.warning .badge {
color: #f0ad4e;
background-color: #fff;
}
.notification_widget.success {
color: #fff;
background-color: #5cb85c;
border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
color: #fff;
background-color: #449d44;
border-color: #255625;
}
.notification_widget.success:hover {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
color: #fff;
background-color: #398439;
border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
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* Author: Jupyter Development Team
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/* Overrides of notebook CSS for static HTML export */
body {
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<div class="prompt input_prompt">
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<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Image-Classification">Image Classification<a class="anchor-link" href="#Image-Classification">&#182;</a></h1><p>In this project, you'll classify images from the <a href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 dataset</a>. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.</p>
<h2 id="Get-the-Data">Get the Data<a class="anchor-link" href="#Get-the-Data">&#182;</a></h2><p>Run the following cell to download the <a href="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz">CIFAR-10 dataset for python</a>.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">urllib.request</span> <span class="k">import</span> <span class="n">urlretrieve</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="k">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">isdir</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
<span class="kn">import</span> <span class="nn">tarfile</span>
<span class="n">cifar10_dataset_folder_path</span> <span class="o">=</span> <span class="s1">&#39;cifar-10-batches-py&#39;</span>
<span class="k">class</span> <span class="nc">DLProgress</span><span class="p">(</span><span class="n">tqdm</span><span class="p">):</span>
<span class="n">last_block</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">total_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">block_num</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_block</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_block</span> <span class="o">=</span> <span class="n">block_num</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">isfile</span><span class="p">(</span><span class="s1">&#39;cifar-10-python.tar.gz&#39;</span><span class="p">):</span>
<span class="k">with</span> <span class="n">DLProgress</span><span class="p">(</span><span class="n">unit</span><span class="o">=</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">unit_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">miniters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;CIFAR-10 Dataset&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">pbar</span><span class="p">:</span>
<span class="n">urlretrieve</span><span class="p">(</span>
<span class="s1">&#39;https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
<span class="s1">&#39;cifar-10-python.tar.gz&#39;</span><span class="p">,</span>
<span class="n">pbar</span><span class="o">.</span><span class="n">hook</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">isdir</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">):</span>
<span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;cifar-10-python.tar.gz&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
<span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">()</span>
<span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_folder_path</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">)</span>
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<pre>All files found!
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<h2 id="Explore-the-Data">Explore the Data<a class="anchor-link" href="#Explore-the-Data">&#182;</a></h2><p>The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named <code>data_batch_1</code>, <code>data_batch_2</code>, etc.. Each batch contains the labels and images that are one of the following:</p>
<ul>
<li>airplane</li>
<li>automobile</li>
<li>bird</li>
<li>cat</li>
<li>deer</li>
<li>dog</li>
<li>frog</li>
<li>horse</li>
<li>ship</li>
<li>truck</li>
</ul>
<p>Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the <code>batch_id</code> and <code>sample_id</code>. The <code>batch_id</code> is the id for a batch (1-5). The <code>sample_id</code> is the id for a image and label pair in the batch.</p>
<p>Ask yourself "What are all possible labels?", "What is the range of values for the image data?", "Are the labels in order or random?". Answers to questions like these will help you preprocess the data and end up with better predictions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
<span class="kn">import</span> <span class="nn">helper</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># Explore the dataset</span>
<span class="n">batch_id</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">sample_id</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">helper</span><span class="o">.</span><span class="n">display_stats</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">)</span>
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Stats of batch 1:
Samples: 10000
Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
Example of Image 5:
Image - Min Value: 0 Max Value: 252
Image - Shape: (32, 32, 3)
Label - Label Id: 1 Name: automobile
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<h2 id="Implement-Preprocess-Functions">Implement Preprocess Functions<a class="anchor-link" href="#Implement-Preprocess-Functions">&#182;</a></h2><h3 id="Normalize">Normalize<a class="anchor-link" href="#Normalize">&#182;</a></h3><p>In the cell below, implement the <code>normalize</code> function to take in image data, <code>x</code>, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as <code>x</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Normalize a list of sample image data in the range of 0 to 1</span>
<span class="sd"> : x: List of image data. The image shape is (32, 32, 3)</span>
<span class="sd"> : return: Numpy array of normalize data</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="k">return</span> <span class="n">x</span><span class="o">/</span><span class="mi">255</span>
<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_normalize</span><span class="p">(</span><span class="n">normalize</span><span class="p">)</span>
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<pre>Tests Passed
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<h3 id="One-hot-encode">One-hot encode<a class="anchor-link" href="#One-hot-encode">&#182;</a></h3><p>Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the <code>one_hot_encode</code> function. The input, <code>x</code>, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to <code>one_hot_encode</code>. Make sure to save the map of encodings outside the function.</p>
<p>Hint: Don't reinvent the wheel.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">one_hot_encode</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> One hot encode a list of sample labels. Return a one-hot encoded vector for each label.</span>
<span class="sd"> : x: List of sample Labels</span>
<span class="sd"> : return: Numpy array of one-hot encoded labels</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">10</span><span class="p">))</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">label</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">labels</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_one_hot_encode</span><span class="p">(</span><span class="n">one_hot_encode</span><span class="p">)</span>
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<pre>Tests Passed
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<h3 id="Randomize-Data">Randomize Data<a class="anchor-link" href="#Randomize-Data">&#182;</a></h3><p>As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset.</p>
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<h2 id="Preprocess-all-the-data-and-save-it">Preprocess all the data and save it<a class="anchor-link" href="#Preprocess-all-the-data-and-save-it">&#182;</a></h2><p>Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># Preprocess Training, Validation, and Testing Data</span>
<span class="n">helper</span><span class="o">.</span><span class="n">preprocess_and_save_data</span><span class="p">(</span><span class="n">cifar10_dataset_folder_path</span><span class="p">,</span> <span class="n">normalize</span><span class="p">,</span> <span class="n">one_hot_encode</span><span class="p">)</span>
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<h1 id="Check-Point">Check Point<a class="anchor-link" href="#Check-Point">&#182;</a></h1><p>This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">problem_unittests</span> <span class="k">as</span> <span class="nn">tests</span>
<span class="kn">import</span> <span class="nn">helper</span>
<span class="c1"># Load the Preprocessed Validation data</span>
<span class="n">valid_features</span><span class="p">,</span> <span class="n">valid_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_validation.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
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<h2 id="Build-the-network">Build the network<a class="anchor-link" href="#Build-the-network">&#182;</a></h2><p>For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.</p>
<blockquote><p><strong>Note:</strong> If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> or <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> packages to build each layer, except the layers you build in the "Convolutional and Max Pooling Layer" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.</p>
<p>However, if you would like to get the most out of this course, try to solve all the problems <em>without</em> using anything from the TF Layers packages. You <strong>can</strong> still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the <code>conv2d</code> class, <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d">tf.layers.conv2d</a>, you would want to use the TF Neural Network version of <code>conv2d</code>, <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d">tf.nn.conv2d</a>.</p>
</blockquote>
<p>Let's begin!</p>
<h3 id="Input">Input<a class="anchor-link" href="#Input">&#182;</a></h3><p>The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions</p>
<ul>
<li>Implement <code>neural_net_image_input</code><ul>
<li>Return a <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
<li>Set the shape using <code>image_shape</code> with batch size set to <code>None</code>.</li>
<li>Name the TensorFlow placeholder "x" using the TensorFlow <code>name</code> parameter in the <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>.</li>
</ul>
</li>
<li>Implement <code>neural_net_label_input</code><ul>
<li>Return a <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a></li>
<li>Set the shape using <code>n_classes</code> with batch size set to <code>None</code>.</li>
<li>Name the TensorFlow placeholder "y" using the TensorFlow <code>name</code> parameter in the <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>.</li>
</ul>
</li>
<li>Implement <code>neural_net_keep_prob_input</code><ul>
<li>Return a <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a> for dropout keep probability.</li>
<li>Name the TensorFlow placeholder "keep_prob" using the TensorFlow <code>name</code> parameter in the <a href="https://www.tensorflow.org/api_docs/python/tf/placeholder">TF Placeholder</a>.</li>
</ul>
</li>
</ul>
<p>These names will be used at the end of the project to load your saved model.</p>
<p>Note: <code>None</code> for shapes in TensorFlow allow for a dynamic size.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="k">def</span> <span class="nf">neural_net_image_input</span><span class="p">(</span><span class="n">image_shape</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a Tensor for a bach of image input</span>
<span class="sd"> : image_shape: Shape of the images</span>
<span class="sd"> : return: Tensor for image input.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="k">return</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">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">image_shape</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">neural_net_label_input</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a Tensor for a batch of label input</span>
<span class="sd"> : n_classes: Number of classes</span>
<span class="sd"> : return: Tensor for label input.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="k">return</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">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">neural_net_keep_prob_input</span><span class="p">():</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a Tensor for keep probability</span>
<span class="sd"> : return: Tensor for keep probability.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="k">return</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">&#39;keep_prob&#39;</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_image_inputs</span><span class="p">(</span><span class="n">neural_net_image_input</span><span class="p">)</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_label_inputs</span><span class="p">(</span><span class="n">neural_net_label_input</span><span class="p">)</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_nn_keep_prob_inputs</span><span class="p">(</span><span class="n">neural_net_keep_prob_input</span><span class="p">)</span>
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<pre>Image Input Tests Passed.
Label Input Tests Passed.
Keep Prob Tests Passed.
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<h3 id="Convolution-and-Max-Pooling-Layer">Convolution and Max Pooling Layer<a class="anchor-link" href="#Convolution-and-Max-Pooling-Layer">&#182;</a></h3><p>Convolution layers have a lot of success with images. For this code cell, you should implement the function <code>conv2d_maxpool</code> to apply convolution then max pooling:</p>
<ul>
<li>Create the weight and bias using <code>conv_ksize</code>, <code>conv_num_outputs</code> and the shape of <code>x_tensor</code>.</li>
<li>Apply a convolution to <code>x_tensor</code> using weight and <code>conv_strides</code>.<ul>
<li>We recommend you use same padding, but you're welcome to use any padding.</li>
</ul>
</li>
<li>Add bias</li>
<li>Add a nonlinear activation to the convolution.</li>
<li>Apply Max Pooling using <code>pool_ksize</code> and <code>pool_strides</code>.<ul>
<li>We recommend you use same padding, but you're welcome to use any padding.</li>
</ul>
</li>
</ul>
<p><strong>Note:</strong> You <strong>can't</strong> use <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> or <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> for <strong>this</strong> layer, but you can still use TensorFlow's <a href="https://www.tensorflow.org/api_docs/python/tf/nn">Neural Network</a> package. You may still use the shortcut option for all the <strong>other</strong> layers.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">convolution</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">):</span>
<span class="c1"># Create the weight and bias using conv_ksize, conv_num_outputs and the shape of x_tensor.</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">3</span><span class="p">],</span> <span class="n">conv_num_outputs</span><span class="p">]</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span> <span class="c1"># input shape times WxHxD</span>
<span class="n">bias</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">conv_num_outputs</span><span class="p">]))</span><span class="c1"># one for each convolution layer [conv_layer_number_of_elemetns] </span>
<span class="c1"># Apply a convolution to x_tensor using weight and conv_strides.</span>
<span class="c1"># -We recommend you use same padding, but you&#39;re welcome to use any padding.</span>
<span class="n">conv_strides</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="n">conv_strides</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x_tensor</span><span class="p">,</span>
<span class="nb">filter</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span>
<span class="n">strides</span><span class="o">=</span><span class="n">conv_strides</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
<span class="c1"># Add bias</span>
<span class="n">layer</span> <span class="o">+=</span> <span class="n">bias</span>
<span class="k">return</span> <span class="n">layer</span>
<span class="k">def</span> <span class="nf">conv2d_maxpool</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Apply convolution then max pooling to x_tensor</span>
<span class="sd"> :param x_tensor: TensorFlow Tensor</span>
<span class="sd"> :param conv_num_outputs: Number of outputs for the convolutional layer</span>
<span class="sd"> :param conv_ksize: kernal size 2-D Tuple for the convolutional layer</span>
<span class="sd"> :param conv_strides: Stride 2-D Tuple for convolution</span>
<span class="sd"> :param pool_ksize: kernal size 2-D Tuple for pool</span>
<span class="sd"> :param pool_strides: Stride 2-D Tuple for pool</span>
<span class="sd"> : return: A tensor that represents convolution and max pooling of x_tensor</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">convolution</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">)</span>
<span class="c1"># Add a nonlinear activation to the convolution.</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">convolution</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">conv_num_outputs</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>
<span class="c1"># Apply Max Pooling using pool_ksize and pool_strides.</span>
<span class="c1"># -We recommend you use same padding, but you&#39;re welcome to use any padding.</span>
<span class="n">pool_ksize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="n">pool_ksize</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
<span class="n">pool_strides</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="n">pool_strides</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="n">layer</span><span class="p">,</span>
<span class="n">ksize</span><span class="o">=</span><span class="n">pool_ksize</span><span class="p">,</span>
<span class="n">strides</span><span class="o">=</span><span class="n">pool_strides</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">layer</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_con_pool</span><span class="p">(</span><span class="n">conv2d_maxpool</span><span class="p">)</span>
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<h3 id="Flatten-Layer">Flatten Layer<a class="anchor-link" href="#Flatten-Layer">&#182;</a></h3><p>Implement the <code>flatten</code> function to change the dimension of <code>x_tensor</code> from a 4-D tensor to a 2-D tensor. The output should be the shape (<em>Batch Size</em>, <em>Flattened Image Size</em>). Shortcut option: you can use classes from the <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> or <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> packages for this layer. For more of a challenge, only use other TensorFlow packages.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Flatten x_tensor to (Batch Size, Flattened Image Size)</span>
<span class="sd"> : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.</span>
<span class="sd"> : return: A tensor of size (Batch Size, Flattened Image Size).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="n">image_size</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
<span class="n">result_tuple</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">as_list</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image_size</span><span class="p">])</span>
<span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">image_size</span><span class="p">])</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_flatten</span><span class="p">(</span><span class="n">flatten</span><span class="p">)</span>
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<h3 id="Fully-Connected-Layer">Fully-Connected Layer<a class="anchor-link" href="#Fully-Connected-Layer">&#182;</a></h3><p>Implement the <code>fully_conn</code> function to apply a fully connected layer to <code>x_tensor</code> with the shape (<em>Batch Size</em>, <em>num_outputs</em>). Shortcut option: you can use classes from the <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> or <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> packages for this layer. For more of a challenge, only use other TensorFlow packages.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fully_conn</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Apply a fully connected layer to x_tensor using weight and bias</span>
<span class="sd"> : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
<span class="sd"> : num_outputs: The number of output that the new tensor should be.</span>
<span class="sd"> : return: A 2-D tensor where the second dimension is num_outputs.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="c1"># Create new weights and biases.</span>
<span class="n">num_inputs</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">([</span><span class="n">num_inputs</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
<span class="n">biases</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
<span class="c1"># Calculate the layer as the matrix multiplication of</span>
<span class="c1"># the input and weights, and then add the bias-values.</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">biases</span>
<span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_fully_conn</span><span class="p">(</span><span class="n">fully_conn</span><span class="p">)</span>
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<h3 id="Output-Layer">Output Layer<a class="anchor-link" href="#Output-Layer">&#182;</a></h3><p>Implement the <code>output</code> function to apply a fully connected layer to <code>x_tensor</code> with the shape (<em>Batch Size</em>, <em>num_outputs</em>). Shortcut option: you can use classes from the <a href="https://www.tensorflow.org/api_docs/python/tf/layers">TensorFlow Layers</a> or <a href="https://www.tensorflow.org/api_guides/python/contrib.layers">TensorFlow Layers (contrib)</a> packages for this layer. For more of a challenge, only use other TensorFlow packages.</p>
<p><strong>Note:</strong> Activation, softmax, or cross entropy should <strong>not</strong> be applied to this.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Apply a output layer to x_tensor using weight and bias</span>
<span class="sd"> : x_tensor: A 2-D tensor where the first dimension is batch size.</span>
<span class="sd"> : num_outputs: The number of output that the new tensor should be.</span>
<span class="sd"> : return: A 2-D tensor where the second dimension is num_outputs.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Implement Function</span>
<span class="n">num_inputs</span> <span class="o">=</span> <span class="n">x_tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">num_elements</span><span class="p">()</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">([</span><span class="n">num_inputs</span><span class="p">,</span> <span class="n">num_outputs</span><span class="p">],</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">0.1</span><span class="p">))</span>
<span class="n">biases</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_outputs</span><span class="p">]))</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">biases</span>
<span class="k">return</span> <span class="n">layer</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
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<h3 id="Create-Convolutional-Model">Create Convolutional Model<a class="anchor-link" href="#Create-Convolutional-Model">&#182;</a></h3><p>Implement the function <code>conv_net</code> to create a convolutional neural network model. The function takes in a batch of images, <code>x</code>, and outputs logits. Use the layers you created above to create this model:</p>
<ul>
<li>Apply 1, 2, or 3 Convolution and Max Pool layers</li>
<li>Apply a Flatten Layer</li>
<li>Apply 1, 2, or 3 Fully Connected Layers</li>
<li>Apply an Output Layer</li>
<li>Return the output</li>
<li>Apply <a href="https://www.tensorflow.org/api_docs/python/tf/nn/dropout">TensorFlow's Dropout</a> to one or more layers in the model using <code>keep_prob</code>. </li>
</ul>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a convolutional neural network model</span>
<span class="sd"> : x: Placeholder tensor that holds image data.</span>
<span class="sd"> : keep_prob: Placeholder tensor that hold dropout keep probability.</span>
<span class="sd"> : return: Tensor that represents logits</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># TODO: Apply 1, 2, or 3 Convolution and Max Pool layers</span>
<span class="n">conv_num_outputs</span><span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
<span class="n">conv_ksize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">conv_strides</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pool_ksize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">pool_strides</span> <span class="o">=</span> <span class="n">pool_ksize</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">conv_num_outputs</span><span class="o">*</span><span class="mi">2</span><span class="p">),</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">conv2d_maxpool</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">conv_num_outputs</span><span class="o">*</span><span class="mi">4</span><span class="p">),</span> <span class="n">conv_ksize</span><span class="p">,</span> <span class="n">conv_strides</span><span class="p">,</span> <span class="n">pool_ksize</span><span class="p">,</span> <span class="n">pool_strides</span><span class="p">)</span>
<span class="c1"># layer = conv2d_maxpool(layer, int(conv_num_outputs*4), conv_ksize, conv_strides, pool_ksize, pool_strides)</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
<span class="c1"># TODO: Apply a Flatten Layer</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span>
<span class="c1"># TODO: Apply 1, 2, or 3 Fully Connected Layers</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">conv_num_outputs</span><span class="p">))</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">fully_conn</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">conv_num_outputs</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
<span class="c1"># TODO: Apply an Output Layer</span>
<span class="c1"># Set this to the number of classes</span>
<span class="c1"># Function Definition from Above:</span>
<span class="n">layer</span> <span class="o">=</span> <span class="n">output</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="c1"># TODO: return output</span>
<span class="k">return</span> <span class="n">layer</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1">##############################</span>
<span class="c1">## Build the Neural Network ##</span>
<span class="c1">##############################</span>
<span class="c1"># Remove previous weights, bias, inputs, etc..</span>
<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span>
<span class="c1"># Inputs</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">neural_net_image_input</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">neural_net_label_input</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">neural_net_keep_prob_input</span><span class="p">()</span>
<span class="c1"># Model</span>
<span class="n">logits</span> <span class="o">=</span> <span class="n">conv_net</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
<span class="c1"># Name logits Tensor, so that is can be loaded from disk after training</span>
<span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;logits&#39;</span><span class="p">)</span>
<span class="c1"># Loss and Optimizer</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">))</span>
<span class="n">optimizer</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="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
<span class="c1"># Accuracy</span>
<span class="n">correct_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_pred</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">&#39;accuracy&#39;</span><span class="p">)</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_conv_net</span><span class="p">(</span><span class="n">conv_net</span><span class="p">)</span>
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<pre>Neural Network Built!
</pre>
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<h2 id="Train-the-Neural-Network">Train the Neural Network<a class="anchor-link" href="#Train-the-Neural-Network">&#182;</a></h2><h3 id="Single-Optimization">Single Optimization<a class="anchor-link" href="#Single-Optimization">&#182;</a></h3><p>Implement the function <code>train_neural_network</code> to do a single optimization. The optimization should use <code>optimizer</code> to optimize in <code>session</code> with a <code>feed_dict</code> of the following:</p>
<ul>
<li><code>x</code> for image input</li>
<li><code>y</code> for labels</li>
<li><code>keep_prob</code> for keep probability for dropout</li>
</ul>
<p>This function will be called for each batch, so <code>tf.global_variables_initializer()</code> has already been called.</p>
<p>Note: Nothing needs to be returned. This function is only optimizing the neural network.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_neural_network</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">feature_batch</span><span class="p">,</span> <span class="n">label_batch</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Optimize the session on a batch of images and labels</span>
<span class="sd"> : session: Current TensorFlow session</span>
<span class="sd"> : optimizer: TensorFlow optimizer function</span>
<span class="sd"> : keep_probability: keep probability</span>
<span class="sd"> : feature_batch: Batch of Numpy image data</span>
<span class="sd"> : label_batch: Batch of Numpy label data</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span><span class="n">feature_batch</span><span class="p">,</span>
<span class="n">y</span><span class="p">:</span><span class="n">label_batch</span><span class="p">,</span>
<span class="n">keep_prob</span><span class="p">:</span><span class="n">keep_probability</span> <span class="p">})</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">tests</span><span class="o">.</span><span class="n">test_train_nn</span><span class="p">(</span><span class="n">train_neural_network</span><span class="p">)</span>
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<pre>Tests Passed
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<h3 id="Show-Stats">Show Stats<a class="anchor-link" href="#Show-Stats">&#182;</a></h3><p>Implement the function <code>print_stats</code> to print loss and validation accuracy. Use the global variables <code>valid_features</code> and <code>valid_labels</code> to calculate validation accuracy. Use a keep probability of <code>1.0</code> to calculate the loss and validation accuracy.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># def print_stats(session, feature_batch, label_batch, cost, accuracy):</span>
<span class="k">def</span> <span class="nf">print_stats</span><span class="p">(</span><span class="n">session</span><span class="p">,</span> <span class="n">x_batch</span><span class="p">,</span> <span class="n">y_true_batch</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">):</span>
<span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">x_batch</span><span class="p">,</span>
<span class="n">y</span><span class="p">:</span> <span class="n">y_true_batch</span><span class="p">,</span>
<span class="n">keep_prob</span><span class="p">:</span><span class="mf">1.0</span><span class="p">}</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="n">feed_dict</span><span class="p">)</span>
<span class="n">feed_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">valid_features</span><span class="p">,</span>
<span class="n">y</span><span class="p">:</span> <span class="n">valid_labels</span><span class="p">,</span>
<span class="n">keep_prob</span><span class="p">:</span><span class="mf">1.0</span><span class="p">}</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="n">feed_dict</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;cost:</span><span class="si">{:&lt;21}</span><span class="s2"> | acc:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">))</span>
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<h3 id="Hyperparameters">Hyperparameters<a class="anchor-link" href="#Hyperparameters">&#182;</a></h3><p>Tune the following parameters:</p>
<ul>
<li>Set <code>epochs</code> to the number of iterations until the network stops learning or start overfitting</li>
<li>Set <code>batch_size</code> to the highest number that your machine has memory for. Most people set them to common sizes of memory:<ul>
<li>64</li>
<li>128</li>
<li>256</li>
<li>...</li>
</ul>
</li>
<li>Set <code>keep_probability</code> to the probability of keeping a node using dropout</li>
</ul>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Tune Parameters</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">200</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">keep_probability</span> <span class="o">=</span> <span class="mf">0.5</span>
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<h3 id="Train-on-a-Single-CIFAR-10-Batch">Train on a Single CIFAR-10 Batch<a class="anchor-link" href="#Train-on-a-Single-CIFAR-10-Batch">&#182;</a></h3><p>Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the Training on a Single Batch...&#39;</span><span class="p">)</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="c1"># Initializing the variables</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="c1"># Training cycle</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">batch_i</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
<span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
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<pre>Checking the Training on a Single Batch...
Epoch 1, CIFAR-10 Batch 1: </pre>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">KeyboardInterrupt</span> Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-28-0ebd1bbc35ad&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold"> 13</span> train_neural_network<span class="ansi-blue-fg">(</span>sess<span class="ansi-blue-fg">,</span> optimizer<span class="ansi-blue-fg">,</span> keep_probability<span class="ansi-blue-fg">,</span> batch_features<span class="ansi-blue-fg">,</span> batch_labels<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 14</span> print<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">&#39;Epoch {:&gt;2}, CIFAR-10 Batch {}: &#39;</span><span class="ansi-blue-fg">.</span>format<span class="ansi-blue-fg">(</span>epoch <span class="ansi-blue-fg">+</span> <span class="ansi-cyan-fg">1</span><span class="ansi-blue-fg">,</span> batch_i<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">,</span> end<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">&#39;&#39;</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">---&gt; 15</span><span class="ansi-red-fg"> </span>print_stats<span class="ansi-blue-fg">(</span>sess<span class="ansi-blue-fg">,</span> batch_features<span class="ansi-blue-fg">,</span> batch_labels<span class="ansi-blue-fg">,</span> cost<span class="ansi-blue-fg">,</span> accuracy<span class="ansi-blue-fg">)</span>
<span class="ansi-green-fg">&lt;ipython-input-26-09858e298b63&gt;</span> in <span class="ansi-cyan-fg">print_stats</span><span class="ansi-blue-fg">(session, x_batch, y_true_batch, cost, accuracy)</span>
<span class="ansi-green-intense-fg ansi-bold"> 10</span> keep_prob:1.0}
<span class="ansi-green-intense-fg ansi-bold"> 11</span>
<span class="ansi-green-fg">---&gt; 12</span><span class="ansi-red-fg"> </span>accuracy <span class="ansi-blue-fg">=</span> session<span class="ansi-blue-fg">.</span>run<span class="ansi-blue-fg">(</span>accuracy<span class="ansi-blue-fg">,</span> feed_dict<span class="ansi-blue-fg">=</span>feed_dict<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 13</span> print<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">&#34;cost:{:&lt;21} | acc:{}&#34;</span><span class="ansi-blue-fg">.</span>format<span class="ansi-blue-fg">(</span>cost<span class="ansi-blue-fg">,</span> accuracy<span class="ansi-blue-fg">)</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold"> 14</span>
<span class="ansi-green-fg">/home/carnd/anaconda3/envs/dl/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>
<span class="ansi-green-intense-fg ansi-bold"> 765</span> <span class="ansi-green-fg">try</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 766</span> result = self._run(None, fetches, feed_dict, options_ptr,
<span class="ansi-green-fg">--&gt; 767</span><span class="ansi-red-fg"> run_metadata_ptr)
</span><span class="ansi-green-intense-fg ansi-bold"> 768</span> <span class="ansi-green-fg">if</span> run_metadata<span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 769</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>
<span class="ansi-green-fg">/home/carnd/anaconda3/envs/dl/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>
<span class="ansi-green-intense-fg ansi-bold"> 963</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>
<span class="ansi-green-intense-fg ansi-bold"> 964</span> results = self._do_run(handle, final_targets, final_fetches,
<span class="ansi-green-fg">--&gt; 965</span><span class="ansi-red-fg"> feed_dict_string, options, run_metadata)
</span><span class="ansi-green-intense-fg ansi-bold"> 966</span> <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 967</span> results <span class="ansi-blue-fg">=</span> <span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">]</span>
<span class="ansi-green-fg">/home/carnd/anaconda3/envs/dl/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>
<span class="ansi-green-intense-fg ansi-bold"> 1013</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>
<span class="ansi-green-intense-fg ansi-bold"> 1014</span> return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
<span class="ansi-green-fg">-&gt; 1015</span><span class="ansi-red-fg"> target_list, options, run_metadata)
</span><span class="ansi-green-intense-fg ansi-bold"> 1016</span> <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold"> 1017</span> return self._do_call(_prun_fn, self._session, handle, feed_dict,
<span class="ansi-green-fg">/home/carnd/anaconda3/envs/dl/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>
<span class="ansi-green-intense-fg ansi-bold"> 1020</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>
<span class="ansi-green-intense-fg ansi-bold"> 1021</span> <span class="ansi-green-fg">try</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">-&gt; 1022</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>
<span class="ansi-green-intense-fg ansi-bold"> 1023</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>
<span class="ansi-green-intense-fg ansi-bold"> 1024</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>
<span class="ansi-green-fg">/home/carnd/anaconda3/envs/dl/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>
<span class="ansi-green-intense-fg ansi-bold"> 1002</span> return tf_session.TF_Run(session, options,
<span class="ansi-green-intense-fg ansi-bold"> 1003</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>
<span class="ansi-green-fg">-&gt; 1004</span><span class="ansi-red-fg"> status, run_metadata)
</span><span class="ansi-green-intense-fg ansi-bold"> 1005</span>
<span class="ansi-green-intense-fg ansi-bold"> 1006</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>
<span class="ansi-red-fg">KeyboardInterrupt</span>: </pre>
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<h3 id="Fully-Train-the-Model">Fully Train the Model<a class="anchor-link" href="#Fully-Train-the-Model">&#182;</a></h3><p>Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./image_classification&#39;</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training...&#39;</span><span class="p">)</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="c1"># Initializing the variables</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="c1"># Training cycle</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="c1"># Loop over all batches</span>
<span class="n">n_batches</span> <span class="o">=</span> <span class="mi">5</span>
<span class="k">for</span> <span class="n">batch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_batches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">for</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">load_preprocess_training_batch</span><span class="p">(</span><span class="n">batch_i</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
<span class="n">train_neural_network</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">keep_probability</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{:&gt;2}</span><span class="s1">, CIFAR-10 Batch </span><span class="si">{}</span><span class="s1">: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">batch_i</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">print_stats</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">batch_features</span><span class="p">,</span> <span class="n">batch_labels</span><span class="p">,</span> <span class="n">cost</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">)</span>
<span class="c1"># Save Model</span>
<span class="n">saver</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">Saver</span><span class="p">()</span>
<span class="n">save_path</span> <span class="o">=</span> <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
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<pre>Training...
Epoch 1, CIFAR-10 Batch 1: cost:2.3013153076171875 | acc:0.10499998927116394
Epoch 1, CIFAR-10 Batch 2: cost:2.2934391498565674 | acc:0.14879998564720154
Epoch 1, CIFAR-10 Batch 3: cost:1.9417580366134644 | acc:0.1735999882221222
Epoch 1, CIFAR-10 Batch 4: cost:2.077556610107422 | acc:0.18959999084472656
Epoch 1, CIFAR-10 Batch 5: cost:2.0504345893859863 | acc:0.19860000908374786
Epoch 2, CIFAR-10 Batch 1: cost:2.1273410320281982 | acc:0.24899999797344208
Epoch 2, CIFAR-10 Batch 2: cost:1.9855499267578125 | acc:0.25999999046325684
Epoch 2, CIFAR-10 Batch 3: cost:1.665779709815979 | acc:0.25999999046325684
Epoch 2, CIFAR-10 Batch 4: cost:1.7781481742858887 | acc:0.296999990940094
Epoch 2, CIFAR-10 Batch 5: cost:1.7231721878051758 | acc:0.3198000192642212
Epoch 3, CIFAR-10 Batch 1: cost:2.017380952835083 | acc:0.33079996705055237
Epoch 3, CIFAR-10 Batch 2: cost:1.9087944030761719 | acc:0.3283999562263489
Epoch 3, CIFAR-10 Batch 3: cost:1.5201936960220337 | acc:0.3335999846458435
Epoch 3, CIFAR-10 Batch 4: cost:1.6032397747039795 | acc:0.3725999593734741
Epoch 3, CIFAR-10 Batch 5: cost:1.590127944946289 | acc:0.4001999497413635
Epoch 4, CIFAR-10 Batch 1: cost:1.8239058256149292 | acc:0.3919999897480011
Epoch 4, CIFAR-10 Batch 2: cost:1.7834103107452393 | acc:0.4039999544620514
Epoch 4, CIFAR-10 Batch 3: cost:1.3192576169967651 | acc:0.38259997963905334
Epoch 4, CIFAR-10 Batch 4: cost:1.43719482421875 | acc:0.4139999747276306
Epoch 4, CIFAR-10 Batch 5: cost:1.4808993339538574 | acc:0.4259999394416809
Epoch 5, CIFAR-10 Batch 1: cost:1.6587121486663818 | acc:0.43279996514320374
Epoch 5, CIFAR-10 Batch 2: cost:1.6271240711212158 | acc:0.43379995226860046
Epoch 5, CIFAR-10 Batch 3: cost:1.2092795372009277 | acc:0.42399996519088745
Epoch 5, CIFAR-10 Batch 4: cost:1.3314433097839355 | acc:0.4509999752044678
Epoch 5, CIFAR-10 Batch 5: cost:1.3638802766799927 | acc:0.4551999866962433
Epoch 6, CIFAR-10 Batch 1: cost:1.5008424520492554 | acc:0.4609999656677246
Epoch 6, CIFAR-10 Batch 2: cost:1.3781614303588867 | acc:0.4529999792575836
Epoch 6, CIFAR-10 Batch 3: cost:1.1152021884918213 | acc:0.45479997992515564
Epoch 6, CIFAR-10 Batch 4: cost:1.2265385389328003 | acc:0.4633999466896057
Epoch 6, CIFAR-10 Batch 5: cost:1.3025031089782715 | acc:0.47179996967315674
Epoch 7, CIFAR-10 Batch 1: cost:1.4579485654830933 | acc:0.4593999981880188
Epoch 7, CIFAR-10 Batch 2: cost:1.2291996479034424 | acc:0.478799968957901
Epoch 7, CIFAR-10 Batch 3: cost:0.9918383359909058 | acc:0.4829999804496765
Epoch 7, CIFAR-10 Batch 4: cost:1.1092722415924072 | acc:0.48659995198249817
Epoch 7, CIFAR-10 Batch 5: cost:1.2247194051742554 | acc:0.48959994316101074
Epoch 8, CIFAR-10 Batch 1: cost:1.3482823371887207 | acc:0.482200026512146
Epoch 8, CIFAR-10 Batch 2: cost:1.1363029479980469 | acc:0.47839999198913574
Epoch 8, CIFAR-10 Batch 3: cost:0.9497741460800171 | acc:0.49139994382858276
Epoch 8, CIFAR-10 Batch 4: cost:1.021355152130127 | acc:0.4963999390602112
Epoch 8, CIFAR-10 Batch 5: cost:1.1401851177215576 | acc:0.4957999587059021
Epoch 9, CIFAR-10 Batch 1: cost:1.2159086465835571 | acc:0.4896000027656555
Epoch 9, CIFAR-10 Batch 2: cost:0.9962618350982666 | acc:0.5065999031066895
Epoch 9, CIFAR-10 Batch 3: cost:0.902256190776825 | acc:0.495199978351593
Epoch 9, CIFAR-10 Batch 4: cost:0.9556984901428223 | acc:0.5011999607086182
Epoch 9, CIFAR-10 Batch 5: cost:1.0902888774871826 | acc:0.4975999593734741
Epoch 10, CIFAR-10 Batch 1: cost:1.181735873222351 | acc:0.5031999349594116
Epoch 10, CIFAR-10 Batch 2: cost:0.9206405878067017 | acc:0.511199951171875
Epoch 10, CIFAR-10 Batch 3: cost:0.8229184746742249 | acc:0.5051999092102051
Epoch 10, CIFAR-10 Batch 4: cost:0.8798347115516663 | acc:0.5105999708175659
Epoch 10, CIFAR-10 Batch 5: cost:0.9839009642601013 | acc:0.511199951171875
Epoch 11, CIFAR-10 Batch 1: cost:1.1546474695205688 | acc:0.4813999533653259
Epoch 11, CIFAR-10 Batch 2: cost:0.821304976940155 | acc:0.515999972820282
Epoch 11, CIFAR-10 Batch 3: cost:0.752850353717804 | acc:0.5097999572753906
Epoch 11, CIFAR-10 Batch 4: cost:0.8353023529052734 | acc:0.5099999904632568
Epoch 11, CIFAR-10 Batch 5: cost:0.9178630113601685 | acc:0.5285999178886414
Epoch 12, CIFAR-10 Batch 1: cost:0.9791525602340698 | acc:0.5187999606132507
Epoch 12, CIFAR-10 Batch 2: cost:0.7723967432975769 | acc:0.5173999071121216
Epoch 12, CIFAR-10 Batch 3: cost:0.6873589158058167 | acc:0.520599901676178
Epoch 12, CIFAR-10 Batch 4: cost:0.8091958165168762 | acc:0.515999972820282
Epoch 12, CIFAR-10 Batch 5: cost:0.8204214572906494 | acc:0.5307999849319458
Epoch 13, CIFAR-10 Batch 1: cost:0.9557157754898071 | acc:0.5229999423027039
Epoch 13, CIFAR-10 Batch 2: cost:0.7126266956329346 | acc:0.5221999883651733
Epoch 13, CIFAR-10 Batch 3: cost:0.675835132598877 | acc:0.5257999300956726
Epoch 13, CIFAR-10 Batch 4: cost:0.7276022434234619 | acc:0.5229998826980591
Epoch 13, CIFAR-10 Batch 5: cost:0.7474497556686401 | acc:0.5315999388694763
Epoch 14, CIFAR-10 Batch 1: cost:0.8026830554008484 | acc:0.5359998941421509
Epoch 14, CIFAR-10 Batch 2: cost:0.7084624171257019 | acc:0.5065999627113342
Epoch 14, CIFAR-10 Batch 3: cost:0.6155060529708862 | acc:0.5387999415397644
Epoch 14, CIFAR-10 Batch 4: cost:0.66913902759552 | acc:0.5389999151229858
Epoch 14, CIFAR-10 Batch 5: cost:0.793664813041687 | acc:0.5363999605178833
Epoch 15, CIFAR-10 Batch 1: cost:0.7651166319847107 | acc:0.5401999354362488
Epoch 15, CIFAR-10 Batch 2: cost:0.5752598643302917 | acc:0.5367999076843262
Epoch 15, CIFAR-10 Batch 3: cost:0.6339616775512695 | acc:0.5357999801635742
Epoch 15, CIFAR-10 Batch 4: cost:0.6354700922966003 | acc:0.5321999192237854
Epoch 15, CIFAR-10 Batch 5: cost:0.7169175744056702 | acc:0.5461999177932739
Epoch 16, CIFAR-10 Batch 1: cost:0.7829287648200989 | acc:0.5449999570846558
Epoch 16, CIFAR-10 Batch 2: cost:0.552869439125061 | acc:0.5291999578475952
Epoch 16, CIFAR-10 Batch 3: cost:0.5496835112571716 | acc:0.5455999374389648
Epoch 16, CIFAR-10 Batch 4: cost:0.6116758584976196 | acc:0.5407999157905579
Epoch 16, CIFAR-10 Batch 5: cost:0.6849182844161987 | acc:0.5441999435424805
Epoch 17, CIFAR-10 Batch 1: cost:0.677723228931427 | acc:0.543799877166748
Epoch 17, CIFAR-10 Batch 2: cost:0.4887632727622986 | acc:0.5353999137878418
Epoch 17, CIFAR-10 Batch 3: cost:0.5094462633132935 | acc:0.5525999069213867
Epoch 17, CIFAR-10 Batch 4: cost:0.5247137546539307 | acc:0.5447999238967896
Epoch 17, CIFAR-10 Batch 5: cost:0.5891541838645935 | acc:0.5525999665260315
Epoch 18, CIFAR-10 Batch 1: cost:0.6537779569625854 | acc:0.5463999509811401
Epoch 18, CIFAR-10 Batch 2: cost:0.5222825407981873 | acc:0.5243999361991882
Epoch 18, CIFAR-10 Batch 3: cost:0.46803903579711914 | acc:0.5397999286651611
Epoch 18, CIFAR-10 Batch 4: cost:0.5340467691421509 | acc:0.5399999618530273
Epoch 18, CIFAR-10 Batch 5: cost:0.6000920534133911 | acc:0.5465999245643616
Epoch 19, CIFAR-10 Batch 1: cost:0.6254660487174988 | acc:0.5575999021530151
Epoch 19, CIFAR-10 Batch 2: cost:0.4604285955429077 | acc:0.5523999333381653
Epoch 19, CIFAR-10 Batch 3: cost:0.46425288915634155 | acc:0.5529999732971191
Epoch 19, CIFAR-10 Batch 4: cost:0.4925011396408081 | acc:0.5429999232292175
Epoch 19, CIFAR-10 Batch 5: cost:0.5056983232498169 | acc:0.5523999333381653
Epoch 20, CIFAR-10 Batch 1: cost:0.5557612180709839 | acc:0.5551998615264893
Epoch 20, CIFAR-10 Batch 2: cost:0.4703412652015686 | acc:0.5443999171257019
Epoch 20, CIFAR-10 Batch 3: cost:0.43011295795440674 | acc:0.5535999536514282
Epoch 20, CIFAR-10 Batch 4: cost:0.4499567151069641 | acc:0.5469999313354492
Epoch 20, CIFAR-10 Batch 5: cost:0.49938321113586426 | acc:0.53739994764328
Epoch 21, CIFAR-10 Batch 1: cost:0.5139492750167847 | acc:0.5612000226974487
Epoch 21, CIFAR-10 Batch 2: cost:0.44648870825767517 | acc:0.5461999773979187
Epoch 21, CIFAR-10 Batch 3: cost:0.4504193663597107 | acc:0.53739994764328
Epoch 21, CIFAR-10 Batch 4: cost:0.41757410764694214 | acc:0.5473999381065369
Epoch 21, CIFAR-10 Batch 5: cost:0.4685375690460205 | acc:0.5527999401092529
Epoch 22, CIFAR-10 Batch 1: cost:0.5034701228141785 | acc:0.5561999082565308
Epoch 22, CIFAR-10 Batch 2: cost:0.4200761318206787 | acc:0.5633999109268188
Epoch 22, CIFAR-10 Batch 3: cost:0.4278845489025116 | acc:0.5573999285697937
Epoch 22, CIFAR-10 Batch 4: cost:0.4696508049964905 | acc:0.5453999042510986
Epoch 22, CIFAR-10 Batch 5: cost:0.42764776945114136 | acc:0.5555999875068665
Epoch 23, CIFAR-10 Batch 1: cost:0.5247390270233154 | acc:0.5301999449729919
Epoch 23, CIFAR-10 Batch 2: cost:0.4356013536453247 | acc:0.5655999183654785
Epoch 23, CIFAR-10 Batch 3: cost:0.3852139413356781 | acc:0.547999918460846
Epoch 23, CIFAR-10 Batch 4: cost:0.39140403270721436 | acc:0.5587999820709229
Epoch 23, CIFAR-10 Batch 5: cost:0.3894302546977997 | acc:0.5637999773025513
Epoch 24, CIFAR-10 Batch 1: cost:0.42845824360847473 | acc:0.5539999008178711
Epoch 24, CIFAR-10 Batch 2: cost:0.3835437297821045 | acc:0.5583999156951904
Epoch 24, CIFAR-10 Batch 3: cost:0.40564480423927307 | acc:0.556999921798706
Epoch 24, CIFAR-10 Batch 4: cost:0.4014459252357483 | acc:0.5597999095916748
Epoch 24, CIFAR-10 Batch 5: cost:0.3570186495780945 | acc:0.5633999109268188
Epoch 25, CIFAR-10 Batch 1: cost:0.37854626774787903 | acc:0.572399914264679
Epoch 25, CIFAR-10 Batch 2: cost:0.3530932068824768 | acc:0.5601999163627625
Epoch 25, CIFAR-10 Batch 3: cost:0.36036181449890137 | acc:0.5565999150276184
Epoch 25, CIFAR-10 Batch 4: cost:0.3781532049179077 | acc:0.5607999563217163
Epoch 25, CIFAR-10 Batch 5: cost:0.36744585633277893 | acc:0.5599998831748962
Epoch 26, CIFAR-10 Batch 1: cost:0.36885952949523926 | acc:0.5613999366760254
Epoch 26, CIFAR-10 Batch 2: cost:0.34696173667907715 | acc:0.5633999705314636
Epoch 26, CIFAR-10 Batch 3: cost:0.3486725687980652 | acc:0.555199921131134
Epoch 26, CIFAR-10 Batch 4: cost:0.35743772983551025 | acc:0.5585999488830566
Epoch 26, CIFAR-10 Batch 5: cost:0.34816551208496094 | acc:0.5509998798370361
Epoch 27, CIFAR-10 Batch 1: cost:0.3467990458011627 | acc:0.5677999258041382
Epoch 27, CIFAR-10 Batch 2: cost:0.31971192359924316 | acc:0.5481998920440674
Epoch 27, CIFAR-10 Batch 3: cost:0.335286408662796 | acc:0.5677999258041382
Epoch 27, CIFAR-10 Batch 4: cost:0.2950162887573242 | acc:0.5639999508857727
Epoch 27, CIFAR-10 Batch 5: cost:0.31763768196105957 | acc:0.561199963092804
Epoch 28, CIFAR-10 Batch 1: cost:0.33455365896224976 | acc:0.5575999021530151
Epoch 28, CIFAR-10 Batch 2: cost:0.3029651641845703 | acc:0.5555998682975769
Epoch 28, CIFAR-10 Batch 3: cost:0.3088243901729584 | acc:0.5645999312400818
Epoch 28, CIFAR-10 Batch 4: cost:0.29511770606040955 | acc:0.5571999549865723
Epoch 28, CIFAR-10 Batch 5: cost:0.2784428596496582 | acc:0.5615999102592468
Epoch 29, CIFAR-10 Batch 1: cost:0.30616050958633423 | acc:0.5649999380111694
Epoch 29, CIFAR-10 Batch 2: cost:0.2525205612182617 | acc:0.5673999786376953
Epoch 29, CIFAR-10 Batch 3: cost:0.2804349362850189 | acc:0.5591999292373657
Epoch 29, CIFAR-10 Batch 4: cost:0.30834388732910156 | acc:0.5573999285697937
Epoch 29, CIFAR-10 Batch 5: cost:0.2660523056983948 | acc:0.5615999698638916
Epoch 30, CIFAR-10 Batch 1: cost:0.2876591086387634 | acc:0.5701999068260193
Epoch 30, CIFAR-10 Batch 2: cost:0.235983207821846 | acc:0.5639998912811279
Epoch 30, CIFAR-10 Batch 3: cost:0.26202481985092163 | acc:0.5647999048233032
Epoch 30, CIFAR-10 Batch 4: cost:0.27726536989212036 | acc:0.5489999651908875
Epoch 30, CIFAR-10 Batch 5: cost:0.2587886154651642 | acc:0.571199893951416
Epoch 31, CIFAR-10 Batch 1: cost:0.2809494435787201 | acc:0.555199921131134
Epoch 31, CIFAR-10 Batch 2: cost:0.21445539593696594 | acc:0.5743999481201172
Epoch 31, CIFAR-10 Batch 3: cost:0.21181637048721313 | acc:0.5577999353408813
Epoch 31, CIFAR-10 Batch 4: cost:0.3122013211250305 | acc:0.5565998554229736
Epoch 31, CIFAR-10 Batch 5: cost:0.23973825573921204 | acc:0.5671999454498291
Epoch 32, CIFAR-10 Batch 1: cost:0.2479555904865265 | acc:0.5611999034881592
Epoch 32, CIFAR-10 Batch 2: cost:0.2205001711845398 | acc:0.578999936580658
Epoch 32, CIFAR-10 Batch 3: cost:0.19018948078155518 | acc:0.5751999020576477
Epoch 32, CIFAR-10 Batch 4: cost:0.2313966453075409 | acc:0.5643999576568604
Epoch 32, CIFAR-10 Batch 5: cost:0.2034684121608734 | acc:0.5621999502182007
Epoch 33, CIFAR-10 Batch 1: cost:0.28143155574798584 | acc:0.5525999665260315
Epoch 33, CIFAR-10 Batch 2: cost:0.2087009698152542 | acc:0.5687999129295349
Epoch 33, CIFAR-10 Batch 3: cost:0.19301722943782806 | acc:0.5667999982833862
Epoch 33, CIFAR-10 Batch 4: cost:0.2784227132797241 | acc:0.5659999251365662
Epoch 33, CIFAR-10 Batch 5: cost:0.19279098510742188 | acc:0.5651999711990356
Epoch 34, CIFAR-10 Batch 1: cost:0.2220839262008667 | acc:0.5747998952865601
Epoch 34, CIFAR-10 Batch 2: cost:0.2366575002670288 | acc:0.5747998952865601
Epoch 34, CIFAR-10 Batch 3: cost:0.2168981432914734 | acc:0.5545998811721802
Epoch 34, CIFAR-10 Batch 4: cost:0.22295473515987396 | acc:0.5669999122619629
Epoch 34, CIFAR-10 Batch 5: cost:0.1592303365468979 | acc:0.5667999386787415
Epoch 35, CIFAR-10 Batch 1: cost:0.21385212242603302 | acc:0.5613999366760254
Epoch 35, CIFAR-10 Batch 2: cost:0.18212546408176422 | acc:0.5685999393463135
Epoch 35, CIFAR-10 Batch 3: cost:0.15708868205547333 | acc:0.5491998791694641
Epoch 35, CIFAR-10 Batch 4: cost:0.23244965076446533 | acc:0.5609999299049377
Epoch 35, CIFAR-10 Batch 5: cost:0.1893375813961029 | acc:0.5691999197006226
Epoch 36, CIFAR-10 Batch 1: cost:0.2015538364648819 | acc:0.5559999346733093
Epoch 36, CIFAR-10 Batch 2: cost:0.15944984555244446 | acc:0.5747999548912048
Epoch 36, CIFAR-10 Batch 3: cost:0.16389024257659912 | acc:0.5717998743057251
Epoch 36, CIFAR-10 Batch 4: cost:0.22999079525470734 | acc:0.5649999380111694
Epoch 36, CIFAR-10 Batch 5: cost:0.16838279366493225 | acc:0.5603999495506287
Epoch 37, CIFAR-10 Batch 1: cost:0.1914355754852295 | acc:0.5709999799728394
Epoch 37, CIFAR-10 Batch 2: cost:0.19372287392616272 | acc:0.5709999203681946
Epoch 37, CIFAR-10 Batch 3: cost:0.16093116998672485 | acc:0.5723999738693237
Epoch 37, CIFAR-10 Batch 4: cost:0.18680910766124725 | acc:0.5641999244689941
Epoch 37, CIFAR-10 Batch 5: cost:0.18359413743019104 | acc:0.5729999542236328
Epoch 38, CIFAR-10 Batch 1: cost:0.18936513364315033 | acc:0.5657999515533447
Epoch 38, CIFAR-10 Batch 2: cost:0.12814238667488098 | acc:0.5771999359130859
Epoch 38, CIFAR-10 Batch 3: cost:0.1387743055820465 | acc:0.5663999319076538
Epoch 38, CIFAR-10 Batch 4: cost:0.18614204227924347 | acc:0.5571999549865723
Epoch 38, CIFAR-10 Batch 5: cost:0.1805238425731659 | acc:0.5721999406814575
Epoch 39, CIFAR-10 Batch 1: cost:0.1743970513343811 | acc:0.5623999834060669
Epoch 39, CIFAR-10 Batch 2: cost:0.1129692941904068 | acc:0.571199893951416
Epoch 39, CIFAR-10 Batch 3: cost:0.14158935844898224 | acc:0.5685998797416687
Epoch 39, CIFAR-10 Batch 4: cost:0.15885651111602783 | acc:0.5659999251365662
Epoch 39, CIFAR-10 Batch 5: cost:0.1207718700170517 | acc:0.5663999319076538
Epoch 40, CIFAR-10 Batch 1: cost:0.2087806761264801 | acc:0.560999870300293
Epoch 40, CIFAR-10 Batch 2: cost:0.1092919111251831 | acc:0.5631999373435974
Epoch 40, CIFAR-10 Batch 3: cost:0.09937123954296112 | acc:0.568399965763092
Epoch 40, CIFAR-10 Batch 4: cost:0.1926909238100052 | acc:0.5635998845100403
Epoch 40, CIFAR-10 Batch 5: cost:0.185811847448349 | acc:0.5685999393463135
Epoch 41, CIFAR-10 Batch 1: cost:0.15460683405399323 | acc:0.5593999624252319
Epoch 41, CIFAR-10 Batch 2: cost:0.11478259414434433 | acc:0.5689998865127563
Epoch 41, CIFAR-10 Batch 3: cost:0.11967311054468155 | acc:0.5757999420166016
Epoch 41, CIFAR-10 Batch 4: cost:0.1727485954761505 | acc:0.5703998804092407
Epoch 41, CIFAR-10 Batch 5: cost:0.1479194611310959 | acc:0.5579999089241028
Epoch 42, CIFAR-10 Batch 1: cost:0.19397760927677155 | acc:0.5537999868392944
Epoch 42, CIFAR-10 Batch 2: cost:0.1109868735074997 | acc:0.5763999223709106
Epoch 42, CIFAR-10 Batch 3: cost:0.11605030298233032 | acc:0.5661998987197876
Epoch 42, CIFAR-10 Batch 4: cost:0.1730315089225769 | acc:0.5819998979568481
Epoch 42, CIFAR-10 Batch 5: cost:0.1423846185207367 | acc:0.5823999047279358
Epoch 43, CIFAR-10 Batch 1: cost:0.14751701056957245 | acc:0.5665999054908752
Epoch 43, CIFAR-10 Batch 2: cost:0.0997529998421669 | acc:0.5681999325752258
Epoch 43, CIFAR-10 Batch 3: cost:0.11322760581970215 | acc:0.5643999576568604
Epoch 43, CIFAR-10 Batch 4: cost:0.1772889643907547 | acc:0.5731999278068542
Epoch 43, CIFAR-10 Batch 5: cost:0.1304873526096344 | acc:0.5731999278068542
Epoch 44, CIFAR-10 Batch 1: cost:0.17588570713996887 | acc:0.5655999183654785
Epoch 44, CIFAR-10 Batch 2: cost:0.1142192929983139 | acc:0.5739999413490295
Epoch 44, CIFAR-10 Batch 3: cost:0.11180011928081512 | acc:0.5819998979568481
Epoch 44, CIFAR-10 Batch 4: cost:0.13017721474170685 | acc:0.568399965763092
Epoch 44, CIFAR-10 Batch 5: cost:0.12160354107618332 | acc:0.5645999312400818
Epoch 45, CIFAR-10 Batch 1: cost:0.16726583242416382 | acc:0.5679999589920044
Epoch 45, CIFAR-10 Batch 2: cost:0.09837782382965088 | acc:0.5771999359130859
Epoch 45, CIFAR-10 Batch 3: cost:0.11985199898481369 | acc:0.5651999115943909
Epoch 45, CIFAR-10 Batch 4: cost:0.127051442861557 | acc:0.5695999264717102
Epoch 45, CIFAR-10 Batch 5: cost:0.1282924860715866 | acc:0.5837999582290649
Epoch 46, CIFAR-10 Batch 1: cost:0.1364060789346695 | acc:0.5707999467849731
Epoch 46, CIFAR-10 Batch 2: cost:0.1015179306268692 | acc:0.5771998763084412
Epoch 46, CIFAR-10 Batch 3: cost:0.11106127500534058 | acc:0.5651999711990356
Epoch 46, CIFAR-10 Batch 4: cost:0.13345707952976227 | acc:0.5783998966217041
Epoch 46, CIFAR-10 Batch 5: cost:0.13149556517601013 | acc:0.5731999278068542
Epoch 47, CIFAR-10 Batch 1: cost:0.15949109196662903 | acc:0.5757999420166016
Epoch 47, CIFAR-10 Batch 2: cost:0.08402353525161743 | acc:0.5709999799728394
Epoch 47, CIFAR-10 Batch 3: cost:0.11061365902423859 | acc:0.5639999508857727
Epoch 47, CIFAR-10 Batch 4: cost:0.11236836016178131 | acc:0.5853999257087708
Epoch 47, CIFAR-10 Batch 5: cost:0.15398456156253815 | acc:0.5775998830795288
Epoch 48, CIFAR-10 Batch 1: cost:0.128215491771698 | acc:0.5745999217033386
Epoch 48, CIFAR-10 Batch 2: cost:0.09952539205551147 | acc:0.5825998783111572
Epoch 48, CIFAR-10 Batch 3: cost:0.07674924284219742 | acc:0.5685999393463135
Epoch 48, CIFAR-10 Batch 4: cost:0.14963550865650177 | acc:0.5751999020576477
Epoch 48, CIFAR-10 Batch 5: cost:0.10421529412269592 | acc:0.5779998898506165
Epoch 49, CIFAR-10 Batch 1: cost:0.1186276376247406 | acc:0.5717998743057251
Epoch 49, CIFAR-10 Batch 2: cost:0.07259970158338547 | acc:0.5763998627662659
Epoch 49, CIFAR-10 Batch 3: cost:0.08324670791625977 | acc:0.5617998838424683
Epoch 49, CIFAR-10 Batch 4: cost:0.13398799300193787 | acc:0.5597999095916748
Epoch 49, CIFAR-10 Batch 5: cost:0.12206102907657623 | acc:0.5661998987197876
Epoch 50, CIFAR-10 Batch 1: cost:0.10187442600727081 | acc:0.571199893951416
Epoch 50, CIFAR-10 Batch 2: cost:0.07646170258522034 | acc:0.56659996509552
Epoch 50, CIFAR-10 Batch 3: cost:0.07789275050163269 | acc:0.574199914932251
Epoch 50, CIFAR-10 Batch 4: cost:0.13524091243743896 | acc:0.5689999461174011
Epoch 50, CIFAR-10 Batch 5: cost:0.1125338152050972 | acc:0.5673999786376953
Epoch 51, CIFAR-10 Batch 1: cost:0.11284809559583664 | acc:0.5755999088287354
Epoch 51, CIFAR-10 Batch 2: cost:0.09134554862976074 | acc:0.5785998702049255
Epoch 51, CIFAR-10 Batch 3: cost:0.07018843293190002 | acc:0.5751999616622925
Epoch 51, CIFAR-10 Batch 4: cost:0.061725225299596786 | acc:0.5743999481201172
Epoch 51, CIFAR-10 Batch 5: cost:0.09348134696483612 | acc:0.5751999020576477
Epoch 52, CIFAR-10 Batch 1: cost:0.09672576934099197 | acc:0.5787999033927917
Epoch 52, CIFAR-10 Batch 2: cost:0.07252807170152664 | acc:0.578999936580658
Epoch 52, CIFAR-10 Batch 3: cost:0.08423647284507751 | acc:0.5715999603271484
Epoch 52, CIFAR-10 Batch 4: cost:0.09236466139554977 | acc:0.5785999298095703
Epoch 52, CIFAR-10 Batch 5: cost:0.08754651248455048 | acc:0.5719999074935913
Epoch 53, CIFAR-10 Batch 1: cost:0.11256758123636246 | acc:0.574999988079071
Epoch 53, CIFAR-10 Batch 2: cost:0.08792898058891296 | acc:0.5697999000549316
Epoch 53, CIFAR-10 Batch 3: cost:0.06853397935628891 | acc:0.5801999568939209
Epoch 53, CIFAR-10 Batch 4: cost:0.09134284406900406 | acc:0.5779999494552612
Epoch 53, CIFAR-10 Batch 5: cost:0.08791206032037735 | acc:0.5751999020576477
Epoch 54, CIFAR-10 Batch 1: cost:0.1353539675474167 | acc:0.5773999691009521
Epoch 54, CIFAR-10 Batch 2: cost:0.10359365493059158 | acc:0.5699999332427979
Epoch 54, CIFAR-10 Batch 3: cost:0.04813389480113983 | acc:0.5789998769760132
Epoch 54, CIFAR-10 Batch 4: cost:0.09574723243713379 | acc:0.5735999345779419
Epoch 54, CIFAR-10 Batch 5: cost:0.08054617047309875 | acc:0.5787999033927917
Epoch 55, CIFAR-10 Batch 1: cost:0.11770103126764297 | acc:0.5751999020576477
Epoch 55, CIFAR-10 Batch 2: cost:0.0910612940788269 | acc:0.5785998702049255
Epoch 55, CIFAR-10 Batch 3: cost:0.05680608004331589 | acc:0.5803999304771423
Epoch 55, CIFAR-10 Batch 4: cost:0.07638224959373474 | acc:0.5785999298095703
Epoch 55, CIFAR-10 Batch 5: cost:0.07135013490915298 | acc:0.5817999243736267
Epoch 56, CIFAR-10 Batch 1: cost:0.09587933123111725 | acc:0.5773999094963074
Epoch 56, CIFAR-10 Batch 2: cost:0.08470194041728973 | acc:0.5837998986244202
Epoch 56, CIFAR-10 Batch 3: cost:0.05206315964460373 | acc:0.5827999114990234
Epoch 56, CIFAR-10 Batch 4: cost:0.06333940476179123 | acc:0.5837999582290649
Epoch 56, CIFAR-10 Batch 5: cost:0.07255814224481583 | acc:0.5695999264717102
Epoch 57, CIFAR-10 Batch 1: cost:0.09125135093927383 | acc:0.5785999298095703
Epoch 57, CIFAR-10 Batch 2: cost:0.0787866860628128 | acc:0.5687999725341797
Epoch 57, CIFAR-10 Batch 3: cost:0.04441005736589432 | acc:0.5870000123977661
Epoch 57, CIFAR-10 Batch 4: cost:0.0698433518409729 | acc:0.5787999033927917
Epoch 57, CIFAR-10 Batch 5: cost:0.0692308321595192 | acc:0.5815999507904053
Epoch 58, CIFAR-10 Batch 1: cost:0.0837811678647995 | acc:0.5719999670982361
Epoch 58, CIFAR-10 Batch 2: cost:0.06581699848175049 | acc:0.5837998986244202
Epoch 58, CIFAR-10 Batch 3: cost:0.05653419345617294 | acc:0.5811998844146729
Epoch 58, CIFAR-10 Batch 4: cost:0.05737585574388504 | acc:0.584399938583374
Epoch 58, CIFAR-10 Batch 5: cost:0.07289010286331177 | acc:0.5697999000549316
Epoch 59, CIFAR-10 Batch 1: cost:0.08060608059167862 | acc:0.5759999752044678
Epoch 59, CIFAR-10 Batch 2: cost:0.08455666899681091 | acc:0.5793999433517456
Epoch 59, CIFAR-10 Batch 3: cost:0.045075923204422 | acc:0.5853999257087708
Epoch 59, CIFAR-10 Batch 4: cost:0.06690305471420288 | acc:0.5875999331474304
Epoch 59, CIFAR-10 Batch 5: cost:0.06781791150569916 | acc:0.5779999494552612
Epoch 60, CIFAR-10 Batch 1: cost:0.08580511063337326 | acc:0.5867999196052551
Epoch 60, CIFAR-10 Batch 2: cost:0.10105253756046295 | acc:0.5821999907493591
Epoch 60, CIFAR-10 Batch 3: cost:0.05769956111907959 | acc:0.5761999487876892
Epoch 60, CIFAR-10 Batch 4: cost:0.07927092909812927 | acc:0.5817999243736267
Epoch 60, CIFAR-10 Batch 5: cost:0.07647232711315155 | acc:0.5751999616622925
Epoch 61, CIFAR-10 Batch 1: cost:0.08654149621725082 | acc:0.5763999223709106
Epoch 61, CIFAR-10 Batch 2: cost:0.09775059670209885 | acc:0.5625999569892883
Epoch 61, CIFAR-10 Batch 3: cost:0.07532568275928497 | acc:0.5831999182701111
Epoch 61, CIFAR-10 Batch 4: cost:0.10576699674129486 | acc:0.5735999345779419
Epoch 61, CIFAR-10 Batch 5: cost:0.07788680493831635 | acc:0.5793998837471008
Epoch 62, CIFAR-10 Batch 1: cost:0.07951143383979797 | acc:0.5765999555587769
Epoch 62, CIFAR-10 Batch 2: cost:0.04805050790309906 | acc:0.5741999745368958
Epoch 62, CIFAR-10 Batch 3: cost:0.04333284869790077 | acc:0.5867999196052551
Epoch 62, CIFAR-10 Batch 4: cost:0.07147809863090515 | acc:0.5809999704360962
Epoch 62, CIFAR-10 Batch 5: cost:0.09629753977060318 | acc:0.5837999582290649
Epoch 63, CIFAR-10 Batch 1: cost:0.1007155030965805 | acc:0.5829999446868896
Epoch 63, CIFAR-10 Batch 2: cost:0.08599335700273514 | acc:0.5779998898506165
Epoch 63, CIFAR-10 Batch 3: cost:0.055674318224191666 | acc:0.5837999582290649
Epoch 63, CIFAR-10 Batch 4: cost:0.05261635780334473 | acc:0.5793998837471008
Epoch 63, CIFAR-10 Batch 5: cost:0.07232286781072617 | acc:0.5831999778747559
Epoch 64, CIFAR-10 Batch 1: cost:0.10108523070812225 | acc:0.5823999643325806
Epoch 64, CIFAR-10 Batch 2: cost:0.06610235571861267 | acc:0.5743998885154724
Epoch 64, CIFAR-10 Batch 3: cost:0.04371269792318344 | acc:0.5895999073982239
Epoch 64, CIFAR-10 Batch 4: cost:0.05926789343357086 | acc:0.5841999053955078
Epoch 64, CIFAR-10 Batch 5: cost:0.07047875225543976 | acc:0.5847998857498169
Epoch 65, CIFAR-10 Batch 1: cost:0.07589234411716461 | acc:0.5821999907493591
Epoch 65, CIFAR-10 Batch 2: cost:0.0660470500588417 | acc:0.5733999609947205
Epoch 65, CIFAR-10 Batch 3: cost:0.046616967767477036 | acc:0.5821999311447144
Epoch 65, CIFAR-10 Batch 4: cost:0.0598917156457901 | acc:0.5845999121665955
Epoch 65, CIFAR-10 Batch 5: cost:0.05663027614355087 | acc:0.5843998789787292
Epoch 66, CIFAR-10 Batch 1: cost:0.07470469176769257 | acc:0.5775999426841736
Epoch 66, CIFAR-10 Batch 2: cost:0.06073373556137085 | acc:0.5755999684333801
Epoch 66, CIFAR-10 Batch 3: cost:0.07052967697381973 | acc:0.5813999176025391
Epoch 66, CIFAR-10 Batch 4: cost:0.048704393208026886 | acc:0.5812000036239624
Epoch 66, CIFAR-10 Batch 5: cost:0.07863933593034744 | acc:0.578999936580658
Epoch 67, CIFAR-10 Batch 1: cost:0.07982654124498367 | acc:0.5779998302459717
Epoch 67, CIFAR-10 Batch 2: cost:0.04857884719967842 | acc:0.5721999406814575
Epoch 67, CIFAR-10 Batch 3: cost:0.04058465361595154 | acc:0.5799999833106995
Epoch 67, CIFAR-10 Batch 4: cost:0.05848638713359833 | acc:0.5795998573303223
Epoch 67, CIFAR-10 Batch 5: cost:0.05985410884022713 | acc:0.5887999534606934
Epoch 68, CIFAR-10 Batch 1: cost:0.07439922541379929 | acc:0.5865998864173889
Epoch 68, CIFAR-10 Batch 2: cost:0.04845826327800751 | acc:0.5661999583244324
Epoch 68, CIFAR-10 Batch 3: cost:0.027892420068383217 | acc:0.5869999527931213
Epoch 68, CIFAR-10 Batch 4: cost:0.06959915906190872 | acc:0.5755999088287354
Epoch 68, CIFAR-10 Batch 5: cost:0.061151113361120224 | acc:0.5885999202728271
Epoch 69, CIFAR-10 Batch 1: cost:0.04912324622273445 | acc:0.5787999629974365
Epoch 69, CIFAR-10 Batch 2: cost:0.04750634729862213 | acc:0.5727998614311218
Epoch 69, CIFAR-10 Batch 3: cost:0.03650601580739021 | acc:0.5851999521255493
Epoch 69, CIFAR-10 Batch 4: cost:0.04510103911161423 | acc:0.5909998416900635
Epoch 69, CIFAR-10 Batch 5: cost:0.05727149918675423 | acc:0.5761999487876892
Epoch 70, CIFAR-10 Batch 1: cost:0.07179152220487595 | acc:0.5679998993873596
Epoch 70, CIFAR-10 Batch 2: cost:0.04324134811758995 | acc:0.569399893283844
Epoch 70, CIFAR-10 Batch 3: cost:0.035174835473299026 | acc:0.5851999521255493
Epoch 70, CIFAR-10 Batch 4: cost:0.04912249743938446 | acc:0.5791999101638794
Epoch 70, CIFAR-10 Batch 5: cost:0.0705173909664154 | acc:0.5811999440193176
Epoch 71, CIFAR-10 Batch 1: cost:0.06645020842552185 | acc:0.5751999616622925
Epoch 71, CIFAR-10 Batch 2: cost:0.04007841274142265 | acc:0.5729999542236328
Epoch 71, CIFAR-10 Batch 3: cost:0.029186083003878593 | acc:0.5879998803138733
Epoch 71, CIFAR-10 Batch 4: cost:0.04690339416265488 | acc:0.5875999331474304
Epoch 71, CIFAR-10 Batch 5: cost:0.06119289994239807 | acc:0.5827999711036682
Epoch 72, CIFAR-10 Batch 1: cost:0.04294387251138687 | acc:0.5757999420166016
Epoch 72, CIFAR-10 Batch 2: cost:0.04500843957066536 | acc:0.571199893951416
Epoch 72, CIFAR-10 Batch 3: cost:0.05041763186454773 | acc:0.5861998796463013
Epoch 72, CIFAR-10 Batch 4: cost:0.06519293785095215 | acc:0.5827999114990234
Epoch 72, CIFAR-10 Batch 5: cost:0.06174691766500473 | acc:0.5771999359130859
Epoch 73, CIFAR-10 Batch 1: cost:0.05298212170600891 | acc:0.5765999555587769
Epoch 73, CIFAR-10 Batch 2: cost:0.04024761542677879 | acc:0.5753998756408691
Epoch 73, CIFAR-10 Batch 3: cost:0.0515705868601799 | acc:0.5709999203681946
Epoch 73, CIFAR-10 Batch 4: cost:0.05038128048181534 | acc:0.5845999121665955
Epoch 73, CIFAR-10 Batch 5: cost:0.05993536859750748 | acc:0.5827998518943787
Epoch 74, CIFAR-10 Batch 1: cost:0.04495390132069588 | acc:0.5785999298095703
Epoch 74, CIFAR-10 Batch 2: cost:0.040767498314380646 | acc:0.5689999461174011
Epoch 74, CIFAR-10 Batch 3: cost:0.04113897681236267 | acc:0.5815999507904053
Epoch 74, CIFAR-10 Batch 4: cost:0.04859336465597153 | acc:0.5803999900817871
Epoch 74, CIFAR-10 Batch 5: cost:0.04811674356460571 | acc:0.5877999067306519
Epoch 75, CIFAR-10 Batch 1: cost:0.028219586238265038 | acc:0.5759999752044678
Epoch 75, CIFAR-10 Batch 2: cost:0.03950602188706398 | acc:0.5705999135971069
Epoch 75, CIFAR-10 Batch 3: cost:0.048342201858758926 | acc:0.5639999508857727
Epoch 75, CIFAR-10 Batch 4: cost:0.05644899606704712 | acc:0.5769999027252197
Epoch 75, CIFAR-10 Batch 5: cost:0.03861263394355774 | acc:0.5825998783111572
Epoch 76, CIFAR-10 Batch 1: cost:0.035181187093257904 | acc:0.5821999311447144
Epoch 76, CIFAR-10 Batch 2: cost:0.06133095547556877 | acc:0.5763999223709106
Epoch 76, CIFAR-10 Batch 3: cost:0.023170892149209976 | acc:0.5695999264717102
Epoch 76, CIFAR-10 Batch 4: cost:0.03877647593617439 | acc:0.5773999094963074
Epoch 76, CIFAR-10 Batch 5: cost:0.038942862302064896 | acc:0.5923999547958374
Epoch 77, CIFAR-10 Batch 1: cost:0.02564072236418724 | acc:0.5821999907493591
Epoch 77, CIFAR-10 Batch 2: cost:0.04572966694831848 | acc:0.5787999033927917
Epoch 77, CIFAR-10 Batch 3: cost:0.03355970233678818 | acc:0.5765998959541321
Epoch 77, CIFAR-10 Batch 4: cost:0.04307010769844055 | acc:0.5895999073982239
Epoch 77, CIFAR-10 Batch 5: cost:0.04011790454387665 | acc:0.58079993724823
Epoch 78, CIFAR-10 Batch 1: cost:0.028234947472810745 | acc:0.5829998850822449
Epoch 78, CIFAR-10 Batch 2: cost:0.047371070832014084 | acc:0.572999894618988
Epoch 78, CIFAR-10 Batch 3: cost:0.026095671579241753 | acc:0.5707999467849731
Epoch 78, CIFAR-10 Batch 4: cost:0.0473274327814579 | acc:0.5755999088287354
Epoch 78, CIFAR-10 Batch 5: cost:0.0460839718580246 | acc:0.5743998885154724
Epoch 79, CIFAR-10 Batch 1: cost:0.03477157652378082 | acc:0.5761999487876892
Epoch 79, CIFAR-10 Batch 2: cost:0.05264396592974663 | acc:0.5795999765396118
Epoch 79, CIFAR-10 Batch 3: cost:0.031906649470329285 | acc:0.5805999636650085
Epoch 79, CIFAR-10 Batch 4: cost:0.05570080131292343 | acc:0.5677999258041382
Epoch 79, CIFAR-10 Batch 5: cost:0.034207746386528015 | acc:0.5841999053955078
Epoch 80, CIFAR-10 Batch 1: cost:0.03176223859190941 | acc:0.585599958896637
Epoch 80, CIFAR-10 Batch 2: cost:0.04185652732849121 | acc:0.5765998959541321
Epoch 80, CIFAR-10 Batch 3: cost:0.018045959994196892 | acc:0.5817998647689819
Epoch 80, CIFAR-10 Batch 4: cost:0.03883352875709534 | acc:0.5815999507904053
Epoch 80, CIFAR-10 Batch 5: cost:0.018957747146487236 | acc:0.5885999202728271
Epoch 81, CIFAR-10 Batch 1: cost:0.020500315353274345 | acc:0.5829998850822449
Epoch 81, CIFAR-10 Batch 2: cost:0.04815497249364853 | acc:0.568199872970581
Epoch 81, CIFAR-10 Batch 3: cost:0.031901922076940536 | acc:0.5697999596595764
Epoch 81, CIFAR-10 Batch 4: cost:0.048272233456373215 | acc:0.5791999101638794
Epoch 81, CIFAR-10 Batch 5: cost:0.0365154892206192 | acc:0.584399938583374
Epoch 82, CIFAR-10 Batch 1: cost:0.020953886210918427 | acc:0.5809999704360962
Epoch 82, CIFAR-10 Batch 2: cost:0.03969942405819893 | acc:0.5815998911857605
Epoch 82, CIFAR-10 Batch 3: cost:0.018238814547657967 | acc:0.5749999284744263
Epoch 82, CIFAR-10 Batch 4: cost:0.05305451154708862 | acc:0.5787999033927917
Epoch 82, CIFAR-10 Batch 5: cost:0.02014504186809063 | acc:0.5783998966217041
Epoch 83, CIFAR-10 Batch 1: cost:0.0368976816534996 | acc:0.5905998945236206
Epoch 83, CIFAR-10 Batch 2: cost:0.029723580926656723 | acc:0.5865999460220337
Epoch 83, CIFAR-10 Batch 3: cost:0.029224473983049393 | acc:0.5765999555587769
Epoch 83, CIFAR-10 Batch 4: cost:0.042657043784856796 | acc:0.5843998789787292
Epoch 83, CIFAR-10 Batch 5: cost:0.03991813585162163 | acc:0.5773999094963074
Epoch 84, CIFAR-10 Batch 1: cost:0.023963268846273422 | acc:0.5867999196052551
Epoch 84, CIFAR-10 Batch 2: cost:0.053629472851753235 | acc:0.5737999081611633
Epoch 84, CIFAR-10 Batch 3: cost:0.022467438131570816 | acc:0.5811999440193176
Epoch 84, CIFAR-10 Batch 4: cost:0.040753480046987534 | acc:0.5755999088287354
Epoch 84, CIFAR-10 Batch 5: cost:0.026314225047826767 | acc:0.5729999542236328
Epoch 85, CIFAR-10 Batch 1: cost:0.016294334083795547 | acc:0.5887998938560486
Epoch 85, CIFAR-10 Batch 2: cost:0.038792889565229416 | acc:0.5845999121665955
Epoch 85, CIFAR-10 Batch 3: cost:0.01727244071662426 | acc:0.5867999196052551
Epoch 85, CIFAR-10 Batch 4: cost:0.03848545253276825 | acc:0.5803999304771423
Epoch 85, CIFAR-10 Batch 5: cost:0.03735606372356415 | acc:0.5815999507904053
Epoch 86, CIFAR-10 Batch 1: cost:0.02697134017944336 | acc:0.5841999650001526
Epoch 86, CIFAR-10 Batch 2: cost:0.031472574919462204 | acc:0.5841999650001526
Epoch 86, CIFAR-10 Batch 3: cost:0.014380613341927528 | acc:0.5775998830795288
Epoch 86, CIFAR-10 Batch 4: cost:0.05496642738580704 | acc:0.5797998905181885
Epoch 86, CIFAR-10 Batch 5: cost:0.02636132389307022 | acc:0.5749999284744263
Epoch 87, CIFAR-10 Batch 1: cost:0.02346072904765606 | acc:0.5811998844146729
Epoch 87, CIFAR-10 Batch 2: cost:0.030577898025512695 | acc:0.5895999073982239
Epoch 87, CIFAR-10 Batch 3: cost:0.018168117851018906 | acc:0.5779999494552612
Epoch 87, CIFAR-10 Batch 4: cost:0.03251676633954048 | acc:0.58079993724823
Epoch 87, CIFAR-10 Batch 5: cost:0.028796445578336716 | acc:0.5767998695373535
Epoch 88, CIFAR-10 Batch 1: cost:0.026518985629081726 | acc:0.5853999257087708
Epoch 88, CIFAR-10 Batch 2: cost:0.02551758848130703 | acc:0.582599937915802
Epoch 88, CIFAR-10 Batch 3: cost:0.011716351844370365 | acc:0.5719999074935913
Epoch 88, CIFAR-10 Batch 4: cost:0.029935544356703758 | acc:0.5889999270439148
Epoch 88, CIFAR-10 Batch 5: cost:0.04645022749900818 | acc:0.572999894618988
Epoch 89, CIFAR-10 Batch 1: cost:0.02721814438700676 | acc:0.579599916934967
Epoch 89, CIFAR-10 Batch 2: cost:0.023785043507814407 | acc:0.5845999717712402
Epoch 89, CIFAR-10 Batch 3: cost:0.017400024458765984 | acc:0.5747998952865601
Epoch 89, CIFAR-10 Batch 4: cost:0.02297266572713852 | acc:0.5793999433517456
Epoch 89, CIFAR-10 Batch 5: cost:0.03564213961362839 | acc:0.5701999068260193
Epoch 90, CIFAR-10 Batch 1: cost:0.021576233208179474 | acc:0.5853999257087708
Epoch 90, CIFAR-10 Batch 2: cost:0.018170226365327835 | acc:0.5935998558998108
Epoch 90, CIFAR-10 Batch 3: cost:0.019535398110747337 | acc:0.5771998763084412
Epoch 90, CIFAR-10 Batch 4: cost:0.026029929518699646 | acc:0.5779999494552612
Epoch 90, CIFAR-10 Batch 5: cost:0.0321507565677166 | acc:0.5827999114990234
Epoch 91, CIFAR-10 Batch 1: cost:0.02522076666355133 | acc:0.5857999324798584
Epoch 91, CIFAR-10 Batch 2: cost:0.01802179031074047 | acc:0.5821998715400696
Epoch 91, CIFAR-10 Batch 3: cost:0.01223494578152895 | acc:0.574199914932251
Epoch 91, CIFAR-10 Batch 4: cost:0.02433980442583561 | acc:0.5865999460220337
Epoch 91, CIFAR-10 Batch 5: cost:0.0171340499073267 | acc:0.5667999386787415
Epoch 92, CIFAR-10 Batch 1: cost:0.027747292071580887 | acc:0.5757999420166016
Epoch 92, CIFAR-10 Batch 2: cost:0.03042982891201973 | acc:0.5825998783111572
Epoch 92, CIFAR-10 Batch 3: cost:0.01505883876234293 | acc:0.5773999094963074
Epoch 92, CIFAR-10 Batch 4: cost:0.02165454998612404 | acc:0.5859999060630798
Epoch 92, CIFAR-10 Batch 5: cost:0.0322972796857357 | acc:0.5765999555587769
Epoch 93, CIFAR-10 Batch 1: cost:0.01696450263261795 | acc:0.58079993724823
Epoch 93, CIFAR-10 Batch 2: cost:0.02454318106174469 | acc:0.5809999108314514
Epoch 93, CIFAR-10 Batch 3: cost:0.010320421308279037 | acc:0.577799916267395
Epoch 93, CIFAR-10 Batch 4: cost:0.024457469582557678 | acc:0.586199939250946
Epoch 93, CIFAR-10 Batch 5: cost:0.036552321165800095 | acc:0.5761999487876892
Epoch 94, CIFAR-10 Batch 1: cost:0.0269392728805542 | acc:0.5809998512268066
Epoch 94, CIFAR-10 Batch 2: cost:0.019842322915792465 | acc:0.5749999284744263
Epoch 94, CIFAR-10 Batch 3: cost:0.012759489938616753 | acc:0.5809999108314514
Epoch 94, CIFAR-10 Batch 4: cost:0.028688253834843636 | acc:0.5837998986244202
Epoch 94, CIFAR-10 Batch 5: cost:0.023526784032583237 | acc:0.5805999636650085
Epoch 95, CIFAR-10 Batch 1: cost:0.019604165107011795 | acc:0.5719999670982361
Epoch 95, CIFAR-10 Batch 2: cost:0.016455205157399178 | acc:0.5812000036239624
Epoch 95, CIFAR-10 Batch 3: cost:0.021444780752062798 | acc:0.5761999487876892
Epoch 95, CIFAR-10 Batch 4: cost:0.015967214480042458 | acc:0.5947999954223633
Epoch 95, CIFAR-10 Batch 5: cost:0.018641462549567223 | acc:0.5805999636650085
Epoch 96, CIFAR-10 Batch 1: cost:0.009694930166006088 | acc:0.5905998945236206
Epoch 96, CIFAR-10 Batch 2: cost:0.02248542569577694 | acc:0.5783999562263489
Epoch 96, CIFAR-10 Batch 3: cost:0.011599697172641754 | acc:0.5879999399185181
Epoch 96, CIFAR-10 Batch 4: cost:0.022754907608032227 | acc:0.5879998803138733
Epoch 96, CIFAR-10 Batch 5: cost:0.03277900815010071 | acc:0.572399914264679
Epoch 97, CIFAR-10 Batch 1: cost:0.01425120048224926 | acc:0.5807998776435852
Epoch 97, CIFAR-10 Batch 2: cost:0.02378598414361477 | acc:0.5855998992919922
Epoch 97, CIFAR-10 Batch 3: cost:0.013585295528173447 | acc:0.5809999108314514
Epoch 97, CIFAR-10 Batch 4: cost:0.018065746873617172 | acc:0.6001999378204346
Epoch 97, CIFAR-10 Batch 5: cost:0.021680153906345367 | acc:0.5751999616622925
Epoch 98, CIFAR-10 Batch 1: cost:0.013587555848062038 | acc:0.577799916267395
Epoch 98, CIFAR-10 Batch 2: cost:0.01573627069592476 | acc:0.5811998844146729
Epoch 98, CIFAR-10 Batch 3: cost:0.010092217475175858 | acc:0.5797999501228333
Epoch 98, CIFAR-10 Batch 4: cost:0.013200436718761921 | acc:0.5925999283790588
Epoch 98, CIFAR-10 Batch 5: cost:0.017089620232582092 | acc:0.5733999013900757
Epoch 99, CIFAR-10 Batch 1: cost:0.023402610793709755 | acc:0.5791999697685242
Epoch 99, CIFAR-10 Batch 2: cost:0.014317245222628117 | acc:0.567599892616272
Epoch 99, CIFAR-10 Batch 3: cost:0.019597727805376053 | acc:0.5659999251365662
Epoch 99, CIFAR-10 Batch 4: cost:0.012778358533978462 | acc:0.5947998762130737
Epoch 99, CIFAR-10 Batch 5: cost:0.03495368734002113 | acc:0.5765998959541321
Epoch 100, CIFAR-10 Batch 1: cost:0.015373659320175648 | acc:0.5847998857498169
Epoch 100, CIFAR-10 Batch 2: cost:0.013422822579741478 | acc:0.5785999298095703
Epoch 100, CIFAR-10 Batch 3: cost:0.008656156249344349 | acc:0.5767999291419983
Epoch 100, CIFAR-10 Batch 4: cost:0.017665492370724678 | acc:0.5957999229431152
Epoch 100, CIFAR-10 Batch 5: cost:0.022597182542085648 | acc:0.5753999352455139
Epoch 101, CIFAR-10 Batch 1: cost:0.014691730029881 | acc:0.5899999141693115
Epoch 101, CIFAR-10 Batch 2: cost:0.014002989046275616 | acc:0.5757999420166016
Epoch 101, CIFAR-10 Batch 3: cost:0.011992031708359718 | acc:0.5793999433517456
Epoch 101, CIFAR-10 Batch 4: cost:0.013448852114379406 | acc:0.5895999073982239
Epoch 101, CIFAR-10 Batch 5: cost:0.01644119992852211 | acc:0.5843999981880188
Epoch 102, CIFAR-10 Batch 1: cost:0.012011315673589706 | acc:0.5923998951911926
Epoch 102, CIFAR-10 Batch 2: cost:0.009291229769587517 | acc:0.577799916267395
Epoch 102, CIFAR-10 Batch 3: cost:0.028560329228639603 | acc:0.5711999535560608
Epoch 102, CIFAR-10 Batch 4: cost:0.016347631812095642 | acc:0.5955999493598938
Epoch 102, CIFAR-10 Batch 5: cost:0.0243535153567791 | acc:0.5759999752044678
Epoch 103, CIFAR-10 Batch 1: cost:0.012001295574009418 | acc:0.5831999182701111
Epoch 103, CIFAR-10 Batch 2: cost:0.0136526208370924 | acc:0.5807998776435852
Epoch 103, CIFAR-10 Batch 3: cost:0.00767988758161664 | acc:0.5833999514579773
Epoch 103, CIFAR-10 Batch 4: cost:0.009045644663274288 | acc:0.5959998965263367
Epoch 103, CIFAR-10 Batch 5: cost:0.016102051362395287 | acc:0.5809999704360962
Epoch 104, CIFAR-10 Batch 1: cost:0.01566058024764061 | acc:0.5917999744415283
Epoch 104, CIFAR-10 Batch 2: cost:0.006432424299418926 | acc:0.5831999182701111
Epoch 104, CIFAR-10 Batch 3: cost:0.008450118824839592 | acc:0.5851999521255493
Epoch 104, CIFAR-10 Batch 4: cost:0.01233205758035183 | acc:0.598599910736084
Epoch 104, CIFAR-10 Batch 5: cost:0.026390179991722107 | acc:0.5737999677658081
Epoch 105, CIFAR-10 Batch 1: cost:0.024623630568385124 | acc:0.5895999073982239
Epoch 105, CIFAR-10 Batch 2: cost:0.010494377464056015 | acc:0.5869999527931213
Epoch 105, CIFAR-10 Batch 3: cost:0.005788926966488361 | acc:0.5797998905181885
Epoch 105, CIFAR-10 Batch 4: cost:0.01174083910882473 | acc:0.5865999460220337
Epoch 105, CIFAR-10 Batch 5: cost:0.018722323700785637 | acc:0.5809999108314514
Epoch 106, CIFAR-10 Batch 1: cost:0.01740686595439911 | acc:0.5951999425888062
Epoch 106, CIFAR-10 Batch 2: cost:0.008346270769834518 | acc:0.5883998870849609
Epoch 106, CIFAR-10 Batch 3: cost:0.009881890378892422 | acc:0.5863999128341675
Epoch 106, CIFAR-10 Batch 4: cost:0.01031981036067009 | acc:0.5907999277114868
Epoch 106, CIFAR-10 Batch 5: cost:0.020143870264291763 | acc:0.5893999338150024
Epoch 107, CIFAR-10 Batch 1: cost:0.014136760495603085 | acc:0.5925998687744141
Epoch 107, CIFAR-10 Batch 2: cost:0.010134294629096985 | acc:0.5799999237060547
Epoch 107, CIFAR-10 Batch 3: cost:0.004415726289153099 | acc:0.5867999196052551
Epoch 107, CIFAR-10 Batch 4: cost:0.009029476903378963 | acc:0.5923999547958374
Epoch 107, CIFAR-10 Batch 5: cost:0.020723732188344002 | acc:0.5725999474525452
Epoch 108, CIFAR-10 Batch 1: cost:0.021441008895635605 | acc:0.5893999338150024
Epoch 108, CIFAR-10 Batch 2: cost:0.0095272371545434 | acc:0.5757998824119568
Epoch 108, CIFAR-10 Batch 3: cost:0.005690528079867363 | acc:0.5865999460220337
Epoch 108, CIFAR-10 Batch 4: cost:0.014453185722231865 | acc:0.5873998403549194
Epoch 108, CIFAR-10 Batch 5: cost:0.02027156949043274 | acc:0.5887999534606934
Epoch 109, CIFAR-10 Batch 1: cost:0.009874920360744 | acc:0.5853999257087708
Epoch 109, CIFAR-10 Batch 2: cost:0.012867078185081482 | acc:0.5769999027252197
Epoch 109, CIFAR-10 Batch 3: cost:0.00916028581559658 | acc:0.5791999101638794
Epoch 109, CIFAR-10 Batch 4: cost:0.011999152600765228 | acc:0.5837998986244202
Epoch 109, CIFAR-10 Batch 5: cost:0.014909865334630013 | acc:0.5745999217033386
Epoch 110, CIFAR-10 Batch 1: cost:0.022951018065214157 | acc:0.5847998857498169
Epoch 110, CIFAR-10 Batch 2: cost:0.009785955771803856 | acc:0.5769999027252197
Epoch 110, CIFAR-10 Batch 3: cost:0.007512732874602079 | acc:0.5705999135971069
Epoch 110, CIFAR-10 Batch 4: cost:0.009640310890972614 | acc:0.5885999202728271
Epoch 110, CIFAR-10 Batch 5: cost:0.00998635869473219 | acc:0.5805999040603638
Epoch 111, CIFAR-10 Batch 1: cost:0.012841367162764072 | acc:0.5923998951911926
Epoch 111, CIFAR-10 Batch 2: cost:0.013798597268760204 | acc:0.5761999487876892
Epoch 111, CIFAR-10 Batch 3: cost:0.009130803868174553 | acc:0.5809999108314514
Epoch 111, CIFAR-10 Batch 4: cost:0.01349855586886406 | acc:0.585399866104126
Epoch 111, CIFAR-10 Batch 5: cost:0.01614917255938053 | acc:0.582599937915802
Epoch 112, CIFAR-10 Batch 1: cost:0.022230956703424454 | acc:0.5887998938560486
Epoch 112, CIFAR-10 Batch 2: cost:0.015011042356491089 | acc:0.5871999263763428
Epoch 112, CIFAR-10 Batch 3: cost:0.009180638939142227 | acc:0.5703999400138855
Epoch 112, CIFAR-10 Batch 4: cost:0.013722767122089863 | acc:0.5835999846458435
Epoch 112, CIFAR-10 Batch 5: cost:0.023178480565547943 | acc:0.5813999176025391
Epoch 113, CIFAR-10 Batch 1: cost:0.01065256167203188 | acc:0.5753998756408691
Epoch 113, CIFAR-10 Batch 2: cost:0.014552385546267033 | acc:0.5859999656677246
Epoch 113, CIFAR-10 Batch 3: cost:0.0033647241070866585 | acc:0.5863999724388123
Epoch 113, CIFAR-10 Batch 4: cost:0.01355825550854206 | acc:0.5812000036239624
Epoch 113, CIFAR-10 Batch 5: cost:0.00932264607399702 | acc:0.5761998891830444
Epoch 114, CIFAR-10 Batch 1: cost:0.016863340511918068 | acc:0.5791999101638794
Epoch 114, CIFAR-10 Batch 2: cost:0.03067409060895443 | acc:0.5897999405860901
Epoch 114, CIFAR-10 Batch 3: cost:0.007858972996473312 | acc:0.582599937915802
Epoch 114, CIFAR-10 Batch 4: cost:0.01392148993909359 | acc:0.596799910068512
Epoch 114, CIFAR-10 Batch 5: cost:0.012887119315564632 | acc:0.5789998769760132
Epoch 115, CIFAR-10 Batch 1: cost:0.022179899737238884 | acc:0.5831999778747559
Epoch 115, CIFAR-10 Batch 2: cost:0.015159460715949535 | acc:0.5749999284744263
Epoch 115, CIFAR-10 Batch 3: cost:0.006994521711021662 | acc:0.5885999202728271
Epoch 115, CIFAR-10 Batch 4: cost:0.011577129364013672 | acc:0.5849999189376831
Epoch 115, CIFAR-10 Batch 5: cost:0.0155458003282547 | acc:0.5877999067306519
Epoch 116, CIFAR-10 Batch 1: cost:0.019865399226546288 | acc:0.5837999582290649
Epoch 116, CIFAR-10 Batch 2: cost:0.014399831183254719 | acc:0.5847999453544617
Epoch 116, CIFAR-10 Batch 3: cost:0.004647685214877129 | acc:0.5887998938560486
Epoch 116, CIFAR-10 Batch 4: cost:0.009229596704244614 | acc:0.5931999087333679
Epoch 116, CIFAR-10 Batch 5: cost:0.012297716923058033 | acc:0.5851999521255493
Epoch 117, CIFAR-10 Batch 1: cost:0.006497472524642944 | acc:0.5953999161720276
Epoch 117, CIFAR-10 Batch 2: cost:0.014758076518774033 | acc:0.5915998816490173
Epoch 117, CIFAR-10 Batch 3: cost:0.008136590011417866 | acc:0.5809999108314514
Epoch 117, CIFAR-10 Batch 4: cost:0.010319479741156101 | acc:0.5929998755455017
Epoch 117, CIFAR-10 Batch 5: cost:0.007648499682545662 | acc:0.5879999399185181
Epoch 118, CIFAR-10 Batch 1: cost:0.01315638329833746 | acc:0.5867999792098999
Epoch 118, CIFAR-10 Batch 2: cost:0.01654781401157379 | acc:0.5899999141693115
Epoch 118, CIFAR-10 Batch 3: cost:0.014998148195445538 | acc:0.579599916934967
Epoch 118, CIFAR-10 Batch 4: cost:0.011244969442486763 | acc:0.5927999019622803
Epoch 118, CIFAR-10 Batch 5: cost:0.011416886933147907 | acc:0.5755999088287354
Epoch 119, CIFAR-10 Batch 1: cost:0.008886676281690598 | acc:0.587399959564209
Epoch 119, CIFAR-10 Batch 2: cost:0.016577694565057755 | acc:0.5897998809814453
Epoch 119, CIFAR-10 Batch 3: cost:0.00467457203194499 | acc:0.5811998844146729
Epoch 119, CIFAR-10 Batch 4: cost:0.00803568959236145 | acc:0.5899999737739563
Epoch 119, CIFAR-10 Batch 5: cost:0.012742465361952782 | acc:0.5877999067306519
Epoch 120, CIFAR-10 Batch 1: cost:0.007870534434914589 | acc:0.5923999547958374
Epoch 120, CIFAR-10 Batch 2: cost:0.015476519241929054 | acc:0.586199939250946
Epoch 120, CIFAR-10 Batch 3: cost:0.006674312986433506 | acc:0.5839999318122864
Epoch 120, CIFAR-10 Batch 4: cost:0.008595895953476429 | acc:0.5937998294830322
Epoch 120, CIFAR-10 Batch 5: cost:0.012641130946576595 | acc:0.5809999108314514
Epoch 121, CIFAR-10 Batch 1: cost:0.011564862914383411 | acc:0.5857999324798584
Epoch 121, CIFAR-10 Batch 2: cost:0.013779868371784687 | acc:0.586199939250946
Epoch 121, CIFAR-10 Batch 3: cost:0.00938095711171627 | acc:0.5775998830795288
Epoch 121, CIFAR-10 Batch 4: cost:0.005538881756365299 | acc:0.5823999047279358
Epoch 121, CIFAR-10 Batch 5: cost:0.008131079375743866 | acc:0.5831999182701111
Epoch 122, CIFAR-10 Batch 1: cost:0.005728506483137608 | acc:0.5877999067306519
Epoch 122, CIFAR-10 Batch 2: cost:0.01603839546442032 | acc:0.5793999433517456
Epoch 122, CIFAR-10 Batch 3: cost:0.005834795534610748 | acc:0.5779999494552612
Epoch 122, CIFAR-10 Batch 4: cost:0.006317781284451485 | acc:0.5947998762130737
Epoch 122, CIFAR-10 Batch 5: cost:0.01324520818889141 | acc:0.5767998695373535
Epoch 123, CIFAR-10 Batch 1: cost:0.009603881277143955 | acc:0.5909999012947083
Epoch 123, CIFAR-10 Batch 2: cost:0.008518685586750507 | acc:0.5935999155044556
Epoch 123, CIFAR-10 Batch 3: cost:0.0032544261775910854 | acc:0.5865999460220337
Epoch 123, CIFAR-10 Batch 4: cost:0.006061766296625137 | acc:0.5937999486923218
Epoch 123, CIFAR-10 Batch 5: cost:0.007537060417234898 | acc:0.5879999399185181
Epoch 124, CIFAR-10 Batch 1: cost:0.012626527808606625 | acc:0.5895999073982239
Epoch 124, CIFAR-10 Batch 2: cost:0.013220185413956642 | acc:0.5871999263763428
Epoch 124, CIFAR-10 Batch 3: cost:0.0063875592313706875 | acc:0.5833999514579773
Epoch 124, CIFAR-10 Batch 4: cost:0.010732396505773067 | acc:0.5959999561309814
Epoch 124, CIFAR-10 Batch 5: cost:0.005484766326844692 | acc:0.5907999277114868
Epoch 125, CIFAR-10 Batch 1: cost:0.007992440834641457 | acc:0.5889999866485596
Epoch 125, CIFAR-10 Batch 2: cost:0.009006991982460022 | acc:0.5967999696731567
Epoch 125, CIFAR-10 Batch 3: cost:0.009060259908437729 | acc:0.5851999521255493
Epoch 125, CIFAR-10 Batch 4: cost:0.005790557246655226 | acc:0.5933998823165894
Epoch 125, CIFAR-10 Batch 5: cost:0.005281957797706127 | acc:0.5907999277114868
Epoch 126, CIFAR-10 Batch 1: cost:0.007181637454777956 | acc:0.5987998843193054
Epoch 126, CIFAR-10 Batch 2: cost:0.007691787555813789 | acc:0.5877999067306519
Epoch 126, CIFAR-10 Batch 3: cost:0.0077852169051766396 | acc:0.5905998945236206
Epoch 126, CIFAR-10 Batch 4: cost:0.005947125609964132 | acc:0.602199912071228
Epoch 126, CIFAR-10 Batch 5: cost:0.01647068001329899 | acc:0.5921999216079712
Epoch 127, CIFAR-10 Batch 1: cost:0.006749117746949196 | acc:0.5939999222755432
Epoch 127, CIFAR-10 Batch 2: cost:0.00796002522110939 | acc:0.5953999757766724
Epoch 127, CIFAR-10 Batch 3: cost:0.006053030490875244 | acc:0.5857999324798584
Epoch 127, CIFAR-10 Batch 4: cost:0.007347666192799807 | acc:0.5945998430252075
Epoch 127, CIFAR-10 Batch 5: cost:0.008099268190562725 | acc:0.5893998742103577
Epoch 128, CIFAR-10 Batch 1: cost:0.00879599153995514 | acc:0.5979999303817749
Epoch 128, CIFAR-10 Batch 2: cost:0.00686652073636651 | acc:0.5879998803138733
Epoch 128, CIFAR-10 Batch 3: cost:0.003997849766165018 | acc:0.5857998728752136
Epoch 128, CIFAR-10 Batch 4: cost:0.006161765661090612 | acc:0.5897999405860901
Epoch 128, CIFAR-10 Batch 5: cost:0.009200051426887512 | acc:0.590799868106842
Epoch 129, CIFAR-10 Batch 1: cost:0.006124334409832954 | acc:0.5945999026298523
Epoch 129, CIFAR-10 Batch 2: cost:0.008854517713189125 | acc:0.5947998762130737
Epoch 129, CIFAR-10 Batch 3: cost:0.0030971725936979055 | acc:0.578999936580658
Epoch 129, CIFAR-10 Batch 4: cost:0.007920771837234497 | acc:0.5895999073982239
Epoch 129, CIFAR-10 Batch 5: cost:0.006634268909692764 | acc:0.5781999826431274
Epoch 130, CIFAR-10 Batch 1: cost:0.010587111115455627 | acc:0.5933998823165894
Epoch 130, CIFAR-10 Batch 2: cost:0.0049116965383291245 | acc:0.5971999168395996
Epoch 130, CIFAR-10 Batch 3: cost:0.003769342787563801 | acc:0.5875998735427856
Epoch 130, CIFAR-10 Batch 4: cost:0.005394320003688335 | acc:0.593799889087677
Epoch 130, CIFAR-10 Batch 5: cost:0.007574863266199827 | acc:0.5887998938560486
Epoch 131, CIFAR-10 Batch 1: cost:0.009293485432863235 | acc:0.5997999310493469
Epoch 131, CIFAR-10 Batch 2: cost:0.013154427520930767 | acc:0.5857999324798584
Epoch 131, CIFAR-10 Batch 3: cost:0.004730717279016972 | acc:0.584399938583374
Epoch 131, CIFAR-10 Batch 4: cost:0.004799101501703262 | acc:0.601599931716919
Epoch 131, CIFAR-10 Batch 5: cost:0.011168789118528366 | acc:0.5845998525619507
Epoch 132, CIFAR-10 Batch 1: cost:0.004525264725089073 | acc:0.5963999629020691
Epoch 132, CIFAR-10 Batch 2: cost:0.008067378774285316 | acc:0.5909999012947083
Epoch 132, CIFAR-10 Batch 3: cost:0.00512742530554533 | acc:0.5859999060630798
Epoch 132, CIFAR-10 Batch 4: cost:0.0070273177698254585 | acc:0.5871999263763428
Epoch 132, CIFAR-10 Batch 5: cost:0.008951940573751926 | acc:0.5815998911857605
Epoch 133, CIFAR-10 Batch 1: cost:0.005389711353927851 | acc:0.5907999277114868
Epoch 133, CIFAR-10 Batch 2: cost:0.008138952776789665 | acc:0.5827999114990234
Epoch 133, CIFAR-10 Batch 3: cost:0.009262312203645706 | acc:0.5705999135971069
Epoch 133, CIFAR-10 Batch 4: cost:0.0023240724112838507 | acc:0.597399890422821
Epoch 133, CIFAR-10 Batch 5: cost:0.007286794018000364 | acc:0.586199939250946
Epoch 134, CIFAR-10 Batch 1: cost:0.00708790635690093 | acc:0.5923998951911926
Epoch 134, CIFAR-10 Batch 2: cost:0.012074984610080719 | acc:0.5893998742103577
Epoch 134, CIFAR-10 Batch 3: cost:0.006647537928074598 | acc:0.5771999359130859
Epoch 134, CIFAR-10 Batch 4: cost:0.005345218814909458 | acc:0.5915999412536621
Epoch 134, CIFAR-10 Batch 5: cost:0.007050841581076384 | acc:0.5839999318122864
Epoch 135, CIFAR-10 Batch 1: cost:0.008274470455944538 | acc:0.5929999351501465
Epoch 135, CIFAR-10 Batch 2: cost:0.006931887939572334 | acc:0.5879999399185181
Epoch 135, CIFAR-10 Batch 3: cost:0.018178217113018036 | acc:0.5725998878479004
Epoch 135, CIFAR-10 Batch 4: cost:0.006318188738077879 | acc:0.5917999148368835
Epoch 135, CIFAR-10 Batch 5: cost:0.003155752783641219 | acc:0.5855998992919922
Epoch 136, CIFAR-10 Batch 1: cost:0.0035731522366404533 | acc:0.5963999032974243
Epoch 136, CIFAR-10 Batch 2: cost:0.004546072334051132 | acc:0.5891999006271362
Epoch 136, CIFAR-10 Batch 3: cost:0.004109054803848267 | acc:0.5857999324798584
Epoch 136, CIFAR-10 Batch 4: cost:0.003694054903462529 | acc:0.6003999710083008
Epoch 136, CIFAR-10 Batch 5: cost:0.0034770313650369644 | acc:0.5897999405860901
Epoch 137, CIFAR-10 Batch 1: cost:0.01634153723716736 | acc:0.5795998573303223
Epoch 137, CIFAR-10 Batch 2: cost:0.0059700533747673035 | acc:0.5939999222755432
Epoch 137, CIFAR-10 Batch 3: cost:0.006140547804534435 | acc:0.5811998844146729
Epoch 137, CIFAR-10 Batch 4: cost:0.002246988471597433 | acc:0.5961999297142029
Epoch 137, CIFAR-10 Batch 5: cost:0.005444329231977463 | acc:0.5847999453544617
Epoch 138, CIFAR-10 Batch 1: cost:0.0024707885459065437 | acc:0.5903998613357544
Epoch 138, CIFAR-10 Batch 2: cost:0.006255050655454397 | acc:0.5943998694419861
Epoch 138, CIFAR-10 Batch 3: cost:0.003976061008870602 | acc:0.5753998756408691
Epoch 138, CIFAR-10 Batch 4: cost:0.005187151022255421 | acc:0.5917999148368835
Epoch 138, CIFAR-10 Batch 5: cost:0.014645510353147984 | acc:0.5819998979568481
Epoch 139, CIFAR-10 Batch 1: cost:0.003808452980592847 | acc:0.5927999019622803
Epoch 139, CIFAR-10 Batch 2: cost:0.006151593290269375 | acc:0.5941998958587646
Epoch 139, CIFAR-10 Batch 3: cost:0.0056825364008545876 | acc:0.5763999223709106
Epoch 139, CIFAR-10 Batch 4: cost:0.0030662978533655405 | acc:0.6047999262809753
Epoch 139, CIFAR-10 Batch 5: cost:0.005012172739952803 | acc:0.5871999263763428
Epoch 140, CIFAR-10 Batch 1: cost:0.004721322096884251 | acc:0.5863999128341675
Epoch 140, CIFAR-10 Batch 2: cost:0.00493251346051693 | acc:0.5905998945236206
Epoch 140, CIFAR-10 Batch 3: cost:0.0022081639617681503 | acc:0.58079993724823
Epoch 140, CIFAR-10 Batch 4: cost:0.005404924042522907 | acc:0.6023998856544495
Epoch 140, CIFAR-10 Batch 5: cost:0.005341708660125732 | acc:0.5795998573303223
Epoch 141, CIFAR-10 Batch 1: cost:0.003992454148828983 | acc:0.5931999683380127
Epoch 141, CIFAR-10 Batch 2: cost:0.00512657267972827 | acc:0.5931999683380127
Epoch 141, CIFAR-10 Batch 3: cost:0.0026477202773094177 | acc:0.5803999304771423
Epoch 141, CIFAR-10 Batch 4: cost:0.0030424753203988075 | acc:0.6037998795509338
Epoch 141, CIFAR-10 Batch 5: cost:0.004646971821784973 | acc:0.585599958896637
Epoch 142, CIFAR-10 Batch 1: cost:0.003961903043091297 | acc:0.5887998938560486
Epoch 142, CIFAR-10 Batch 2: cost:0.004205123521387577 | acc:0.5925998687744141
Epoch 142, CIFAR-10 Batch 3: cost:0.0036641398910433054 | acc:0.5785999298095703
Epoch 142, CIFAR-10 Batch 4: cost:0.010907119140028954 | acc:0.5957999229431152
Epoch 142, CIFAR-10 Batch 5: cost:0.0037916386500000954 | acc:0.5907999277114868
Epoch 143, CIFAR-10 Batch 1: cost:0.0035053170286118984 | acc:0.5995999574661255
Epoch 143, CIFAR-10 Batch 2: cost:0.008563357405364513 | acc:0.5959999561309814
Epoch 143, CIFAR-10 Batch 3: cost:0.0022883140482008457 | acc:0.5861998796463013
Epoch 143, CIFAR-10 Batch 4: cost:0.004631413146853447 | acc:0.6027999520301819
Epoch 143, CIFAR-10 Batch 5: cost:0.0031990064308047295 | acc:0.5821999311447144
Epoch 144, CIFAR-10 Batch 1: cost:0.005394944921135902 | acc:0.5929999351501465
Epoch 144, CIFAR-10 Batch 2: cost:0.007945952005684376 | acc:0.5939999222755432
Epoch 144, CIFAR-10 Batch 3: cost:0.0019844789057970047 | acc:0.5831999778747559
Epoch 144, CIFAR-10 Batch 4: cost:0.004023346118628979 | acc:0.5927999019622803
Epoch 144, CIFAR-10 Batch 5: cost:0.0033882749266922474 | acc:0.5907999277114868
Epoch 145, CIFAR-10 Batch 1: cost:0.0028879004530608654 | acc:0.5945999026298523
Epoch 145, CIFAR-10 Batch 2: cost:0.006902216002345085 | acc:0.5879999399185181
Epoch 145, CIFAR-10 Batch 3: cost:0.0034967102110385895 | acc:0.574199914932251
Epoch 145, CIFAR-10 Batch 4: cost:0.006431637331843376 | acc:0.5917999148368835
Epoch 145, CIFAR-10 Batch 5: cost:0.006394116207957268 | acc:0.5797999501228333
Epoch 146, CIFAR-10 Batch 1: cost:0.006280791945755482 | acc:0.5863999128341675
Epoch 146, CIFAR-10 Batch 2: cost:0.005090450868010521 | acc:0.5939999222755432
Epoch 146, CIFAR-10 Batch 3: cost:0.007193433120846748 | acc:0.575999915599823
Epoch 146, CIFAR-10 Batch 4: cost:0.0027912219520658255 | acc:0.5953999161720276
Epoch 146, CIFAR-10 Batch 5: cost:0.002872440731152892 | acc:0.6013999581336975
Epoch 147, CIFAR-10 Batch 1: cost:0.0057508619502186775 | acc:0.5911998748779297
Epoch 147, CIFAR-10 Batch 2: cost:0.014230279251933098 | acc:0.5935999155044556
Epoch 147, CIFAR-10 Batch 3: cost:0.0025833328254520893 | acc:0.5853999257087708
Epoch 147, CIFAR-10 Batch 4: cost:0.006850298028439283 | acc:0.5917999148368835
Epoch 147, CIFAR-10 Batch 5: cost:0.005020519718527794 | acc:0.587399959564209
Epoch 148, CIFAR-10 Batch 1: cost:0.0062032416462898254 | acc:0.5949999094009399
Epoch 148, CIFAR-10 Batch 2: cost:0.011918088421225548 | acc:0.5913999080657959
Epoch 148, CIFAR-10 Batch 3: cost:0.008562689647078514 | acc:0.5727999210357666
Epoch 148, CIFAR-10 Batch 4: cost:0.0034342841245234013 | acc:0.5963999629020691
Epoch 148, CIFAR-10 Batch 5: cost:0.004553004167973995 | acc:0.5827999114990234
Epoch 149, CIFAR-10 Batch 1: cost:0.0014771790010854602 | acc:0.5905999541282654
Epoch 149, CIFAR-10 Batch 2: cost:0.0034653181210160255 | acc:0.5945999622344971
Epoch 149, CIFAR-10 Batch 3: cost:0.0044908481650054455 | acc:0.5733999609947205
Epoch 149, CIFAR-10 Batch 4: cost:0.006305835675448179 | acc:0.5935999155044556
Epoch 149, CIFAR-10 Batch 5: cost:0.0069464268162846565 | acc:0.5895999073982239
Epoch 150, CIFAR-10 Batch 1: cost:0.002163697499781847 | acc:0.5891999006271362
Epoch 150, CIFAR-10 Batch 2: cost:0.0036534774117171764 | acc:0.5929999351501465
Epoch 150, CIFAR-10 Batch 3: cost:0.003486006986349821 | acc:0.5881999731063843
Epoch 150, CIFAR-10 Batch 4: cost:0.004222624469548464 | acc:0.5913999080657959
Epoch 150, CIFAR-10 Batch 5: cost:0.0034273110795766115 | acc:0.587399959564209
Epoch 151, CIFAR-10 Batch 1: cost:0.00578007148578763 | acc:0.5915998816490173
Epoch 151, CIFAR-10 Batch 2: cost:0.006716208532452583 | acc:0.5929998755455017
Epoch 151, CIFAR-10 Batch 3: cost:0.003181985579431057 | acc:0.5847999453544617
Epoch 151, CIFAR-10 Batch 4: cost:0.003510464681312442 | acc:0.5869999527931213
Epoch 151, CIFAR-10 Batch 5: cost:0.004195389337837696 | acc:0.5883998870849609
Epoch 152, CIFAR-10 Batch 1: cost:0.004544366151094437 | acc:0.5929999351501465
Epoch 152, CIFAR-10 Batch 2: cost:0.004143828526139259 | acc:0.5891999006271362
Epoch 152, CIFAR-10 Batch 3: cost:0.003528652945533395 | acc:0.5801998972892761
Epoch 152, CIFAR-10 Batch 4: cost:0.005667449906468391 | acc:0.598599910736084
Epoch 152, CIFAR-10 Batch 5: cost:0.003048188053071499 | acc:0.5841999053955078
Epoch 153, CIFAR-10 Batch 1: cost:0.00652969628572464 | acc:0.5889999270439148
Epoch 153, CIFAR-10 Batch 2: cost:0.005262355785816908 | acc:0.5901999473571777
Epoch 153, CIFAR-10 Batch 3: cost:0.0017017337959259748 | acc:0.5871999263763428
Epoch 153, CIFAR-10 Batch 4: cost:0.0033409795723855495 | acc:0.5951999425888062
Epoch 153, CIFAR-10 Batch 5: cost:0.005290153436362743 | acc:0.5917999148368835
Epoch 154, CIFAR-10 Batch 1: cost:0.007378093432635069 | acc:0.585599958896637
Epoch 154, CIFAR-10 Batch 2: cost:0.0017605568282306194 | acc:0.5827999114990234
Epoch 154, CIFAR-10 Batch 3: cost:0.0015793700003996491 | acc:0.5875999331474304
Epoch 154, CIFAR-10 Batch 4: cost:0.005978846922516823 | acc:0.597599983215332
Epoch 154, CIFAR-10 Batch 5: cost:0.00820129457861185 | acc:0.584399938583374
Epoch 155, CIFAR-10 Batch 1: cost:0.0021983610931783915 | acc:0.5897999405860901
Epoch 155, CIFAR-10 Batch 2: cost:0.0027173501439392567 | acc:0.5909999012947083
Epoch 155, CIFAR-10 Batch 3: cost:0.002001794520765543 | acc:0.578999936580658
Epoch 155, CIFAR-10 Batch 4: cost:0.005986413918435574 | acc:0.5927999019622803
Epoch 155, CIFAR-10 Batch 5: cost:0.00532032735645771 | acc:0.5865999460220337
Epoch 156, CIFAR-10 Batch 1: cost:0.0035457818303257227 | acc:0.5865999460220337
Epoch 156, CIFAR-10 Batch 2: cost:0.004998227581381798 | acc:0.5937999486923218
Epoch 156, CIFAR-10 Batch 3: cost:0.006086322478950024 | acc:0.5803998708724976
Epoch 156, CIFAR-10 Batch 4: cost:0.0030219387263059616 | acc:0.5979999303817749
Epoch 156, CIFAR-10 Batch 5: cost:0.006522551644593477 | acc:0.5865998864173889
Epoch 157, CIFAR-10 Batch 1: cost:0.0019610761664807796 | acc:0.5953999161720276
Epoch 157, CIFAR-10 Batch 2: cost:0.0030452399514615536 | acc:0.5889999270439148
Epoch 157, CIFAR-10 Batch 3: cost:0.0034750953782349825 | acc:0.5767999887466431
Epoch 157, CIFAR-10 Batch 4: cost:0.0056857517920434475 | acc:0.5893999338150024
Epoch 157, CIFAR-10 Batch 5: cost:0.004057689569890499 | acc:0.5853999853134155
Epoch 158, CIFAR-10 Batch 1: cost:0.0031953458674252033 | acc:0.5905998945236206
Epoch 158, CIFAR-10 Batch 2: cost:0.002541243564337492 | acc:0.5913999080657959
Epoch 158, CIFAR-10 Batch 3: cost:0.0058631375432014465 | acc:0.5769999027252197
Epoch 158, CIFAR-10 Batch 4: cost:0.0073189083486795425 | acc:0.5887998938560486
Epoch 158, CIFAR-10 Batch 5: cost:0.004513464868068695 | acc:0.5799999237060547
Epoch 159, CIFAR-10 Batch 1: cost:0.0032955624628812075 | acc:0.5947998762130737
Epoch 159, CIFAR-10 Batch 2: cost:0.0030983511824160814 | acc:0.5921999216079712
Epoch 159, CIFAR-10 Batch 3: cost:0.003210693597793579 | acc:0.5771998763084412
Epoch 159, CIFAR-10 Batch 4: cost:0.0025016069412231445 | acc:0.584399938583374
Epoch 159, CIFAR-10 Batch 5: cost:0.0019953520968556404 | acc:0.5883999466896057
Epoch 160, CIFAR-10 Batch 1: cost:0.008361046202480793 | acc:0.5881999135017395
Epoch 160, CIFAR-10 Batch 2: cost:0.0028032585978507996 | acc:0.5909999012947083
Epoch 160, CIFAR-10 Batch 3: cost:0.004701044876128435 | acc:0.5777999758720398
Epoch 160, CIFAR-10 Batch 4: cost:0.0017637326382100582 | acc:0.5871999263763428
Epoch 160, CIFAR-10 Batch 5: cost:0.0010940789943560958 | acc:0.5929999351501465
Epoch 161, CIFAR-10 Batch 1: cost:0.0048188213258981705 | acc:0.5875998735427856
Epoch 161, CIFAR-10 Batch 2: cost:0.0025552520528435707 | acc:0.5909999012947083
Epoch 161, CIFAR-10 Batch 3: cost:0.0021278916392475367 | acc:0.5791999101638794
Epoch 161, CIFAR-10 Batch 4: cost:0.004601106978952885 | acc:0.5939999222755432
Epoch 161, CIFAR-10 Batch 5: cost:0.002020367421209812 | acc:0.5949999094009399
Epoch 162, CIFAR-10 Batch 1: cost:0.005893146153539419 | acc:0.5995998978614807
Epoch 162, CIFAR-10 Batch 2: cost:0.004695297218859196 | acc:0.5913999080657959
Epoch 162, CIFAR-10 Batch 3: cost:0.002250648569315672 | acc:0.5819998979568481
Epoch 162, CIFAR-10 Batch 4: cost:0.003446030430495739 | acc:0.5899999141693115
Epoch 162, CIFAR-10 Batch 5: cost:0.006063036620616913 | acc:0.596799910068512
Epoch 163, CIFAR-10 Batch 1: cost:0.0026331471744924784 | acc:0.591999888420105
Epoch 163, CIFAR-10 Batch 2: cost:0.0050092278979718685 | acc:0.5827999114990234
Epoch 163, CIFAR-10 Batch 3: cost:0.0017691574757918715 | acc:0.5805999040603638
Epoch 163, CIFAR-10 Batch 4: cost:0.0019607774447649717 | acc:0.5903998613357544
Epoch 163, CIFAR-10 Batch 5: cost:0.002484724624082446 | acc:0.5875998735427856
Epoch 164, CIFAR-10 Batch 1: cost:0.001095346175134182 | acc:0.5933999419212341
Epoch 164, CIFAR-10 Batch 2: cost:0.003234606934711337 | acc:0.5877999067306519
Epoch 164, CIFAR-10 Batch 3: cost:0.0042334310710430145 | acc:0.5821999311447144
Epoch 164, CIFAR-10 Batch 4: cost:0.004664713516831398 | acc:0.5947998762130737
Epoch 164, CIFAR-10 Batch 5: cost:0.0039016588125377893 | acc:0.5867999196052551
Epoch 165, CIFAR-10 Batch 1: cost:0.004603811074048281 | acc:0.5959999561309814
Epoch 165, CIFAR-10 Batch 2: cost:0.0024916843976825476 | acc:0.5857998728752136
Epoch 165, CIFAR-10 Batch 3: cost:0.0022717048414051533 | acc:0.5823999047279358
Epoch 165, CIFAR-10 Batch 4: cost:0.002693507354706526 | acc:0.5929999351501465
Epoch 165, CIFAR-10 Batch 5: cost:0.0054621221497654915 | acc:0.5899998545646667
Epoch 166, CIFAR-10 Batch 1: cost:0.0014776393072679639 | acc:0.5979999303817749
Epoch 166, CIFAR-10 Batch 2: cost:0.002303285989910364 | acc:0.5881999731063843
Epoch 166, CIFAR-10 Batch 3: cost:0.0013116791378706694 | acc:0.5817999839782715
Epoch 166, CIFAR-10 Batch 4: cost:0.0025458848103880882 | acc:0.5973998308181763
Epoch 166, CIFAR-10 Batch 5: cost:0.0021585316862910986 | acc:0.5915999412536621
Epoch 167, CIFAR-10 Batch 1: cost:0.0012781365076079965 | acc:0.5945999026298523
Epoch 167, CIFAR-10 Batch 2: cost:0.004220201168209314 | acc:0.5881999135017395
Epoch 167, CIFAR-10 Batch 3: cost:0.0020331901032477617 | acc:0.5899999141693115
Epoch 167, CIFAR-10 Batch 4: cost:0.005124465562403202 | acc:0.5879999399185181
Epoch 167, CIFAR-10 Batch 5: cost:0.001508550369180739 | acc:0.5956000089645386
Epoch 168, CIFAR-10 Batch 1: cost:0.003659695852547884 | acc:0.5957998633384705
Epoch 168, CIFAR-10 Batch 2: cost:0.0016722658183425665 | acc:0.5883999466896057
Epoch 168, CIFAR-10 Batch 3: cost:0.0026774185243993998 | acc:0.5787999033927917
Epoch 168, CIFAR-10 Batch 4: cost:0.003082968294620514 | acc:0.5793999433517456
Epoch 168, CIFAR-10 Batch 5: cost:0.0016995224868878722 | acc:0.5869998931884766
Epoch 169, CIFAR-10 Batch 1: cost:0.005290893372148275 | acc:0.597000002861023
Epoch 169, CIFAR-10 Batch 2: cost:0.004347043111920357 | acc:0.5797998309135437
Epoch 169, CIFAR-10 Batch 3: cost:0.0011173388920724392 | acc:0.5831999182701111
Epoch 169, CIFAR-10 Batch 4: cost:0.0024250298738479614 | acc:0.5895999073982239
Epoch 169, CIFAR-10 Batch 5: cost:0.002587332855910063 | acc:0.5869999527931213
Epoch 170, CIFAR-10 Batch 1: cost:0.002960028126835823 | acc:0.5951999425888062
Epoch 170, CIFAR-10 Batch 2: cost:0.0036679357290267944 | acc:0.582599937915802
Epoch 170, CIFAR-10 Batch 3: cost:0.00186528405174613 | acc:0.5839999318122864
Epoch 170, CIFAR-10 Batch 4: cost:0.002819312736392021 | acc:0.5847999453544617
Epoch 170, CIFAR-10 Batch 5: cost:0.0013859050814062357 | acc:0.5905999541282654
Epoch 171, CIFAR-10 Batch 1: cost:0.003849351080134511 | acc:0.5893999338150024
Epoch 171, CIFAR-10 Batch 2: cost:0.005004320293664932 | acc:0.5857998728752136
Epoch 171, CIFAR-10 Batch 3: cost:0.002010798081755638 | acc:0.5849999189376831
Epoch 171, CIFAR-10 Batch 4: cost:0.0027558165602385998 | acc:0.5839999318122864
Epoch 171, CIFAR-10 Batch 5: cost:0.001281309756450355 | acc:0.5911998748779297
Epoch 172, CIFAR-10 Batch 1: cost:0.003969934768974781 | acc:0.5879999399185181
Epoch 172, CIFAR-10 Batch 2: cost:0.0035437738988548517 | acc:0.5823999643325806
Epoch 172, CIFAR-10 Batch 3: cost:0.003051359672099352 | acc:0.5815998911857605
Epoch 172, CIFAR-10 Batch 4: cost:0.002574923448264599 | acc:0.5995999574661255
Epoch 172, CIFAR-10 Batch 5: cost:0.0009188731200993061 | acc:0.5979999303817749
Epoch 173, CIFAR-10 Batch 1: cost:0.0017441078089177608 | acc:0.5983998775482178
Epoch 173, CIFAR-10 Batch 2: cost:0.0010580007219687104 | acc:0.5905998945236206
Epoch 173, CIFAR-10 Batch 3: cost:0.004111346788704395 | acc:0.5877999663352966
Epoch 173, CIFAR-10 Batch 4: cost:0.001289262087084353 | acc:0.5881998538970947
Epoch 173, CIFAR-10 Batch 5: cost:0.0012922302121296525 | acc:0.5951999425888062
Epoch 174, CIFAR-10 Batch 1: cost:0.00236637145280838 | acc:0.5871999263763428
Epoch 174, CIFAR-10 Batch 2: cost:0.0025332486256957054 | acc:0.5875998735427856
Epoch 174, CIFAR-10 Batch 3: cost:0.0033152196556329727 | acc:0.5755999088287354
Epoch 174, CIFAR-10 Batch 4: cost:0.0026295813731849194 | acc:0.5905999541282654
Epoch 174, CIFAR-10 Batch 5: cost:0.0013940732460469007 | acc:0.5855998992919922
Epoch 175, CIFAR-10 Batch 1: cost:0.0019647344015538692 | acc:0.5969998836517334
Epoch 175, CIFAR-10 Batch 2: cost:0.002892123069614172 | acc:0.5881998538970947
Epoch 175, CIFAR-10 Batch 3: cost:0.003665775991976261 | acc:0.5841999053955078
Epoch 175, CIFAR-10 Batch 4: cost:0.0027143354527652264 | acc:0.5873998403549194
Epoch 175, CIFAR-10 Batch 5: cost:0.0015040256548672915 | acc:0.5919999480247498
Epoch 176, CIFAR-10 Batch 1: cost:0.001209259731695056 | acc:0.5953999161720276
Epoch 176, CIFAR-10 Batch 2: cost:0.0023053428158164024 | acc:0.58899986743927
Epoch 176, CIFAR-10 Batch 3: cost:0.00782364048063755 | acc:0.5809998512268066
Epoch 176, CIFAR-10 Batch 4: cost:0.0024470302741974592 | acc:0.5871999263763428
Epoch 176, CIFAR-10 Batch 5: cost:0.0006596133462153375 | acc:0.5937999486923218
Epoch 177, CIFAR-10 Batch 1: cost:0.0020671451929956675 | acc:0.5883999466896057
Epoch 177, CIFAR-10 Batch 2: cost:0.0028007635846734047 | acc:0.577799916267395
Epoch 177, CIFAR-10 Batch 3: cost:0.0029198278207331896 | acc:0.5837998986244202
Epoch 177, CIFAR-10 Batch 4: cost:0.0021473951637744904 | acc:0.5919999480247498
Epoch 177, CIFAR-10 Batch 5: cost:0.0026729386299848557 | acc:0.5917998552322388
Epoch 178, CIFAR-10 Batch 1: cost:0.002374910283833742 | acc:0.5947999358177185
Epoch 178, CIFAR-10 Batch 2: cost:0.002365794498473406 | acc:0.5829998850822449
Epoch 178, CIFAR-10 Batch 3: cost:0.0025616325438022614 | acc:0.577799916267395
Epoch 178, CIFAR-10 Batch 4: cost:0.0010168832959607244 | acc:0.5845999717712402
Epoch 178, CIFAR-10 Batch 5: cost:0.0012665912508964539 | acc:0.5883999466896057
Epoch 179, CIFAR-10 Batch 1: cost:0.002044407883659005 | acc:0.5925998687744141
Epoch 179, CIFAR-10 Batch 2: cost:0.0008532085921615362 | acc:0.5883999466896057
Epoch 179, CIFAR-10 Batch 3: cost:0.002931073075160384 | acc:0.5849999189376831
Epoch 179, CIFAR-10 Batch 4: cost:0.001877293805591762 | acc:0.5965999364852905
Epoch 179, CIFAR-10 Batch 5: cost:0.0014083775458857417 | acc:0.5829999446868896
Epoch 180, CIFAR-10 Batch 1: cost:0.0026591899804770947 | acc:0.5923998951911926
Epoch 180, CIFAR-10 Batch 2: cost:0.004308641888201237 | acc:0.587399959564209
Epoch 180, CIFAR-10 Batch 3: cost:0.0016423638444393873 | acc:0.5831999182701111
Epoch 180, CIFAR-10 Batch 4: cost:0.0014873958425596356 | acc:0.5939999222755432
Epoch 180, CIFAR-10 Batch 5: cost:0.0028726349119096994 | acc:0.5879999399185181
Epoch 181, CIFAR-10 Batch 1: cost:0.0031776237301528454 | acc:0.5905999541282654
Epoch 181, CIFAR-10 Batch 2: cost:0.002730556530877948 | acc:0.5915999412536621
Epoch 181, CIFAR-10 Batch 3: cost:0.0032906080596148968 | acc:0.587399959564209
Epoch 181, CIFAR-10 Batch 4: cost:0.0008295221487060189 | acc:0.5961998701095581
Epoch 181, CIFAR-10 Batch 5: cost:0.0015765596181154251 | acc:0.5977998971939087
Epoch 182, CIFAR-10 Batch 1: cost:0.0014299846952781081 | acc:0.595599889755249
Epoch 182, CIFAR-10 Batch 2: cost:0.001617702771909535 | acc:0.5835999250411987
Epoch 182, CIFAR-10 Batch 3: cost:0.0008789539569988847 | acc:0.5833999514579773
Epoch 182, CIFAR-10 Batch 4: cost:0.0020180060528218746 | acc:0.5951998829841614
Epoch 182, CIFAR-10 Batch 5: cost:0.0035984781570732594 | acc:0.5867999196052551
Epoch 183, CIFAR-10 Batch 1: cost:0.001713088247925043 | acc:0.5811998844146729
Epoch 183, CIFAR-10 Batch 2: cost:0.00250391592271626 | acc:0.5849999189376831
Epoch 183, CIFAR-10 Batch 3: cost:0.0023977153468877077 | acc:0.5819999575614929
Epoch 183, CIFAR-10 Batch 4: cost:0.002003519330173731 | acc:0.5885998606681824
Epoch 183, CIFAR-10 Batch 5: cost:0.0008944513974711299 | acc:0.5879999399185181
Epoch 184, CIFAR-10 Batch 1: cost:0.0015011532232165337 | acc:0.5965999364852905
Epoch 184, CIFAR-10 Batch 2: cost:0.00139282934833318 | acc:0.5865999460220337
Epoch 184, CIFAR-10 Batch 3: cost:0.0016276331152766943 | acc:0.5915999412536621
Epoch 184, CIFAR-10 Batch 4: cost:0.002687581581994891 | acc:0.5935999155044556
Epoch 184, CIFAR-10 Batch 5: cost:0.0019958766642957926 | acc:0.5929999351501465
Epoch 185, CIFAR-10 Batch 1: cost:0.0015072855167090893 | acc:0.5911999344825745
Epoch 185, CIFAR-10 Batch 2: cost:0.005085612181574106 | acc:0.5921999216079712
Epoch 185, CIFAR-10 Batch 3: cost:0.003091041464358568 | acc:0.5779999494552612
Epoch 185, CIFAR-10 Batch 4: cost:0.0032528871670365334 | acc:0.590399980545044
Epoch 185, CIFAR-10 Batch 5: cost:0.0034262428525835276 | acc:0.5917999148368835
Epoch 186, CIFAR-10 Batch 1: cost:0.0023408792912960052 | acc:0.6049998998641968
cost:0.0010993832256644964 | acc:0.5959999561309814
Epoch 191, CIFAR-10 Batch 1: cost:0.0009519937448203564 | acc:0.586199939250946
Epoch 191, CIFAR-10 Batch 2: cost:0.005502348765730858 | acc:0.5921999216079712
Epoch 191, CIFAR-10 Batch 3: cost:0.00044750882079824805 | acc:0.5881998538970947
Epoch 191, CIFAR-10 Batch 4: cost:0.004760732874274254 | acc:0.5831999182701111
Epoch 191, CIFAR-10 Batch 5: cost:0.0011914231581613421 | acc:0.5837998986244202
Epoch 192, CIFAR-10 Batch 1: cost:0.0013515133177861571 | acc:0.5949999690055847
Epoch 192, CIFAR-10 Batch 2: cost:0.0020732118282467127 | acc:0.5987999439239502
Epoch 192, CIFAR-10 Batch 3: cost:0.0021655571181327105 | acc:0.5877999067306519
Epoch 192, CIFAR-10 Batch 4: cost:0.0022676691878587008 | acc:0.5975999236106873
Epoch 192, CIFAR-10 Batch 5: cost:0.0014569045742973685 | acc:0.5971999168395996
Epoch 193, CIFAR-10 Batch 1: cost:0.0006772954948246479 | acc:0.597000002861023
Epoch 193, CIFAR-10 Batch 2: cost:0.0030321618542075157 | acc:0.5957999229431152
Epoch 193, CIFAR-10 Batch 3: cost:0.0006745931459590793 | acc:0.590799868106842
Epoch 193, CIFAR-10 Batch 4: cost:0.001945807016454637 | acc:0.5895999670028687
Epoch 193, CIFAR-10 Batch 5: cost:0.0023829389829188585 | acc:0.5933998823165894
Epoch 194, CIFAR-10 Batch 1: cost:0.0022376845590770245 | acc:0.5975998640060425
Epoch 194, CIFAR-10 Batch 2: cost:0.0032027028501033783 | acc:0.5949999094009399
Epoch 194, CIFAR-10 Batch 3: cost:0.0012894276296719909 | acc:0.5917999148368835
Epoch 194, CIFAR-10 Batch 4: cost:0.0010931524448096752 | acc:0.5921999216079712
Epoch 194, CIFAR-10 Batch 5: cost:0.0008211819804273546 | acc:0.5959999561309814
Epoch 195, CIFAR-10 Batch 1: cost:0.0028309780173003674 | acc:0.5949999094009399
Epoch 195, CIFAR-10 Batch 2: cost:0.004944032058119774 | acc:0.592799961566925
Epoch 195, CIFAR-10 Batch 3: cost:0.0009008756605908275 | acc:0.5899999141693115
Epoch 195, CIFAR-10 Batch 4: cost:0.0016938120825216174 | acc:0.5835999250411987
Epoch 195, CIFAR-10 Batch 5: cost:0.0007995467749424279 | acc:0.586199939250946
Epoch 196, CIFAR-10 Batch 1: cost:0.001255123526789248 | acc:0.5959999561309814
Epoch 196, CIFAR-10 Batch 2: cost:0.004133785143494606 | acc:0.5911998748779297
Epoch 196, CIFAR-10 Batch 3: cost:0.001087382435798645 | acc:0.5885999202728271
Epoch 196, CIFAR-10 Batch 4: cost:0.0008657622965984046 | acc:0.5899999141693115
Epoch 196, CIFAR-10 Batch 5: cost:0.0011997512774541974 | acc:0.5961999297142029
Epoch 197, CIFAR-10 Batch 1: cost:0.0023749670945107937 | acc:0.5953999161720276
Epoch 197, CIFAR-10 Batch 2: cost:0.00750460010021925 | acc:0.5901999473571777
Epoch 197, CIFAR-10 Batch 3: cost:0.0004089319263584912 | acc:0.5917999744415283
Epoch 197, CIFAR-10 Batch 4: cost:0.001985106850042939 | acc:0.5895999073982239
Epoch 197, CIFAR-10 Batch 5: cost:0.0011373681481927633 | acc:0.5943998694419861
Epoch 198, CIFAR-10 Batch 1: cost:0.0013803449692204595 | acc:0.5921999216079712
Epoch 198, CIFAR-10 Batch 2: cost:0.003028708044439554 | acc:0.5961998701095581
Epoch 198, CIFAR-10 Batch 3: cost:0.0003604758530855179 | acc:0.5853999257087708
Epoch 198, CIFAR-10 Batch 4: cost:0.0017897863872349262 | acc:0.5801999568939209
Epoch 198, CIFAR-10 Batch 5: cost:0.001396165695041418 | acc:0.5933999419212341
Epoch 199, CIFAR-10 Batch 1: cost:0.0026484536938369274 | acc:0.5983998775482178
Epoch 199, CIFAR-10 Batch 2: cost:0.002471786690875888 | acc:0.5953999161720276
Epoch 199, CIFAR-10 Batch 3: cost:0.00033311411971226335 | acc:0.5935999155044556
Epoch 199, CIFAR-10 Batch 4: cost:0.001422425964847207 | acc:0.5951999425888062
Epoch 199, CIFAR-10 Batch 5: cost:0.0010621288092806935 | acc:0.5915999412536621
Epoch 200, CIFAR-10 Batch 1: cost:0.001419935841113329 | acc:0.5939999222755432
Epoch 200, CIFAR-10 Batch 2: cost:0.003096450585871935 | acc:0.5945999026298523
Epoch 200, CIFAR-10 Batch 3: cost:0.00040637690108269453 | acc:0.5811998844146729
Epoch 200, CIFAR-10 Batch 4: cost:0.0011811762815341353 | acc:0.5823999047279358
Epoch 200, CIFAR-10 Batch 5: cost:0.0006032247911207378 | acc:0.5947999358177185
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<h1 id="Checkpoint">Checkpoint<a class="anchor-link" href="#Checkpoint">&#182;</a></h1><p>The model has been saved to disk.</p>
<h2 id="Test-Model">Test Model<a class="anchor-link" href="#Test-Model">&#182;</a></h2><p>Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">DON&#39;T MODIFY ANYTHING IN THIS CELL</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39;
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">helper</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="c1"># Set batch size if not already set</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">if</span> <span class="n">batch_size</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">save_model_path</span> <span class="o">=</span> <span class="s1">&#39;./image_classification&#39;</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">top_n_predictions</span> <span class="o">=</span> <span class="mi">3</span>
<span class="k">def</span> <span class="nf">test_model</span><span class="p">():</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Test the saved model against the test dataset</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;preprocess_training.p&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">))</span>
<span class="n">loaded_graph</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</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="n">graph</span><span class="o">=</span><span class="n">loaded_graph</span><span class="p">)</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
<span class="c1"># Load model</span>
<span class="n">loader</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">import_meta_graph</span><span class="p">(</span><span class="n">save_model_path</span> <span class="o">+</span> <span class="s1">&#39;.meta&#39;</span><span class="p">)</span>
<span class="n">loader</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">save_model_path</span><span class="p">)</span>
<span class="c1"># Get Tensors from loaded model</span>
<span class="n">loaded_x</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;x:0&#39;</span><span class="p">)</span>
<span class="n">loaded_y</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;y:0&#39;</span><span class="p">)</span>
<span class="n">loaded_keep_prob</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;keep_prob:0&#39;</span><span class="p">)</span>
<span class="n">loaded_logits</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;logits:0&#39;</span><span class="p">)</span>
<span class="n">loaded_acc</span> <span class="o">=</span> <span class="n">loaded_graph</span><span class="o">.</span><span class="n">get_tensor_by_name</span><span class="p">(</span><span class="s1">&#39;accuracy:0&#39;</span><span class="p">)</span>
<span class="c1"># Get accuracy in batches for memory limitations</span>
<span class="n">test_batch_acc_total</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">test_batch_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">train_feature_batch</span><span class="p">,</span> <span class="n">train_label_batch</span> <span class="ow">in</span> <span class="n">helper</span><span class="o">.</span><span class="n">batch_features_labels</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
<span class="n">test_batch_acc_total</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">loaded_acc</span><span class="p">,</span>
<span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">train_feature_batch</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">train_label_batch</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
<span class="n">test_batch_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Testing Accuracy: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_batch_acc_total</span><span class="o">/</span><span class="n">test_batch_count</span><span class="p">))</span>
<span class="c1"># Print Random Samples</span>
<span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">test_features</span><span class="p">,</span> <span class="n">test_labels</span><span class="p">)),</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="n">random_test_predictions</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">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">loaded_logits</span><span class="p">),</span> <span class="n">top_n_predictions</span><span class="p">),</span>
<span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">loaded_x</span><span class="p">:</span> <span class="n">random_test_features</span><span class="p">,</span> <span class="n">loaded_y</span><span class="p">:</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">loaded_keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
<span class="n">helper</span><span class="o">.</span><span class="n">display_image_predictions</span><span class="p">(</span><span class="n">random_test_features</span><span class="p">,</span> <span class="n">random_test_labels</span><span class="p">,</span> <span class="n">random_test_predictions</span><span class="p">)</span>
<span class="n">test_model</span><span class="p">()</span>
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<pre>Testing Accuracy: 0.5921875
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<h2 id="Why-50-70%-Accuracy?">Why 50-70% Accuracy?<a class="anchor-link" href="#Why-50-70%-Accuracy?">&#182;</a></h2><p>You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores <a href="http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130">well above 70%</a>. That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.</p>
<h2 id="Submitting-This-Project">Submitting This Project<a class="anchor-link" href="#Submitting-This-Project">&#182;</a></h2><p>When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.ipynb" and save it as a HTML file under "File" -&gt; "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.</p>
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