Domestic Audio Classification (Keras).html

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<title>Domestic Audio Classification (Keras)</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">cd</span> H:/SINS dataset
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<pre>H:\SINS dataset
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">seed</span> <span class="o">=</span> <span class="mi">1356</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">tensorflow</span> <span class="k">import</span> <span class="n">set_random_seed</span>
<span class="n">set_random_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">sklearn</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">datetime</span>

<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">chain</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">f1_score</span>

<span class="c1"># constants</span>
<span class="n">img_folder</span> <span class="o">=</span> <span class="s1">&#39;audio&#39;</span>
<span class="n">img_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;_pressure.png&#39;</span><span class="p">,</span> <span class="s1">&#39;_spec1.png&#39;</span><span class="p">,</span> <span class="s1">&#39;_spec2.png&#39;</span><span class="p">,</span> <span class="s1">&#39;_spec3.png&#39;</span><span class="p">,</span> <span class="s1">&#39;.wav&#39;</span><span class="p">]</span>

<span class="n">im_size</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">im_size_flat</span> <span class="o">=</span> <span class="n">im_size</span> <span class="o">*</span> <span class="n">im_size</span>
<span class="n">n_labels</span> <span class="o">=</span> <span class="mi">9</span>
<span class="n">n_channels</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># grayscale</span>
<span class="n">sd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">im_size_flat</span><span class="p">)</span>

<span class="n">req_improve</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">update_cnt</span> <span class="o">=</span> <span class="mi">50</span>
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<pre>c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">Conv1D</span><span class="p">,</span> <span class="n">MaxPooling1D</span><span class="p">,</span> <span class="n">LSTM</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">np_utils</span>
<span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">optimizers</span>
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<pre>Using TensorFlow backend.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">librosa</span>

<span class="n">afs</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;meta.txt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()])</span>
</pre></div>

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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_folds</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">4</span><span class="p">):</span>
    <span class="n">afs</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;meta.txt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()])</span>
    <span class="n">afs_imlinks</span> <span class="o">=</span> <span class="p">[</span><span class="n">img_folder</span> <span class="o">+</span> <span class="n">af</span><span class="p">[</span><span class="mi">5</span><span class="p">:</span><span class="o">-</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="n">img_name</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="k">for</span> <span class="n">af</span> <span class="ow">in</span> <span class="n">afs</span><span class="p">]</span>
        
    <span class="c1"># get smallest no. of classes</span>
    <span class="n">mincnt</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">most_common</span><span class="p">()[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
    
    <span class="n">afs_label_sep</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="p">(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">afs_imlinks</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">afs_label_sep</span><span class="p">:</span>
            <span class="n">afs_label_sep</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">af</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">afs_label_sep</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">af</span><span class="p">]</span>

    <span class="n">afs_label_sep_sampled</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">afs_label_sep</span><span class="p">[</span><span class="n">label</span><span class="p">],</span> <span class="n">mincnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">afs_label_sep</span><span class="o">.</span><span class="n">keys</span><span class="p">())}</span>
    
    <span class="c1"># partition the graph into n_folds partitions for cross-validation</span>
    <span class="n">folds</span> <span class="o">=</span> <span class="p">{</span><span class="n">fold</span><span class="o">+</span><span class="mi">1</span><span class="p">:[]</span> <span class="k">for</span> <span class="n">fold</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_folds</span><span class="p">)}</span>
    <span class="n">samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">mincnt</span> <span class="o">/</span> <span class="n">n_folds</span><span class="p">))</span>

    <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">afs_label_sep_sampled</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
        <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">afs_label_sep_sampled</span><span class="p">[</span><span class="n">label</span><span class="p">])</span>
        <span class="k">for</span> <span class="n">fold</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_folds</span><span class="p">):</span>
            <span class="n">folds</span><span class="p">[</span><span class="n">fold</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="p">[(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="k">for</span> <span class="n">af</span> <span class="ow">in</span> <span class="n">afs_label_sep_sampled</span><span class="p">[</span><span class="n">label</span><span class="p">][</span><span class="n">fold</span><span class="o">*</span><span class="n">samples</span><span class="p">:(</span><span class="n">fold</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">samples</span><span class="p">]]</span>

    <span class="k">return</span> <span class="nb">list</span><span class="p">([</span><span class="n">folds</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">folds</span><span class="o">.</span><span class="n">keys</span><span class="p">())])</span>
<span class="c1">#     return list(folds.values())</span>

<span class="c1"># 0-indexed get training/testing sets</span>
<span class="k">def</span> <span class="nf">get_sets</span><span class="p">(</span><span class="n">folds</span><span class="p">,</span> <span class="n">fold</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
    <span class="n">_</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;meta.txt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()])</span>
    <span class="n">sces</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">labels</span><span class="p">)))</span>
    <span class="n">sce_int_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">sce</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sce</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sces</span><span class="p">)}</span>
    <span class="n">int_sce_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">sce_int_map</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">sce_int_map</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>

    <span class="n">ex_fold</span> <span class="o">=</span> <span class="n">folds</span><span class="p">[</span><span class="n">fold</span><span class="p">]</span>
    <span class="n">ot_fold</span> <span class="o">=</span> <span class="n">folds</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">fold</span><span class="p">]</span> <span class="o">+</span> <span class="n">folds</span><span class="p">[</span><span class="n">fold</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span>
    <span class="n">chainfold</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">ot_fold</span><span class="p">))</span>
    <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ex_fold</span><span class="p">)</span>
    <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">chainfold</span><span class="p">)</span>
    <span class="n">trX</span><span class="p">,</span> <span class="n">trY</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="n">chainfold</span><span class="p">])</span>
    <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">af</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="n">ex_fold</span><span class="p">])</span>
        
    <span class="n">trY</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="mi">1</span> <span class="k">if</span> <span class="n">int_sce_map</span><span class="p">[</span><span class="n">c</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">label</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sces</span><span class="p">))]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">trY</span><span class="p">])</span>
    <span class="n">tY</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="mi">1</span> <span class="k">if</span> <span class="n">int_sce_map</span><span class="p">[</span><span class="n">c</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">label</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sces</span><span class="p">))]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">tY</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="p">(</span><span class="n">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">,</span> <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span><span class="p">)</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>

<span class="n">mfccs</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="s2">&quot;mfccs.pickle&quot;</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">))</span>
</pre></div>

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</div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_ims</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">link</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">im_size</span><span class="p">,</span> <span class="n">im_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">links</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">get_wav</span><span class="p">(</span><span class="n">links</span><span class="p">):</span>
    <span class="n">temp</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">link</span> <span class="ow">in</span> <span class="n">links</span><span class="p">:</span>
<span class="c1">#         y, sr = librosa.load(link, sr = None, mono = True)</span>
<span class="c1">#         temp.append(librosa.feature.mfcc(y=y,sr=sr).T)</span>
        <span class="n">temp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfccs</span><span class="p">[</span><span class="n">link</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span>
</pre></div>

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</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">data_loader</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
    <span class="n">L</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">)</span>
    <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
        <span class="n">batch_start</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">batch_end</span> <span class="o">=</span> <span class="n">batch_size</span>
        
        <span class="k">while</span> <span class="n">batch_start</span> <span class="o">&lt;</span> <span class="n">L</span><span class="p">:</span>
            <span class="n">lim</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="n">batch_end</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">file_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">X</span> <span class="o">=</span> <span class="n">get_ims</span><span class="p">([</span><span class="n">f</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">[</span><span class="n">batch_start</span><span class="p">:</span><span class="n">lim</span><span class="p">]])</span>
                <span class="n">Y</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">f</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">[</span><span class="n">batch_start</span><span class="p">:</span><span class="n">lim</span><span class="p">]])</span>
                <span class="k">yield</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
            <span class="k">elif</span> <span class="n">file_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="c1"># wav files</span>
                <span class="n">X</span> <span class="o">=</span> <span class="n">get_wav</span><span class="p">([</span><span class="n">f</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">[</span><span class="n">batch_start</span><span class="p">:</span><span class="n">lim</span><span class="p">]])</span>
                <span class="n">Y</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">f</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">[</span><span class="n">batch_start</span><span class="p">:</span><span class="n">lim</span><span class="p">]])</span>
                <span class="k">yield</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
            <span class="n">batch_start</span> <span class="o">+=</span> <span class="n">batch_size</span>
            <span class="n">batch_end</span> <span class="o">+=</span> <span class="n">batch_size</span>
</pre></div>

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</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_model1D</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv1D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">313</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv1D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling1D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv1D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">15</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling1D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_labels</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

    <span class="n">adam</span> <span class="o">=</span> <span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">beta_1</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">beta_2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">amsgrad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>

</div>
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</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_model2D_S</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">im_size</span><span class="p">,</span> <span class="n">im_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>  <span class="c1"># this converts our 3D feature maps to 1D feature vectors</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="n">n_labels</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_model2D</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="n">im_size</span><span class="p">,</span> <span class="n">im_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">dilation_rate</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>  <span class="c1"># this converts our 3D feature maps to 1D feature vectors</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="n">n_labels</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_modelLSTM</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">input_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">313</span><span class="p">,</span> <span class="mi">20</span><span class="p">)))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">))</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="n">n_labels</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">make_modelCom</span><span class="p">():</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv1D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">313</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv1D</span><span class="p">(</span><span class="n">filters</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling1D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">return_sequences</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">stateful</span> <span class="o">=</span> <span class="kc">False</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="n">n_labels</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>

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

</div>
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<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">cnn</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">use_model</span><span class="p">,</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sel_fold</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">use_model</span><span class="p">()</span>
    
    <span class="n">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">,</span> <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span> <span class="o">=</span> <span class="n">get_sets</span><span class="p">(</span><span class="n">get_folds</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">n_folds</span><span class="p">),</span> <span class="n">sel_fold</span><span class="p">)</span>
    <span class="n">vsplit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tX</span><span class="p">)</span> <span class="o">*</span> <span class="n">val_split</span><span class="p">)</span>
    <span class="n">val_X</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[:</span><span class="n">vsplit</span><span class="p">]</span>
    <span class="n">val_Y</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[:</span><span class="n">vsplit</span><span class="p">]</span>
    <span class="n">testX</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
    <span class="n">testY</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
                
    <span class="n">batch_per_epoch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    <span class="n">batch_per_val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">val_X</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    <span class="n">batch_per_test</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">testX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    
    <span class="k">if</span> <span class="n">save_model</span><span class="p">:</span>
        <span class="n">filepath</span> <span class="o">=</span> <span class="s1">&#39;models/model_</span><span class="si">{index}</span><span class="s1">.hdf5&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span> <span class="o">=</span> <span class="n">name</span><span class="p">)</span>
        <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">monitor</span> <span class="o">=</span> <span class="s1">&#39;val_acc&#39;</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">save_best_only</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;max&#39;</span><span class="p">)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_loader</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">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">steps_per_epoch</span> <span class="o">=</span> <span class="n">batch_per_epoch</span><span class="p">,</span>
                            <span class="n">epochs</span> <span class="o">=</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">,</span>
                            <span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">checkpoint</span><span class="p">],</span>
                            <span class="n">validation_data</span> <span class="o">=</span> <span class="n">data_loader</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">val_X</span><span class="p">,</span> <span class="n">val_Y</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">validation_steps</span> <span class="o">=</span> <span class="n">batch_per_val</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_loader</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">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">steps_per_epoch</span> <span class="o">=</span> <span class="n">batch_per_epoch</span><span class="p">,</span>
                            <span class="n">epochs</span> <span class="o">=</span> <span class="n">epochs</span><span class="p">,</span>
                            <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">,</span>
                            <span class="n">validation_data</span> <span class="o">=</span> <span class="n">data_loader</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">val_X</span><span class="p">,</span> <span class="n">val_Y</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">validation_steps</span> <span class="o">=</span> <span class="n">batch_per_val</span><span class="p">)</span>
    
    <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">data_loader</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">testX</span><span class="p">,</span> <span class="n">testY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                                    <span class="n">steps</span> <span class="o">=</span> <span class="n">batch_per_test</span><span class="p">,</span>
                                    <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">)</span>
    
    <span class="n">predsT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">trueT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">testY</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{}</span><span class="s2">. F1: </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">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">)),</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">,</span> <span class="n">average</span> <span class="o">=</span> <span class="s1">&#39;macro&#39;</span><span class="p">)))</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">load_model</span>

<span class="k">def</span> <span class="nf">test_model</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sel_fold</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">val_split</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
    
    <span class="n">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">,</span> <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span> <span class="o">=</span> <span class="n">get_sets</span><span class="p">(</span><span class="n">get_folds</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">n_folds</span><span class="p">),</span> <span class="n">sel_fold</span><span class="p">)</span>
    <span class="n">vsplit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tX</span><span class="p">)</span> <span class="o">*</span> <span class="n">val_split</span><span class="p">)</span>
    <span class="n">testX</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
    <span class="n">testY</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
                
    <span class="n">batch_per_test</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">testX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
        
    <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">data_loader</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">testX</span><span class="p">,</span> <span class="n">testY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                                    <span class="n">steps</span> <span class="o">=</span> <span class="n">batch_per_test</span><span class="p">,</span>
                                    <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">)</span>
    
    <span class="n">predsT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">trueT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">testY</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{}</span><span class="s2">. F1: </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">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">)),</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">,</span> <span class="n">average</span> <span class="o">=</span> <span class="s1">&#39;macro&#39;</span><span class="p">)))</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test_model_avg</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">file_types</span><span class="p">,</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sel_fold</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">val_split</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">):</span>
    
    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">file_types</span><span class="p">)):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Weights, models, file_types need to be the same length.&quot;</span><span class="p">)</span>
        <span class="k">return</span>
    <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Sum of weights should equal 1&quot;</span><span class="p">)</span>
        
    <span class="n">preds</span> <span class="o">=</span> <span class="kc">None</span>
    
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">file_type</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">file_types</span><span class="p">):</span>
        
        <span class="k">if</span> <span class="n">weight</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">continue</span>
    
        <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>

        <span class="n">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">,</span> <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span> <span class="o">=</span> <span class="n">get_sets</span><span class="p">(</span><span class="n">get_folds</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">n_folds</span><span class="p">),</span> <span class="n">sel_fold</span><span class="p">)</span>
        <span class="n">vsplit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tX</span><span class="p">)</span> <span class="o">*</span> <span class="n">val_split</span><span class="p">)</span>
        <span class="n">testX</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
        <span class="n">testY</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>

        <span class="n">batch_per_test</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">testX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>

        <span class="k">if</span> <span class="n">preds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">preds</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">data_loader</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">testX</span><span class="p">,</span> <span class="n">testY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                                                     <span class="n">steps</span> <span class="o">=</span> <span class="n">batch_per_test</span><span class="p">,</span>
                                                     <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">preds</span> <span class="o">+=</span> <span class="n">weight</span> <span class="o">*</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">data_loader</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">testX</span><span class="p">,</span> <span class="n">testY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                                                      <span class="n">steps</span> <span class="o">=</span> <span class="n">batch_per_test</span><span class="p">,</span>
                                                      <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">)</span>
    
    <span class="n">predsT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">trueT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">testY</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{}</span><span class="s2">. F1: </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">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">)),</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">,</span> <span class="n">average</span> <span class="o">=</span> <span class="s1">&#39;macro&#39;</span><span class="p">)))</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train_model</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">n_folds</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sel_fold</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">val_split</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
    
    <span class="n">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">,</span> <span class="n">tX</span><span class="p">,</span> <span class="n">tY</span> <span class="o">=</span> <span class="n">get_sets</span><span class="p">(</span><span class="n">get_folds</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">n_folds</span><span class="p">),</span> <span class="n">sel_fold</span><span class="p">)</span>
    <span class="n">vsplit</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tX</span><span class="p">)</span> <span class="o">*</span> <span class="n">val_split</span><span class="p">)</span>
    <span class="n">val_X</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[:</span><span class="n">vsplit</span><span class="p">]</span>
    <span class="n">val_Y</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[:</span><span class="n">vsplit</span><span class="p">]</span>
    <span class="n">testX</span> <span class="o">=</span> <span class="n">tX</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
    <span class="n">testY</span> <span class="o">=</span> <span class="n">tY</span><span class="p">[</span><span class="n">vsplit</span><span class="p">:]</span>
    
    <span class="n">batch_per_epoch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    <span class="n">batch_per_val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">val_X</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    <span class="n">batch_per_test</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">testX</span><span class="p">)</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
    
    <span class="k">if</span> <span class="n">save_model</span><span class="p">:</span>
        <span class="n">filepath</span> <span class="o">=</span> <span class="s1">&#39;models/model_</span><span class="si">{index}</span><span class="s1">.hdf5&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span> <span class="o">=</span> <span class="n">name</span><span class="p">)</span>
        <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">monitor</span> <span class="o">=</span> <span class="s1">&#39;val_acc&#39;</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">save_best_only</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;max&#39;</span><span class="p">)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_loader</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">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">steps_per_epoch</span> <span class="o">=</span> <span class="n">batch_per_epoch</span><span class="p">,</span>
                            <span class="n">epochs</span> <span class="o">=</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">,</span>
                            <span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">checkpoint</span><span class="p">],</span>
                            <span class="n">validation_data</span> <span class="o">=</span> <span class="n">data_loader</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">val_X</span><span class="p">,</span> <span class="n">val_Y</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">validation_steps</span> <span class="o">=</span> <span class="n">batch_per_val</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">data_loader</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">trX</span><span class="p">,</span> <span class="n">trY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">steps_per_epoch</span> <span class="o">=</span> <span class="n">batch_per_epoch</span><span class="p">,</span>
                            <span class="n">epochs</span> <span class="o">=</span> <span class="n">epochs</span><span class="p">,</span>
                            <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">,</span>
                            <span class="n">validation_data</span> <span class="o">=</span> <span class="n">data_loader</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">val_X</span><span class="p">,</span> <span class="n">val_Y</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                            <span class="n">validation_steps</span> <span class="o">=</span> <span class="n">batch_per_val</span><span class="p">)</span>
    
    <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">data_loader</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">testX</span><span class="p">,</span> <span class="n">testY</span><span class="p">)),</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">file_type</span><span class="p">),</span>
                                    <span class="n">steps</span> <span class="o">=</span> <span class="n">batch_per_test</span><span class="p">,</span>
                                    <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">)</span>
    
    <span class="n">predsT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">trueT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">testY</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy: </span><span class="si">{}</span><span class="s2">. F1: </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">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">)),</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">trueT</span><span class="p">,</span> <span class="n">predsT</span><span class="p">,</span> <span class="n">average</span> <span class="o">=</span> <span class="s1">&#39;macro&#39;</span><span class="p">)))</span>
</pre></div>

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<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">make_model2D_S</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;2D_small_0&quot;</span><span class="p">)</span>
</pre></div>

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<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Epoch 1/500
132/132 [==============================] - 143s 1s/step - loss: 1.6718 - acc: 0.3581 - val_loss: 1.4093 - val_acc: 0.4083
Epoch 2/500
132/132 [==============================] - 30s 229ms/step - loss: 1.4294 - acc: 0.4508 - val_loss: 1.2829 - val_acc: 0.4954
Epoch 3/500
132/132 [==============================] - 35s 264ms/step - loss: 1.3506 - acc: 0.4765 - val_loss: 1.2107 - val_acc: 0.5642
Epoch 4/500
132/132 [==============================] - 34s 261ms/step - loss: 1.2755 - acc: 0.5158 - val_loss: 1.2250 - val_acc: 0.5183
Epoch 5/500
132/132 [==============================] - 34s 257ms/step - loss: 1.2331 - acc: 0.5338 - val_loss: 1.1709 - val_acc: 0.5183
Epoch 6/500
132/132 [==============================] - 34s 257ms/step - loss: 1.2002 - acc: 0.5413 - val_loss: 1.1404 - val_acc: 0.5367
Epoch 7/500
132/132 [==============================] - 34s 254ms/step - loss: 1.1640 - acc: 0.5554 - val_loss: 1.1906 - val_acc: 0.5367
Epoch 8/500
132/132 [==============================] - 35s 263ms/step - loss: 1.1296 - acc: 0.5700 - val_loss: 1.1463 - val_acc: 0.5642
Epoch 9/500
132/132 [==============================] - 36s 273ms/step - loss: 1.1127 - acc: 0.5761 - val_loss: 1.1204 - val_acc: 0.5780
Epoch 10/500
132/132 [==============================] - 35s 265ms/step - loss: 1.0712 - acc: 0.5921 - val_loss: 1.0879 - val_acc: 0.5780
Epoch 11/500
132/132 [==============================] - 34s 255ms/step - loss: 1.0451 - acc: 0.6040 - val_loss: 1.1251 - val_acc: 0.5872
Epoch 12/500
132/132 [==============================] - 34s 259ms/step - loss: 1.0270 - acc: 0.6175 - val_loss: 1.1091 - val_acc: 0.5688
Epoch 13/500
132/132 [==============================] - 34s 254ms/step - loss: 0.9981 - acc: 0.6209 - val_loss: 1.1264 - val_acc: 0.5917
Epoch 14/500
132/132 [==============================] - 34s 254ms/step - loss: 0.9676 - acc: 0.6386 - val_loss: 1.0956 - val_acc: 0.5917
Epoch 15/500
132/132 [==============================] - 33s 246ms/step - loss: 0.9441 - acc: 0.6412 - val_loss: 1.1265 - val_acc: 0.6193
Epoch 16/500
132/132 [==============================] - 33s 251ms/step - loss: 0.9166 - acc: 0.6607 - val_loss: 1.1231 - val_acc: 0.6239
Epoch 17/500
132/132 [==============================] - 33s 247ms/step - loss: 0.8851 - acc: 0.6567 - val_loss: 1.1410 - val_acc: 0.5780
Epoch 18/500
132/132 [==============================] - 33s 246ms/step - loss: 0.8692 - acc: 0.6717 - val_loss: 1.1978 - val_acc: 0.5917
Epoch 19/500
132/132 [==============================] - 33s 247ms/step - loss: 0.8518 - acc: 0.6729 - val_loss: 1.1938 - val_acc: 0.6055
Epoch 20/500
132/132 [==============================] - 32s 246ms/step - loss: 0.8183 - acc: 0.6870 - val_loss: 1.2295 - val_acc: 0.6101
Epoch 21/500
132/132 [==============================] - 33s 247ms/step - loss: 0.8034 - acc: 0.6970 - val_loss: 1.2082 - val_acc: 0.6147
Epoch 22/500
132/132 [==============================] - 34s 254ms/step - loss: 0.7752 - acc: 0.7057 - val_loss: 1.2347 - val_acc: 0.6284
Epoch 23/500
132/132 [==============================] - 32s 246ms/step - loss: 0.7587 - acc: 0.7098 - val_loss: 1.2797 - val_acc: 0.6147
Epoch 24/500
132/132 [==============================] - 33s 247ms/step - loss: 0.7243 - acc: 0.7242 - val_loss: 1.2919 - val_acc: 0.6101
Epoch 25/500
132/132 [==============================] - 33s 246ms/step - loss: 0.7176 - acc: 0.7278 - val_loss: 1.3502 - val_acc: 0.5688
Epoch 26/500
132/132 [==============================] - 33s 247ms/step - loss: 0.6879 - acc: 0.7374 - val_loss: 1.3349 - val_acc: 0.6101
Epoch 27/500
132/132 [==============================] - 32s 246ms/step - loss: 0.6673 - acc: 0.7516 - val_loss: 1.4104 - val_acc: 0.5826
Epoch 28/500
132/132 [==============================] - 33s 248ms/step - loss: 0.6556 - acc: 0.7563 - val_loss: 1.3941 - val_acc: 0.6055
Epoch 29/500
132/132 [==============================] - 32s 245ms/step - loss: 0.6511 - acc: 0.7541 - val_loss: 1.3866 - val_acc: 0.6193
Epoch 30/500
132/132 [==============================] - 33s 247ms/step - loss: 0.6384 - acc: 0.7581 - val_loss: 1.4756 - val_acc: 0.5642
Epoch 31/500
132/132 [==============================] - 33s 247ms/step - loss: 0.6405 - acc: 0.7595 - val_loss: 1.4851 - val_acc: 0.6055
Epoch 32/500
132/132 [==============================] - 32s 246ms/step - loss: 0.6181 - acc: 0.7629 - val_loss: 1.5277 - val_acc: 0.5917
Epoch 33/500
132/132 [==============================] - 33s 248ms/step - loss: 0.5886 - acc: 0.7742 - val_loss: 1.5737 - val_acc: 0.5826
Epoch 34/500
132/132 [==============================] - 33s 252ms/step - loss: 0.5820 - acc: 0.7781 - val_loss: 1.5385 - val_acc: 0.6055
Epoch 35/500
132/132 [==============================] - 34s 254ms/step - loss: 0.5610 - acc: 0.7843 - val_loss: 1.5922 - val_acc: 0.5688
Epoch 36/500
132/132 [==============================] - 31s 238ms/step - loss: 0.5587 - acc: 0.7886 - val_loss: 1.5871 - val_acc: 0.5963
Epoch 37/500
132/132 [==============================] - 32s 241ms/step - loss: 0.5611 - acc: 0.7851 - val_loss: 1.7197 - val_acc: 0.5872
Epoch 38/500
132/132 [==============================] - 31s 235ms/step - loss: 0.5630 - acc: 0.7817 - val_loss: 1.6891 - val_acc: 0.5780
Epoch 39/500
132/132 [==============================] - 31s 234ms/step - loss: 0.5587 - acc: 0.7875 - val_loss: 1.7430 - val_acc: 0.5963
Epoch 40/500
132/132 [==============================] - 31s 234ms/step - loss: 0.5343 - acc: 0.7976 - val_loss: 1.7968 - val_acc: 0.5734
Epoch 41/500
132/132 [==============================] - 31s 234ms/step - loss: 0.5289 - acc: 0.8034 - val_loss: 1.7907 - val_acc: 0.6055
Epoch 42/500
132/132 [==============================] - 31s 233ms/step - loss: 0.5323 - acc: 0.7985 - val_loss: 1.8369 - val_acc: 0.5963
Epoch 43/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4978 - acc: 0.8110 - val_loss: 1.7437 - val_acc: 0.5963
Epoch 44/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4984 - acc: 0.8116 - val_loss: 1.8182 - val_acc: 0.6009
Epoch 45/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4849 - acc: 0.8173 - val_loss: 1.8135 - val_acc: 0.6101
Epoch 46/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4678 - acc: 0.8239 - val_loss: 1.8330 - val_acc: 0.5963
Epoch 47/500
132/132 [==============================] - 31s 235ms/step - loss: 0.4731 - acc: 0.8173 - val_loss: 1.9078 - val_acc: 0.5688
Epoch 48/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4676 - acc: 0.8235 - val_loss: 1.8621 - val_acc: 0.5963
Epoch 49/500
132/132 [==============================] - 31s 235ms/step - loss: 0.4588 - acc: 0.8267 - val_loss: 1.8269 - val_acc: 0.5963
Epoch 50/500
132/132 [==============================] - 31s 233ms/step - loss: 0.4550 - acc: 0.8283 - val_loss: 1.9002 - val_acc: 0.6055
Epoch 51/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4488 - acc: 0.8320 - val_loss: 1.7824 - val_acc: 0.6193
Epoch 52/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4423 - acc: 0.8361 - val_loss: 1.9905 - val_acc: 0.5780
Epoch 53/500
132/132 [==============================] - 31s 233ms/step - loss: 0.4502 - acc: 0.8289 - val_loss: 1.8724 - val_acc: 0.6239
Epoch 54/500
132/132 [==============================] - 31s 234ms/step - loss: 0.4433 - acc: 0.8346 - val_loss: 1.9451 - val_acc: 0.6009
Epoch 55/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4318 - acc: 0.8398 - val_loss: 1.8416 - val_acc: 0.6193
Epoch 56/500
132/132 [==============================] - 31s 238ms/step - loss: 0.4296 - acc: 0.8396 - val_loss: 2.0277 - val_acc: 0.5917
Epoch 57/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4229 - acc: 0.8375 - val_loss: 1.9107 - val_acc: 0.6009
Epoch 58/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4177 - acc: 0.8476 - val_loss: 2.1016 - val_acc: 0.5826
Epoch 59/500
132/132 [==============================] - 31s 235ms/step - loss: 0.4221 - acc: 0.8427 - val_loss: 1.9824 - val_acc: 0.5963
Epoch 60/500
132/132 [==============================] - 31s 233ms/step - loss: 0.4071 - acc: 0.8481 - val_loss: 2.0522 - val_acc: 0.6193
Epoch 61/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3941 - acc: 0.8534 - val_loss: 2.1242 - val_acc: 0.5826
Epoch 62/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3917 - acc: 0.8536 - val_loss: 2.2786 - val_acc: 0.5917
Epoch 63/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3873 - acc: 0.8558 - val_loss: 2.3598 - val_acc: 0.5459
Epoch 64/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3941 - acc: 0.8537 - val_loss: 2.4919 - val_acc: 0.5550
Epoch 65/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3916 - acc: 0.8552 - val_loss: 2.3296 - val_acc: 0.6009
Epoch 66/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3866 - acc: 0.8563 - val_loss: 2.2588 - val_acc: 0.5826
Epoch 67/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3763 - acc: 0.8578 - val_loss: 2.2652 - val_acc: 0.5872
Epoch 68/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3743 - acc: 0.8614 - val_loss: 2.3258 - val_acc: 0.5872
Epoch 69/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3879 - acc: 0.8557 - val_loss: 2.2374 - val_acc: 0.5734
Epoch 70/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3830 - acc: 0.8573 - val_loss: 2.1057 - val_acc: 0.5963
Epoch 71/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3739 - acc: 0.8601 - val_loss: 2.2482 - val_acc: 0.5780
Epoch 72/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3617 - acc: 0.8629 - val_loss: 2.2746 - val_acc: 0.5780
Epoch 73/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3648 - acc: 0.8670 - val_loss: 2.4250 - val_acc: 0.5917
Epoch 74/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3537 - acc: 0.8686 - val_loss: 2.3297 - val_acc: 0.5780
Epoch 75/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3661 - acc: 0.8619 - val_loss: 2.2730 - val_acc: 0.5963
Epoch 76/500
132/132 [==============================] - 31s 239ms/step - loss: 0.3575 - acc: 0.8654 - val_loss: 2.2949 - val_acc: 0.6101
Epoch 77/500
132/132 [==============================] - 32s 242ms/step - loss: 0.3425 - acc: 0.8736 - val_loss: 2.3128 - val_acc: 0.5826
Epoch 78/500
132/132 [==============================] - 32s 241ms/step - loss: 0.3436 - acc: 0.8673 - val_loss: 2.3576 - val_acc: 0.6101
Epoch 79/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3537 - acc: 0.8699 - val_loss: 2.5111 - val_acc: 0.5734
Epoch 80/500
132/132 [==============================] - 32s 239ms/step - loss: 0.3496 - acc: 0.8689 - val_loss: 2.4251 - val_acc: 0.5917
Epoch 81/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3413 - acc: 0.8731 - val_loss: 2.4373 - val_acc: 0.5780
Epoch 82/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3424 - acc: 0.8732 - val_loss: 2.2500 - val_acc: 0.5963
Epoch 83/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3340 - acc: 0.8773 - val_loss: 2.3934 - val_acc: 0.5642
Epoch 84/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3379 - acc: 0.8747 - val_loss: 2.4145 - val_acc: 0.5734
Epoch 85/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3373 - acc: 0.8761 - val_loss: 2.4928 - val_acc: 0.5872
Epoch 86/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3398 - acc: 0.8764 - val_loss: 2.5017 - val_acc: 0.6147
Epoch 87/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3376 - acc: 0.8755 - val_loss: 2.5546 - val_acc: 0.5734
Epoch 88/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3288 - acc: 0.8767 - val_loss: 2.6118 - val_acc: 0.5917
Epoch 89/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3277 - acc: 0.8796 - val_loss: 2.7243 - val_acc: 0.5688
Epoch 90/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3185 - acc: 0.8818 - val_loss: 2.4506 - val_acc: 0.5505
Epoch 91/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3218 - acc: 0.8797 - val_loss: 2.5563 - val_acc: 0.5688
Epoch 92/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3309 - acc: 0.8809 - val_loss: 2.6024 - val_acc: 0.5505
Epoch 93/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3197 - acc: 0.8832 - val_loss: 2.5314 - val_acc: 0.5688
Epoch 94/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3137 - acc: 0.8820 - val_loss: 2.5605 - val_acc: 0.5917
Epoch 95/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3260 - acc: 0.8773 - val_loss: 2.5560 - val_acc: 0.6009
Epoch 96/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3225 - acc: 0.8817 - val_loss: 2.4963 - val_acc: 0.6284
Epoch 97/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3217 - acc: 0.8796 - val_loss: 2.4437 - val_acc: 0.6193
Epoch 98/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3002 - acc: 0.8895 - val_loss: 2.6824 - val_acc: 0.6009
Epoch 99/500
132/132 [==============================] - 31s 233ms/step - loss: 0.3038 - acc: 0.8869 - val_loss: 2.6532 - val_acc: 0.5872
Epoch 100/500
132/132 [==============================] - 31s 233ms/step - loss: 0.3201 - acc: 0.8819 - val_loss: 2.4978 - val_acc: 0.6239
Epoch 101/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3184 - acc: 0.8831 - val_loss: 2.4929 - val_acc: 0.5917
Epoch 102/500
132/132 [==============================] - 31s 234ms/step - loss: 0.3120 - acc: 0.8813 - val_loss: 2.7090 - val_acc: 0.5642
Epoch 103/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2997 - acc: 0.8895 - val_loss: 2.7287 - val_acc: 0.5917
Epoch 104/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2993 - acc: 0.8887 - val_loss: 2.6595 - val_acc: 0.5963
Epoch 105/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2932 - acc: 0.8916 - val_loss: 2.6842 - val_acc: 0.5872
Epoch 106/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2915 - acc: 0.8922 - val_loss: 2.7633 - val_acc: 0.5688
Epoch 107/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3056 - acc: 0.8882 - val_loss: 2.7818 - val_acc: 0.5642
Epoch 108/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2897 - acc: 0.8896 - val_loss: 2.6867 - val_acc: 0.6239
Epoch 109/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3022 - acc: 0.8858 - val_loss: 2.9327 - val_acc: 0.5642
Epoch 110/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2959 - acc: 0.8870 - val_loss: 2.6835 - val_acc: 0.6147
Epoch 111/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2976 - acc: 0.8904 - val_loss: 2.6750 - val_acc: 0.5826
Epoch 112/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2866 - acc: 0.8912 - val_loss: 2.9278 - val_acc: 0.5826
Epoch 113/500
132/132 [==============================] - 31s 232ms/step - loss: 0.2732 - acc: 0.8972 - val_loss: 2.8890 - val_acc: 0.5642
Epoch 114/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2890 - acc: 0.8920 - val_loss: 2.6707 - val_acc: 0.5963
Epoch 115/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2845 - acc: 0.8929 - val_loss: 2.7373 - val_acc: 0.6193
Epoch 116/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3021 - acc: 0.8905 - val_loss: 2.6464 - val_acc: 0.5642
Epoch 117/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2758 - acc: 0.8996 - val_loss: 2.5929 - val_acc: 0.6055
Epoch 118/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2783 - acc: 0.8969 - val_loss: 2.7243 - val_acc: 0.6009
Epoch 119/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2878 - acc: 0.8978 - val_loss: 2.8304 - val_acc: 0.5734
Epoch 120/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2797 - acc: 0.8934 - val_loss: 2.7484 - val_acc: 0.5917
Epoch 121/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2653 - acc: 0.9037 - val_loss: 2.7331 - val_acc: 0.5826
Epoch 122/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2731 - acc: 0.8986 - val_loss: 2.8478 - val_acc: 0.5734
Epoch 123/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2725 - acc: 0.9005 - val_loss: 2.8213 - val_acc: 0.6101
Epoch 124/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2780 - acc: 0.8959 - val_loss: 2.7471 - val_acc: 0.5963
Epoch 125/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2832 - acc: 0.8948 - val_loss: 2.9487 - val_acc: 0.5780
Epoch 126/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2918 - acc: 0.8954 - val_loss: 2.7842 - val_acc: 0.5872
Epoch 127/500
132/132 [==============================] - 31s 232ms/step - loss: 0.2870 - acc: 0.8936 - val_loss: 2.7385 - val_acc: 0.6101
Epoch 128/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2805 - acc: 0.8954 - val_loss: 2.8474 - val_acc: 0.6009
Epoch 129/500
132/132 [==============================] - 32s 240ms/step - loss: 0.2858 - acc: 0.8943 - val_loss: 2.6535 - val_acc: 0.6284
Epoch 130/500
132/132 [==============================] - 29s 219ms/step - loss: 0.2640 - acc: 0.9025 - val_loss: 2.6975 - val_acc: 0.6193
Epoch 131/500
132/132 [==============================] - 33s 248ms/step - loss: 0.2884 - acc: 0.8936 - val_loss: 2.8097 - val_acc: 0.6101
Epoch 132/500
132/132 [==============================] - 32s 242ms/step - loss: 0.2664 - acc: 0.9014 - val_loss: 2.8109 - val_acc: 0.6009
Epoch 133/500
132/132 [==============================] - 32s 240ms/step - loss: 0.2731 - acc: 0.8995 - val_loss: 2.7492 - val_acc: 0.5963
Epoch 134/500
132/132 [==============================] - 32s 239ms/step - loss: 0.2667 - acc: 0.9001 - val_loss: 2.7973 - val_acc: 0.6055
Epoch 135/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2706 - acc: 0.8995 - val_loss: 2.7162 - val_acc: 0.6239
Epoch 136/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2641 - acc: 0.9041 - val_loss: 2.7037 - val_acc: 0.6193
Epoch 137/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2610 - acc: 0.9020 - val_loss: 2.8406 - val_acc: 0.5917
Epoch 138/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2616 - acc: 0.9019 - val_loss: 2.9127 - val_acc: 0.5734
Epoch 139/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2617 - acc: 0.9025 - val_loss: 3.0679 - val_acc: 0.5917
Epoch 140/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2632 - acc: 0.9008 - val_loss: 3.1439 - val_acc: 0.5872
Epoch 141/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2674 - acc: 0.9024 - val_loss: 2.8903 - val_acc: 0.6284
Epoch 142/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2588 - acc: 0.9049 - val_loss: 2.9311 - val_acc: 0.5963
Epoch 143/500
132/132 [==============================] - 31s 238ms/step - loss: 0.2636 - acc: 0.9020 - val_loss: 2.7835 - val_acc: 0.5780
Epoch 144/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2553 - acc: 0.9055 - val_loss: 2.8463 - val_acc: 0.5826
Epoch 145/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2517 - acc: 0.9079 - val_loss: 2.9542 - val_acc: 0.6055
Epoch 146/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2443 - acc: 0.9081 - val_loss: 2.7809 - val_acc: 0.5917
Epoch 147/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2414 - acc: 0.9107 - val_loss: 2.8313 - val_acc: 0.5917
Epoch 148/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2463 - acc: 0.9089 - val_loss: 3.0181 - val_acc: 0.5872
Epoch 149/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2487 - acc: 0.9060 - val_loss: 2.8792 - val_acc: 0.5826
Epoch 150/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2544 - acc: 0.9060 - val_loss: 2.7392 - val_acc: 0.6009
Epoch 151/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2719 - acc: 0.9010 - val_loss: 2.8079 - val_acc: 0.6055
Epoch 152/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2580 - acc: 0.9049 - val_loss: 2.9826 - val_acc: 0.5917
Epoch 153/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2686 - acc: 0.8998 - val_loss: 2.8806 - val_acc: 0.5780
Epoch 154/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2673 - acc: 0.9031 - val_loss: 3.0470 - val_acc: 0.5780
Epoch 155/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2520 - acc: 0.9089 - val_loss: 2.9440 - val_acc: 0.5872
Epoch 156/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2726 - acc: 0.9034 - val_loss: 2.9596 - val_acc: 0.5780
Epoch 157/500
132/132 [==============================] - 31s 238ms/step - loss: 0.2472 - acc: 0.9083 - val_loss: 3.0613 - val_acc: 0.5734
Epoch 158/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2451 - acc: 0.9091 - val_loss: 3.1665 - val_acc: 0.5826
Epoch 159/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2486 - acc: 0.9073 - val_loss: 3.1831 - val_acc: 0.5780
Epoch 160/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2393 - acc: 0.9110 - val_loss: 3.2437 - val_acc: 0.5917
Epoch 161/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2324 - acc: 0.9117 - val_loss: 3.1595 - val_acc: 0.6101
Epoch 162/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2456 - acc: 0.9070 - val_loss: 3.3570 - val_acc: 0.5826
Epoch 163/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2361 - acc: 0.9125 - val_loss: 3.4328 - val_acc: 0.5688
Epoch 164/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2328 - acc: 0.9134 - val_loss: 3.4008 - val_acc: 0.6009
Epoch 165/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2415 - acc: 0.9128 - val_loss: 3.5653 - val_acc: 0.5826
Epoch 166/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2346 - acc: 0.9140 - val_loss: 3.3507 - val_acc: 0.5688
Epoch 167/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2448 - acc: 0.9086 - val_loss: 3.2430 - val_acc: 0.5826
Epoch 168/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2572 - acc: 0.9069 - val_loss: 3.2274 - val_acc: 0.5688
Epoch 169/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2410 - acc: 0.9083 - val_loss: 3.2384 - val_acc: 0.5963
Epoch 170/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2417 - acc: 0.9125 - val_loss: 3.3061 - val_acc: 0.5872
Epoch 171/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2442 - acc: 0.9119 - val_loss: 3.4001 - val_acc: 0.5917
Epoch 172/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2508 - acc: 0.9076 - val_loss: 3.4595 - val_acc: 0.5642
Epoch 173/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2468 - acc: 0.9096 - val_loss: 3.2176 - val_acc: 0.6009
Epoch 174/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2509 - acc: 0.9117 - val_loss: 3.0208 - val_acc: 0.6101
Epoch 175/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2400 - acc: 0.9102 - val_loss: 3.2266 - val_acc: 0.5872
Epoch 176/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2215 - acc: 0.9183 - val_loss: 3.4202 - val_acc: 0.5872
Epoch 177/500
132/132 [==============================] - 32s 241ms/step - loss: 0.2268 - acc: 0.9167 - val_loss: 3.3652 - val_acc: 0.5780
Epoch 178/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2295 - acc: 0.9143 - val_loss: 3.5163 - val_acc: 0.6055
Epoch 179/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2321 - acc: 0.9160 - val_loss: 3.1985 - val_acc: 0.6055
Epoch 180/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2307 - acc: 0.9149 - val_loss: 3.3331 - val_acc: 0.5963
Epoch 181/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2210 - acc: 0.9189 - val_loss: 3.6562 - val_acc: 0.5826
Epoch 182/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2170 - acc: 0.9193 - val_loss: 3.6047 - val_acc: 0.5688
Epoch 183/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2342 - acc: 0.9155 - val_loss: 3.3896 - val_acc: 0.5826
Epoch 184/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2355 - acc: 0.9154 - val_loss: 3.1760 - val_acc: 0.5688
Epoch 185/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2364 - acc: 0.9142 - val_loss: 3.4113 - val_acc: 0.5688
Epoch 186/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2295 - acc: 0.9165 - val_loss: 3.7813 - val_acc: 0.5459
Epoch 187/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2511 - acc: 0.9111 - val_loss: 3.3614 - val_acc: 0.5872
Epoch 188/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2255 - acc: 0.9151 - val_loss: 3.4882 - val_acc: 0.5642
Epoch 189/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2279 - acc: 0.9173 - val_loss: 3.4438 - val_acc: 0.5688
Epoch 190/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2282 - acc: 0.9167 - val_loss: 3.3634 - val_acc: 0.6055
Epoch 191/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2158 - acc: 0.9212 - val_loss: 3.5110 - val_acc: 0.5780
Epoch 192/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2208 - acc: 0.9178 - val_loss: 3.7007 - val_acc: 0.5917
Epoch 193/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2260 - acc: 0.9175 - val_loss: 3.5899 - val_acc: 0.5872
Epoch 194/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2331 - acc: 0.9154 - val_loss: 3.3991 - val_acc: 0.5826
Epoch 195/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2333 - acc: 0.9136 - val_loss: 3.6060 - val_acc: 0.5826
Epoch 196/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2118 - acc: 0.9213 - val_loss: 3.3608 - val_acc: 0.6055
Epoch 197/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2168 - acc: 0.9216 - val_loss: 3.5370 - val_acc: 0.5963
Epoch 198/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2168 - acc: 0.9213 - val_loss: 3.3578 - val_acc: 0.6009
Epoch 199/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2191 - acc: 0.9222 - val_loss: 3.3904 - val_acc: 0.5642
Epoch 200/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2228 - acc: 0.9205 - val_loss: 3.5055 - val_acc: 0.5872
Epoch 201/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2228 - acc: 0.9179 - val_loss: 3.5176 - val_acc: 0.5734
Epoch 202/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2175 - acc: 0.9202 - val_loss: 3.4871 - val_acc: 0.5826
Epoch 203/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2101 - acc: 0.9228 - val_loss: 3.6294 - val_acc: 0.6009
Epoch 204/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2253 - acc: 0.9201 - val_loss: 3.3341 - val_acc: 0.5780
Epoch 205/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2184 - acc: 0.9229 - val_loss: 3.3577 - val_acc: 0.6101
Epoch 206/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2157 - acc: 0.9198 - val_loss: 3.4149 - val_acc: 0.5734
Epoch 207/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2060 - acc: 0.9238 - val_loss: 3.5303 - val_acc: 0.5780
Epoch 208/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2133 - acc: 0.9201 - val_loss: 3.4028 - val_acc: 0.6055
Epoch 209/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2119 - acc: 0.9235 - val_loss: 3.6142 - val_acc: 0.5688
Epoch 210/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2163 - acc: 0.9226 - val_loss: 3.5555 - val_acc: 0.5826
Epoch 211/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2119 - acc: 0.9208 - val_loss: 3.5454 - val_acc: 0.5826
Epoch 212/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2038 - acc: 0.9278 - val_loss: 3.5014 - val_acc: 0.6055
Epoch 213/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2198 - acc: 0.9199 - val_loss: 3.6625 - val_acc: 0.5550
Epoch 214/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2213 - acc: 0.9196 - val_loss: 3.5802 - val_acc: 0.5780
Epoch 215/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2160 - acc: 0.9213 - val_loss: 3.5324 - val_acc: 0.5872
Epoch 216/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2165 - acc: 0.9202 - val_loss: 3.8603 - val_acc: 0.5688
Epoch 217/500
132/132 [==============================] - 32s 239ms/step - loss: 0.2031 - acc: 0.9247 - val_loss: 3.6369 - val_acc: 0.5780
Epoch 218/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2156 - acc: 0.9216 - val_loss: 3.4240 - val_acc: 0.5872
Epoch 219/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1991 - acc: 0.9276 - val_loss: 3.5681 - val_acc: 0.5734
Epoch 220/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2029 - acc: 0.9243 - val_loss: 3.5929 - val_acc: 0.5917
Epoch 221/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2051 - acc: 0.9257 - val_loss: 3.5665 - val_acc: 0.5780
Epoch 222/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2128 - acc: 0.9237 - val_loss: 3.3423 - val_acc: 0.5780
Epoch 223/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2071 - acc: 0.9233 - val_loss: 3.6068 - val_acc: 0.5688
Epoch 224/500
132/132 [==============================] - 31s 232ms/step - loss: 0.2087 - acc: 0.9255 - val_loss: 3.4869 - val_acc: 0.5688
Epoch 225/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2004 - acc: 0.9285 - val_loss: 3.4440 - val_acc: 0.5780
Epoch 226/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1986 - acc: 0.9278 - val_loss: 3.7489 - val_acc: 0.5642
Epoch 227/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2043 - acc: 0.9257 - val_loss: 3.6999 - val_acc: 0.5505
Epoch 228/500
132/132 [==============================] - 31s 232ms/step - loss: 0.2165 - acc: 0.9195 - val_loss: 3.5908 - val_acc: 0.5826
Epoch 229/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2033 - acc: 0.9270 - val_loss: 3.6539 - val_acc: 0.5688
Epoch 230/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2133 - acc: 0.9247 - val_loss: 3.6475 - val_acc: 0.5734
Epoch 231/500
132/132 [==============================] - 31s 232ms/step - loss: 0.2115 - acc: 0.9239 - val_loss: 3.6127 - val_acc: 0.5780
Epoch 232/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2205 - acc: 0.9190 - val_loss: 3.5547 - val_acc: 0.5734
Epoch 233/500
132/132 [==============================] - 31s 234ms/step - loss: 0.2051 - acc: 0.9248 - val_loss: 3.6858 - val_acc: 0.6009
Epoch 234/500
132/132 [==============================] - 31s 233ms/step - loss: 0.2098 - acc: 0.9236 - val_loss: 3.5749 - val_acc: 0.5780
Epoch 235/500
132/132 [==============================] - 36s 270ms/step - loss: 0.1925 - acc: 0.9279 - val_loss: 3.6966 - val_acc: 0.5963
Epoch 236/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1922 - acc: 0.9272 - val_loss: 3.8949 - val_acc: 0.5688
Epoch 237/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1932 - acc: 0.9311 - val_loss: 3.4407 - val_acc: 0.5780
Epoch 238/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1950 - acc: 0.9285 - val_loss: 3.7435 - val_acc: 0.5780
Epoch 239/500
132/132 [==============================] - 32s 243ms/step - loss: 0.2071 - acc: 0.9260 - val_loss: 3.5772 - val_acc: 0.6101
Epoch 240/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1953 - acc: 0.9292 - val_loss: 3.5958 - val_acc: 0.5688
Epoch 241/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1875 - acc: 0.9292 - val_loss: 3.4125 - val_acc: 0.5963
Epoch 242/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1980 - acc: 0.9293 - val_loss: 3.6432 - val_acc: 0.6009
Epoch 243/500
132/132 [==============================] - 32s 243ms/step - loss: 0.2051 - acc: 0.9251 - val_loss: 3.8080 - val_acc: 0.5734
Epoch 244/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1907 - acc: 0.9325 - val_loss: 3.6266 - val_acc: 0.5872
Epoch 245/500
132/132 [==============================] - 32s 242ms/step - loss: 0.1863 - acc: 0.9317 - val_loss: 3.6606 - val_acc: 0.5688
Epoch 246/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1890 - acc: 0.9304 - val_loss: 3.4811 - val_acc: 0.5963
Epoch 247/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1931 - acc: 0.9298 - val_loss: 3.2892 - val_acc: 0.5963
Epoch 248/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1871 - acc: 0.9314 - val_loss: 3.6517 - val_acc: 0.5872
Epoch 249/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1968 - acc: 0.9298 - val_loss: 3.6203 - val_acc: 0.6009
Epoch 250/500
132/132 [==============================] - 32s 245ms/step - loss: 0.2094 - acc: 0.9231 - val_loss: 3.4318 - val_acc: 0.6009
Epoch 251/500
132/132 [==============================] - 33s 249ms/step - loss: 0.2141 - acc: 0.9217 - val_loss: 3.5503 - val_acc: 0.6055
Epoch 252/500
132/132 [==============================] - 32s 246ms/step - loss: 0.1911 - acc: 0.9304 - val_loss: 3.5344 - val_acc: 0.5963
Epoch 253/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1975 - acc: 0.9302 - val_loss: 3.5762 - val_acc: 0.6055
Epoch 254/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1877 - acc: 0.9323 - val_loss: 3.6390 - val_acc: 0.5826
Epoch 255/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1917 - acc: 0.9303 - val_loss: 3.8948 - val_acc: 0.5688
Epoch 256/500
132/132 [==============================] - 33s 249ms/step - loss: 0.1840 - acc: 0.9320 - val_loss: 3.8567 - val_acc: 0.5642
Epoch 257/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1852 - acc: 0.9328 - val_loss: 3.7489 - val_acc: 0.5963
Epoch 258/500
132/132 [==============================] - 32s 246ms/step - loss: 0.1886 - acc: 0.9320 - val_loss: 3.4729 - val_acc: 0.6009
Epoch 259/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1824 - acc: 0.9348 - val_loss: 3.9925 - val_acc: 0.5780
Epoch 260/500
132/132 [==============================] - 32s 246ms/step - loss: 0.1938 - acc: 0.9276 - val_loss: 3.8571 - val_acc: 0.5780
Epoch 261/500
132/132 [==============================] - 32s 246ms/step - loss: 0.1935 - acc: 0.9298 - val_loss: 3.8095 - val_acc: 0.5872
Epoch 262/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1792 - acc: 0.9348 - val_loss: 4.0460 - val_acc: 0.5826
Epoch 263/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1792 - acc: 0.9354 - val_loss: 3.6515 - val_acc: 0.5917
Epoch 264/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1776 - acc: 0.9349 - val_loss: 3.8139 - val_acc: 0.5872
Epoch 265/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1783 - acc: 0.9358 - val_loss: 3.9932 - val_acc: 0.5872
Epoch 266/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1711 - acc: 0.9367 - val_loss: 3.9893 - val_acc: 0.5780
Epoch 267/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1928 - acc: 0.9305 - val_loss: 3.7749 - val_acc: 0.5872
Epoch 268/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1882 - acc: 0.9337 - val_loss: 3.5699 - val_acc: 0.5917
Epoch 269/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1824 - acc: 0.9354 - val_loss: 3.6181 - val_acc: 0.5917
Epoch 270/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1951 - acc: 0.9299 - val_loss: 3.6966 - val_acc: 0.5826
Epoch 271/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1800 - acc: 0.9337 - val_loss: 3.4739 - val_acc: 0.5963
Epoch 272/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1727 - acc: 0.9375 - val_loss: 3.3772 - val_acc: 0.5963
Epoch 273/500
132/132 [==============================] - 33s 251ms/step - loss: 0.1779 - acc: 0.9357 - val_loss: 3.7905 - val_acc: 0.5734
Epoch 274/500
132/132 [==============================] - 36s 276ms/step - loss: 0.1738 - acc: 0.9361 - val_loss: 3.7627 - val_acc: 0.5734
Epoch 275/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1998 - acc: 0.9313 - val_loss: 3.7155 - val_acc: 0.6055
Epoch 276/500
132/132 [==============================] - 33s 250ms/step - loss: 0.2029 - acc: 0.9284 - val_loss: 3.8409 - val_acc: 0.5642
Epoch 277/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1707 - acc: 0.9376 - val_loss: 3.6925 - val_acc: 0.6101
Epoch 278/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1882 - acc: 0.9313 - val_loss: 3.7385 - val_acc: 0.5917
Epoch 279/500
132/132 [==============================] - 32s 242ms/step - loss: 0.1763 - acc: 0.9381 - val_loss: 3.9055 - val_acc: 0.6147
Epoch 280/500
132/132 [==============================] - 33s 246ms/step - loss: 0.1740 - acc: 0.9375 - val_loss: 3.9430 - val_acc: 0.5826
Epoch 281/500
132/132 [==============================] - 32s 241ms/step - loss: 0.1855 - acc: 0.9336 - val_loss: 3.5228 - val_acc: 0.6055
Epoch 282/500
132/132 [==============================] - 33s 246ms/step - loss: 0.1720 - acc: 0.9369 - val_loss: 3.8204 - val_acc: 0.6101
Epoch 283/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1757 - acc: 0.9370 - val_loss: 3.9431 - val_acc: 0.5963
Epoch 284/500
132/132 [==============================] - 32s 242ms/step - loss: 0.1709 - acc: 0.9387 - val_loss: 3.7786 - val_acc: 0.5963
Epoch 285/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1722 - acc: 0.9383 - val_loss: 3.6498 - val_acc: 0.6055
Epoch 286/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1920 - acc: 0.9322 - val_loss: 3.6889 - val_acc: 0.5872
Epoch 287/500
132/132 [==============================] - 34s 260ms/step - loss: 0.1792 - acc: 0.9381 - val_loss: 3.8296 - val_acc: 0.5688
Epoch 288/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1760 - acc: 0.9348 - val_loss: 3.7708 - val_acc: 0.5963
Epoch 289/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1782 - acc: 0.9363 - val_loss: 3.6729 - val_acc: 0.6101
Epoch 290/500
132/132 [==============================] - 34s 257ms/step - loss: 0.1678 - acc: 0.9386 - val_loss: 3.7330 - val_acc: 0.6009
Epoch 291/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1554 - acc: 0.9416 - val_loss: 3.7042 - val_acc: 0.5872
Epoch 292/500
132/132 [==============================] - 32s 242ms/step - loss: 0.1652 - acc: 0.9402 - val_loss: 3.5842 - val_acc: 0.6009
Epoch 293/500
132/132 [==============================] - 32s 244ms/step - loss: 0.1653 - acc: 0.9414 - val_loss: 3.8285 - val_acc: 0.5826
Epoch 294/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1536 - acc: 0.9442 - val_loss: 3.9434 - val_acc: 0.6009
Epoch 295/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1777 - acc: 0.9364 - val_loss: 4.0364 - val_acc: 0.5734
Epoch 296/500
132/132 [==============================] - 32s 241ms/step - loss: 0.1726 - acc: 0.9346 - val_loss: 3.9459 - val_acc: 0.5780
Epoch 297/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1807 - acc: 0.9383 - val_loss: 3.9368 - val_acc: 0.5734
Epoch 298/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1840 - acc: 0.9370 - val_loss: 3.8765 - val_acc: 0.5688
Epoch 299/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1805 - acc: 0.9370 - val_loss: 3.9428 - val_acc: 0.5826
Epoch 300/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1834 - acc: 0.9348 - val_loss: 3.7680 - val_acc: 0.5963
Epoch 301/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1802 - acc: 0.9358 - val_loss: 4.1115 - val_acc: 0.5734
Epoch 302/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1667 - acc: 0.9381 - val_loss: 4.0665 - val_acc: 0.5596
Epoch 303/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1621 - acc: 0.9425 - val_loss: 3.9224 - val_acc: 0.5780
Epoch 304/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1635 - acc: 0.9399 - val_loss: 3.8437 - val_acc: 0.5963
Epoch 305/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1669 - acc: 0.9402 - val_loss: 3.9939 - val_acc: 0.5780
Epoch 306/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1643 - acc: 0.9417 - val_loss: 3.8150 - val_acc: 0.6239
Epoch 307/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1722 - acc: 0.9373 - val_loss: 3.8155 - val_acc: 0.6193
Epoch 308/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1711 - acc: 0.9396 - val_loss: 4.1449 - val_acc: 0.5780
Epoch 309/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1614 - acc: 0.9411 - val_loss: 3.9953 - val_acc: 0.5734
Epoch 310/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1628 - acc: 0.9433 - val_loss: 4.0814 - val_acc: 0.5963
Epoch 311/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1610 - acc: 0.9420 - val_loss: 4.1436 - val_acc: 0.5780
Epoch 312/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1532 - acc: 0.9448 - val_loss: 3.9308 - val_acc: 0.5826
Epoch 313/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1876 - acc: 0.9348 - val_loss: 3.8929 - val_acc: 0.5505
Epoch 314/500
132/132 [==============================] - 32s 241ms/step - loss: 0.1844 - acc: 0.9352 - val_loss: 4.1058 - val_acc: 0.5642
Epoch 315/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1698 - acc: 0.9390 - val_loss: 4.0308 - val_acc: 0.5826
Epoch 316/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1663 - acc: 0.9405 - val_loss: 3.8793 - val_acc: 0.5826
Epoch 317/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1720 - acc: 0.9409 - val_loss: 3.8579 - val_acc: 0.5780
Epoch 318/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1633 - acc: 0.9422 - val_loss: 3.8548 - val_acc: 0.5780
Epoch 319/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1654 - acc: 0.9396 - val_loss: 3.8391 - val_acc: 0.5826
Epoch 320/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1723 - acc: 0.9419 - val_loss: 3.8460 - val_acc: 0.5734
Epoch 321/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1593 - acc: 0.9429 - val_loss: 4.1802 - val_acc: 0.5872
Epoch 322/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1648 - acc: 0.9395 - val_loss: 3.8269 - val_acc: 0.5642
Epoch 323/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1593 - acc: 0.9402 - val_loss: 3.7445 - val_acc: 0.5963
Epoch 324/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1646 - acc: 0.9442 - val_loss: 3.6495 - val_acc: 0.5780
Epoch 325/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1602 - acc: 0.9434 - val_loss: 3.9781 - val_acc: 0.5550
Epoch 326/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1464 - acc: 0.9484 - val_loss: 3.8291 - val_acc: 0.5872
Epoch 327/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1558 - acc: 0.9439 - val_loss: 4.0001 - val_acc: 0.5688
Epoch 328/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1674 - acc: 0.9392 - val_loss: 4.0006 - val_acc: 0.5734
Epoch 329/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1526 - acc: 0.9455 - val_loss: 4.0920 - val_acc: 0.5734
Epoch 330/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1599 - acc: 0.9431 - val_loss: 4.0387 - val_acc: 0.5872
Epoch 331/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1679 - acc: 0.9392 - val_loss: 4.1960 - val_acc: 0.5688
Epoch 332/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1657 - acc: 0.9437 - val_loss: 3.8247 - val_acc: 0.6009
Epoch 333/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1732 - acc: 0.9409 - val_loss: 4.3422 - val_acc: 0.5642
Epoch 334/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1689 - acc: 0.9422 - val_loss: 4.1742 - val_acc: 0.5505
Epoch 335/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1574 - acc: 0.9444 - val_loss: 4.2558 - val_acc: 0.5688
Epoch 336/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1506 - acc: 0.9452 - val_loss: 4.3527 - val_acc: 0.5780
Epoch 337/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1523 - acc: 0.9429 - val_loss: 4.3635 - val_acc: 0.5459
Epoch 338/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1543 - acc: 0.9451 - val_loss: 4.0915 - val_acc: 0.5596
Epoch 339/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1568 - acc: 0.9461 - val_loss: 3.9028 - val_acc: 0.5872
Epoch 340/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1516 - acc: 0.9439 - val_loss: 4.0483 - val_acc: 0.5872
Epoch 341/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1391 - acc: 0.9480 - val_loss: 4.2211 - val_acc: 0.5734
Epoch 342/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1434 - acc: 0.9497 - val_loss: 4.1903 - val_acc: 0.5642
Epoch 343/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1538 - acc: 0.9457 - val_loss: 4.3989 - val_acc: 0.5734
Epoch 344/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1495 - acc: 0.9465 - val_loss: 4.1746 - val_acc: 0.5550
Epoch 345/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1395 - acc: 0.9484 - val_loss: 4.2818 - val_acc: 0.5780
Epoch 346/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1556 - acc: 0.9448 - val_loss: 4.4863 - val_acc: 0.5688
Epoch 347/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1505 - acc: 0.9461 - val_loss: 4.2395 - val_acc: 0.5780
Epoch 348/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1552 - acc: 0.9443 - val_loss: 3.9105 - val_acc: 0.5734
Epoch 349/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1560 - acc: 0.9477 - val_loss: 3.9618 - val_acc: 0.5780
Epoch 350/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1553 - acc: 0.9433 - val_loss: 4.2348 - val_acc: 0.5872
Epoch 351/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1425 - acc: 0.9487 - val_loss: 4.2445 - val_acc: 0.5459
Epoch 352/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1579 - acc: 0.9419 - val_loss: 4.2444 - val_acc: 0.5413
Epoch 353/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1552 - acc: 0.9429 - val_loss: 3.7273 - val_acc: 0.5963
Epoch 354/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1651 - acc: 0.9460 - val_loss: 3.8076 - val_acc: 0.5780
Epoch 355/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1647 - acc: 0.9443 - val_loss: 4.2433 - val_acc: 0.5734
Epoch 356/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1500 - acc: 0.9451 - val_loss: 4.5331 - val_acc: 0.5550
Epoch 357/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1608 - acc: 0.9441 - val_loss: 4.0638 - val_acc: 0.5826
Epoch 358/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1525 - acc: 0.9476 - val_loss: 3.8011 - val_acc: 0.5688
Epoch 359/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1452 - acc: 0.9496 - val_loss: 3.9369 - val_acc: 0.5642
Epoch 360/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1324 - acc: 0.9525 - val_loss: 4.2907 - val_acc: 0.5734
Epoch 361/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1487 - acc: 0.9477 - val_loss: 4.0410 - val_acc: 0.5872
Epoch 362/500
132/132 [==============================] - 31s 231ms/step - loss: 0.1595 - acc: 0.9439 - val_loss: 4.1848 - val_acc: 0.5963
Epoch 363/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1389 - acc: 0.9504 - val_loss: 4.3896 - val_acc: 0.5642
Epoch 364/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1395 - acc: 0.9505 - val_loss: 4.1752 - val_acc: 0.5642
Epoch 365/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1374 - acc: 0.9523 - val_loss: 4.4160 - val_acc: 0.5872
Epoch 366/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1402 - acc: 0.9511 - val_loss: 4.5456 - val_acc: 0.5459
Epoch 367/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1462 - acc: 0.9504 - val_loss: 4.6155 - val_acc: 0.5413
Epoch 368/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1592 - acc: 0.9475 - val_loss: 4.2830 - val_acc: 0.5505
Epoch 369/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1591 - acc: 0.9431 - val_loss: 4.0706 - val_acc: 0.5963
Epoch 370/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1399 - acc: 0.9508 - val_loss: 4.1336 - val_acc: 0.5642
Epoch 371/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1399 - acc: 0.9496 - val_loss: 4.3284 - val_acc: 0.5550
Epoch 372/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1446 - acc: 0.9492 - val_loss: 4.2817 - val_acc: 0.5734
Epoch 373/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1447 - acc: 0.9498 - val_loss: 4.1322 - val_acc: 0.5780
Epoch 374/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1390 - acc: 0.9512 - val_loss: 4.2981 - val_acc: 0.5688
Epoch 375/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1391 - acc: 0.9524 - val_loss: 4.2901 - val_acc: 0.5780
Epoch 376/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1457 - acc: 0.9487 - val_loss: 4.4014 - val_acc: 0.5413
Epoch 377/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1483 - acc: 0.9464 - val_loss: 4.2400 - val_acc: 0.5734
Epoch 378/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1319 - acc: 0.9526 - val_loss: 4.2670 - val_acc: 0.5826
Epoch 379/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1465 - acc: 0.9471 - val_loss: 4.2865 - val_acc: 0.5596
Epoch 380/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1593 - acc: 0.9463 - val_loss: 4.2721 - val_acc: 0.5688
Epoch 381/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1421 - acc: 0.9480 - val_loss: 4.3358 - val_acc: 0.5734
Epoch 382/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1367 - acc: 0.9487 - val_loss: 4.2957 - val_acc: 0.5826
Epoch 383/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1376 - acc: 0.9520 - val_loss: 4.4664 - val_acc: 0.5872
Epoch 384/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1384 - acc: 0.9508 - val_loss: 4.3030 - val_acc: 0.5780
Epoch 385/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1359 - acc: 0.9520 - val_loss: 4.1423 - val_acc: 0.5872
Epoch 386/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1459 - acc: 0.9512 - val_loss: 4.2633 - val_acc: 0.5780
Epoch 387/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1447 - acc: 0.9487 - val_loss: 4.3531 - val_acc: 0.5688
Epoch 388/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1365 - acc: 0.9521 - val_loss: 4.4154 - val_acc: 0.5688
Epoch 389/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1343 - acc: 0.9517 - val_loss: 3.9558 - val_acc: 0.5872
Epoch 390/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1436 - acc: 0.9470 - val_loss: 4.2377 - val_acc: 0.5596
Epoch 391/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1550 - acc: 0.9470 - val_loss: 4.4146 - val_acc: 0.5367
Epoch 392/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1372 - acc: 0.9523 - val_loss: 4.3549 - val_acc: 0.5596
Epoch 393/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1452 - acc: 0.9517 - val_loss: 4.2758 - val_acc: 0.5780
Epoch 394/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1518 - acc: 0.9475 - val_loss: 4.0200 - val_acc: 0.5505
Epoch 395/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1374 - acc: 0.9524 - val_loss: 4.3385 - val_acc: 0.5596
Epoch 396/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1321 - acc: 0.9504 - val_loss: 4.2883 - val_acc: 0.5642
Epoch 397/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1400 - acc: 0.9532 - val_loss: 4.1556 - val_acc: 0.5642
Epoch 398/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1278 - acc: 0.9539 - val_loss: 4.0591 - val_acc: 0.5826
Epoch 399/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1363 - acc: 0.9521 - val_loss: 4.1743 - val_acc: 0.5917
Epoch 400/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1459 - acc: 0.9508 - val_loss: 4.1730 - val_acc: 0.5780
Epoch 401/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1373 - acc: 0.9511 - val_loss: 4.5725 - val_acc: 0.5642
Epoch 402/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1482 - acc: 0.9495 - val_loss: 4.2589 - val_acc: 0.5413
Epoch 403/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1346 - acc: 0.9531 - val_loss: 4.2211 - val_acc: 0.5642
Epoch 404/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1450 - acc: 0.9499 - val_loss: 4.3554 - val_acc: 0.5642
Epoch 405/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1272 - acc: 0.9520 - val_loss: 4.1736 - val_acc: 0.5642
Epoch 406/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1400 - acc: 0.9508 - val_loss: 4.1822 - val_acc: 0.5734
Epoch 407/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1634 - acc: 0.9460 - val_loss: 4.0162 - val_acc: 0.5596
Epoch 408/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1383 - acc: 0.9508 - val_loss: 4.1178 - val_acc: 0.5688
Epoch 409/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1431 - acc: 0.9516 - val_loss: 4.1353 - val_acc: 0.5826
Epoch 410/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1412 - acc: 0.9525 - val_loss: 4.2795 - val_acc: 0.5642
Epoch 411/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1301 - acc: 0.9548 - val_loss: 4.2362 - val_acc: 0.5688
Epoch 412/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1343 - acc: 0.9546 - val_loss: 4.0705 - val_acc: 0.5780
Epoch 413/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1341 - acc: 0.9495 - val_loss: 4.0620 - val_acc: 0.5963
Epoch 414/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1295 - acc: 0.9564 - val_loss: 4.0798 - val_acc: 0.6101
Epoch 415/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1335 - acc: 0.9543 - val_loss: 4.0307 - val_acc: 0.5872
Epoch 416/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1239 - acc: 0.9551 - val_loss: 4.0324 - val_acc: 0.5734
Epoch 417/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1289 - acc: 0.9560 - val_loss: 4.0887 - val_acc: 0.5872
Epoch 418/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1275 - acc: 0.9553 - val_loss: 4.1660 - val_acc: 0.6009
Epoch 419/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1361 - acc: 0.9539 - val_loss: 3.7495 - val_acc: 0.5917
Epoch 420/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1368 - acc: 0.9535 - val_loss: 3.8588 - val_acc: 0.5917
Epoch 421/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1278 - acc: 0.9555 - val_loss: 4.3249 - val_acc: 0.5780
Epoch 422/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1398 - acc: 0.9522 - val_loss: 3.9034 - val_acc: 0.5963
Epoch 423/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1360 - acc: 0.9520 - val_loss: 4.0650 - val_acc: 0.5734
Epoch 424/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1200 - acc: 0.9582 - val_loss: 3.9838 - val_acc: 0.5734
Epoch 425/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1318 - acc: 0.9538 - val_loss: 4.1145 - val_acc: 0.5688
Epoch 426/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1298 - acc: 0.9536 - val_loss: 3.9110 - val_acc: 0.5963
Epoch 427/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1264 - acc: 0.9538 - val_loss: 3.8905 - val_acc: 0.5872
Epoch 428/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1295 - acc: 0.9568 - val_loss: 4.2205 - val_acc: 0.5826
Epoch 429/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1413 - acc: 0.9544 - val_loss: 3.8093 - val_acc: 0.6330
Epoch 430/500
132/132 [==============================] - 28s 211ms/step - loss: 0.1456 - acc: 0.9527 - val_loss: 3.9408 - val_acc: 0.5963
Epoch 431/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1301 - acc: 0.9538 - val_loss: 4.2088 - val_acc: 0.5917
Epoch 432/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1384 - acc: 0.9515 - val_loss: 3.8190 - val_acc: 0.5917
Epoch 433/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1351 - acc: 0.9533 - val_loss: 3.7964 - val_acc: 0.6009
Epoch 434/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1286 - acc: 0.9561 - val_loss: 3.8307 - val_acc: 0.5963
Epoch 435/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1280 - acc: 0.9559 - val_loss: 4.3421 - val_acc: 0.5734
Epoch 436/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1278 - acc: 0.9558 - val_loss: 4.2568 - val_acc: 0.5826
Epoch 437/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1277 - acc: 0.9557 - val_loss: 4.2452 - val_acc: 0.5780
Epoch 438/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1303 - acc: 0.9562 - val_loss: 4.4879 - val_acc: 0.5780
Epoch 439/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1376 - acc: 0.9520 - val_loss: 4.2245 - val_acc: 0.5826
Epoch 440/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1251 - acc: 0.9579 - val_loss: 4.1290 - val_acc: 0.5963
Epoch 441/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1293 - acc: 0.9560 - val_loss: 4.0910 - val_acc: 0.6101
Epoch 442/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1319 - acc: 0.9541 - val_loss: 4.2553 - val_acc: 0.6055
Epoch 443/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1337 - acc: 0.9558 - val_loss: 4.1717 - val_acc: 0.6009
Epoch 444/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1202 - acc: 0.9583 - val_loss: 4.2989 - val_acc: 0.5734
Epoch 445/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1227 - acc: 0.9583 - val_loss: 4.5436 - val_acc: 0.5734
Epoch 446/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1379 - acc: 0.9531 - val_loss: 4.4455 - val_acc: 0.5780
Epoch 447/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1433 - acc: 0.9529 - val_loss: 4.2504 - val_acc: 0.5963
Epoch 448/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1411 - acc: 0.9551 - val_loss: 4.2347 - val_acc: 0.5963
Epoch 449/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1372 - acc: 0.9523 - val_loss: 4.1907 - val_acc: 0.5826
Epoch 450/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1445 - acc: 0.9529 - val_loss: 4.1344 - val_acc: 0.5688
Epoch 451/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1266 - acc: 0.9564 - val_loss: 4.1888 - val_acc: 0.5780
Epoch 452/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1215 - acc: 0.9580 - val_loss: 4.1336 - val_acc: 0.6101
Epoch 453/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1132 - acc: 0.9589 - val_loss: 4.5535 - val_acc: 0.5550
Epoch 454/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1184 - acc: 0.9608 - val_loss: 4.2022 - val_acc: 0.5642
Epoch 455/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1226 - acc: 0.9581 - val_loss: 4.0707 - val_acc: 0.6101
Epoch 456/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1121 - acc: 0.9601 - val_loss: 4.0733 - val_acc: 0.5688
Epoch 457/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1192 - acc: 0.9591 - val_loss: 3.9947 - val_acc: 0.6284
Epoch 458/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1164 - acc: 0.9588 - val_loss: 4.3066 - val_acc: 0.5734
Epoch 459/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1280 - acc: 0.9567 - val_loss: 4.2469 - val_acc: 0.5688
Epoch 460/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1221 - acc: 0.9559 - val_loss: 4.0760 - val_acc: 0.5642
Epoch 461/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1311 - acc: 0.9556 - val_loss: 4.1079 - val_acc: 0.5872
Epoch 462/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1212 - acc: 0.9567 - val_loss: 4.2919 - val_acc: 0.5872
Epoch 463/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1114 - acc: 0.9609 - val_loss: 4.1222 - val_acc: 0.5642
Epoch 464/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1163 - acc: 0.9608 - val_loss: 4.3848 - val_acc: 0.5826
Epoch 465/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1262 - acc: 0.9567 - val_loss: 4.1719 - val_acc: 0.5642
Epoch 466/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1454 - acc: 0.9509 - val_loss: 4.2388 - val_acc: 0.5963
Epoch 467/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1319 - acc: 0.9559 - val_loss: 4.1811 - val_acc: 0.6055
Epoch 468/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1361 - acc: 0.9560 - val_loss: 4.0863 - val_acc: 0.6009
Epoch 469/500
132/132 [==============================] - 32s 240ms/step - loss: 0.1391 - acc: 0.9545 - val_loss: 3.9121 - val_acc: 0.6009
Epoch 470/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1301 - acc: 0.9591 - val_loss: 3.9102 - val_acc: 0.6009
Epoch 471/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1284 - acc: 0.9554 - val_loss: 4.1310 - val_acc: 0.5734
Epoch 472/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1247 - acc: 0.9565 - val_loss: 4.2568 - val_acc: 0.5963
Epoch 473/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1304 - acc: 0.9539 - val_loss: 3.9419 - val_acc: 0.6009
Epoch 474/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1482 - acc: 0.9519 - val_loss: 4.3143 - val_acc: 0.5780
Epoch 475/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1363 - acc: 0.9536 - val_loss: 4.3331 - val_acc: 0.5734
Epoch 476/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1361 - acc: 0.9555 - val_loss: 4.1639 - val_acc: 0.5872
Epoch 477/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1263 - acc: 0.9580 - val_loss: 4.4224 - val_acc: 0.5642
Epoch 478/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1230 - acc: 0.9579 - val_loss: 4.3565 - val_acc: 0.5596
Epoch 479/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1107 - acc: 0.9614 - val_loss: 4.3239 - val_acc: 0.5780
Epoch 480/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1201 - acc: 0.9579 - val_loss: 4.5515 - val_acc: 0.5688
Epoch 481/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1245 - acc: 0.9585 - val_loss: 4.5318 - val_acc: 0.5642
Epoch 482/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1284 - acc: 0.9608 - val_loss: 4.3845 - val_acc: 0.5642
Epoch 483/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1264 - acc: 0.9576 - val_loss: 4.3571 - val_acc: 0.5917
Epoch 484/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1516 - acc: 0.9506 - val_loss: 4.3145 - val_acc: 0.5826
Epoch 485/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1230 - acc: 0.9600 - val_loss: 4.3055 - val_acc: 0.5780
Epoch 486/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1282 - acc: 0.9580 - val_loss: 4.3866 - val_acc: 0.5872
Epoch 487/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1249 - acc: 0.9573 - val_loss: 4.2973 - val_acc: 0.5963
Epoch 488/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1127 - acc: 0.9620 - val_loss: 4.3914 - val_acc: 0.5826
Epoch 489/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1276 - acc: 0.9592 - val_loss: 4.3275 - val_acc: 0.5780
Epoch 490/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1228 - acc: 0.9592 - val_loss: 4.4770 - val_acc: 0.5413
Epoch 491/500
132/132 [==============================] - 31s 233ms/step - loss: 0.1290 - acc: 0.9568 - val_loss: 4.3410 - val_acc: 0.5872
Epoch 492/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1164 - acc: 0.9590 - val_loss: 4.1288 - val_acc: 0.5963
Epoch 493/500
132/132 [==============================] - 31s 232ms/step - loss: 0.1151 - acc: 0.9612 - val_loss: 4.4569 - val_acc: 0.5917
Epoch 494/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1068 - acc: 0.9637 - val_loss: 4.3189 - val_acc: 0.5872
Epoch 495/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1079 - acc: 0.9612 - val_loss: 4.2749 - val_acc: 0.6009
Epoch 496/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1202 - acc: 0.9606 - val_loss: 4.5202 - val_acc: 0.5596
Epoch 497/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1290 - acc: 0.9570 - val_loss: 4.2441 - val_acc: 0.6009
Epoch 498/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1167 - acc: 0.9597 - val_loss: 4.4212 - val_acc: 0.6009
Epoch 499/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1253 - acc: 0.9588 - val_loss: 4.3808 - val_acc: 0.5872
Epoch 500/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1051 - acc: 0.9615 - val_loss: 4.2584 - val_acc: 0.5734
40/40 [==============================] - 37s 930ms/step
Accuracy: 0.5540883697308279. F1: 0.545068878917053
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<div class="prompt input_prompt">In&nbsp;[18]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">make_model2D_S</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;2D_small_1&quot;</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 92s 695ms/step - loss: 2.1612 - acc: 0.1454 - val_loss: 1.9334 - val_acc: 0.2569
Epoch 2/500
132/132 [==============================] - 27s 204ms/step - loss: 1.5063 - acc: 0.4263 - val_loss: 1.2985 - val_acc: 0.4862
Epoch 3/500
132/132 [==============================] - 31s 232ms/step - loss: 1.1907 - acc: 0.5279 - val_loss: 1.1807 - val_acc: 0.5092
Epoch 4/500
132/132 [==============================] - 32s 239ms/step - loss: 1.0952 - acc: 0.5775 - val_loss: 1.0861 - val_acc: 0.5459
Epoch 5/500
132/132 [==============================] - 32s 240ms/step - loss: 1.0292 - acc: 0.6064 - val_loss: 1.2076 - val_acc: 0.5550
Epoch 6/500
132/132 [==============================] - 31s 238ms/step - loss: 0.9832 - acc: 0.6279 - val_loss: 0.9657 - val_acc: 0.6330
Epoch 7/500
132/132 [==============================] - 31s 237ms/step - loss: 0.9184 - acc: 0.6621 - val_loss: 0.9102 - val_acc: 0.6560
Epoch 8/500
132/132 [==============================] - 31s 238ms/step - loss: 0.8710 - acc: 0.6770 - val_loss: 0.9005 - val_acc: 0.6330
Epoch 9/500
132/132 [==============================] - 31s 236ms/step - loss: 0.8230 - acc: 0.7010 - val_loss: 0.7943 - val_acc: 0.7202
Epoch 10/500
132/132 [==============================] - 31s 238ms/step - loss: 0.7797 - acc: 0.7231 - val_loss: 0.7727 - val_acc: 0.7248
Epoch 11/500
132/132 [==============================] - 31s 238ms/step - loss: 0.7418 - acc: 0.7254 - val_loss: 0.7392 - val_acc: 0.7248
Epoch 12/500
132/132 [==============================] - 31s 236ms/step - loss: 0.7141 - acc: 0.7407 - val_loss: 0.7202 - val_acc: 0.7339
Epoch 13/500
132/132 [==============================] - 31s 237ms/step - loss: 0.6714 - acc: 0.7566 - val_loss: 0.6961 - val_acc: 0.7156
Epoch 14/500
132/132 [==============================] - 31s 236ms/step - loss: 0.6654 - acc: 0.7575 - val_loss: 0.6512 - val_acc: 0.7569
Epoch 15/500
132/132 [==============================] - 31s 236ms/step - loss: 0.6455 - acc: 0.7678 - val_loss: 0.6714 - val_acc: 0.7339
Epoch 16/500
132/132 [==============================] - 31s 235ms/step - loss: 0.6024 - acc: 0.7786 - val_loss: 0.6384 - val_acc: 0.7615
Epoch 17/500
132/132 [==============================] - 31s 235ms/step - loss: 0.5885 - acc: 0.7879 - val_loss: 0.6411 - val_acc: 0.7431
Epoch 18/500
132/132 [==============================] - 31s 236ms/step - loss: 0.5698 - acc: 0.7901 - val_loss: 0.6291 - val_acc: 0.7523
Epoch 19/500
132/132 [==============================] - 31s 237ms/step - loss: 0.5343 - acc: 0.8097 - val_loss: 0.6221 - val_acc: 0.7523
Epoch 20/500
132/132 [==============================] - 31s 235ms/step - loss: 0.5392 - acc: 0.8032 - val_loss: 0.6486 - val_acc: 0.7477
Epoch 21/500
132/132 [==============================] - 31s 236ms/step - loss: 0.5175 - acc: 0.8122 - val_loss: 0.6426 - val_acc: 0.7615
Epoch 22/500
132/132 [==============================] - 31s 235ms/step - loss: 0.4968 - acc: 0.8150 - val_loss: 0.5974 - val_acc: 0.7752
Epoch 23/500
132/132 [==============================] - 31s 236ms/step - loss: 0.4849 - acc: 0.8201 - val_loss: 0.5778 - val_acc: 0.7661
Epoch 24/500
132/132 [==============================] - 31s 238ms/step - loss: 0.4647 - acc: 0.8323 - val_loss: 0.5829 - val_acc: 0.7798
Epoch 25/500
132/132 [==============================] - 31s 238ms/step - loss: 0.4440 - acc: 0.8377 - val_loss: 0.5612 - val_acc: 0.7798
Epoch 26/500
132/132 [==============================] - 32s 240ms/step - loss: 0.4339 - acc: 0.8401 - val_loss: 0.5767 - val_acc: 0.7752
Epoch 27/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4197 - acc: 0.8458 - val_loss: 0.5677 - val_acc: 0.7844
Epoch 28/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4046 - acc: 0.8522 - val_loss: 0.6004 - val_acc: 0.7615
Epoch 29/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3934 - acc: 0.8594 - val_loss: 0.5564 - val_acc: 0.7706
Epoch 30/500
132/132 [==============================] - 32s 239ms/step - loss: 0.3865 - acc: 0.8551 - val_loss: 0.5830 - val_acc: 0.7844
Epoch 31/500
132/132 [==============================] - 32s 239ms/step - loss: 0.3705 - acc: 0.8660 - val_loss: 0.5578 - val_acc: 0.7844
Epoch 32/500
132/132 [==============================] - 32s 239ms/step - loss: 0.3596 - acc: 0.8674 - val_loss: 0.5374 - val_acc: 0.8073
Epoch 33/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3423 - acc: 0.8795 - val_loss: 0.5763 - val_acc: 0.7844
Epoch 34/500
132/132 [==============================] - 32s 239ms/step - loss: 0.3372 - acc: 0.8786 - val_loss: 0.5465 - val_acc: 0.7982
Epoch 35/500
132/132 [==============================] - 31s 235ms/step - loss: 0.3088 - acc: 0.8842 - val_loss: 0.5604 - val_acc: 0.7936
Epoch 36/500
132/132 [==============================] - 31s 237ms/step - loss: 0.3155 - acc: 0.8832 - val_loss: 0.5977 - val_acc: 0.7890
Epoch 37/500
132/132 [==============================] - 31s 238ms/step - loss: 0.3128 - acc: 0.8875 - val_loss: 0.5506 - val_acc: 0.8211
Epoch 38/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3037 - acc: 0.8883 - val_loss: 0.5752 - val_acc: 0.8028
Epoch 39/500
132/132 [==============================] - 31s 236ms/step - loss: 0.3045 - acc: 0.8870 - val_loss: 0.6096 - val_acc: 0.8073
Epoch 40/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2752 - acc: 0.8971 - val_loss: 0.5810 - val_acc: 0.7936
Epoch 41/500
132/132 [==============================] - 32s 239ms/step - loss: 0.2737 - acc: 0.8970 - val_loss: 0.6378 - val_acc: 0.7982
Epoch 42/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2730 - acc: 0.8977 - val_loss: 0.6374 - val_acc: 0.7890
Epoch 43/500
132/132 [==============================] - 31s 238ms/step - loss: 0.2679 - acc: 0.9008 - val_loss: 0.6438 - val_acc: 0.7706
Epoch 44/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2607 - acc: 0.9011 - val_loss: 0.6324 - val_acc: 0.7844
Epoch 45/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2439 - acc: 0.9106 - val_loss: 0.7420 - val_acc: 0.7615
Epoch 46/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2355 - acc: 0.9121 - val_loss: 0.6119 - val_acc: 0.7890
Epoch 47/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2286 - acc: 0.9145 - val_loss: 0.6477 - val_acc: 0.7890
Epoch 48/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2427 - acc: 0.9100 - val_loss: 0.6842 - val_acc: 0.7752
Epoch 49/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2237 - acc: 0.9161 - val_loss: 0.6477 - val_acc: 0.8073
Epoch 50/500
132/132 [==============================] - 31s 238ms/step - loss: 0.2069 - acc: 0.9234 - val_loss: 0.6499 - val_acc: 0.8165
Epoch 51/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2037 - acc: 0.9258 - val_loss: 0.6863 - val_acc: 0.7752
Epoch 52/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2015 - acc: 0.9270 - val_loss: 0.6292 - val_acc: 0.7936
Epoch 53/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2044 - acc: 0.9249 - val_loss: 0.7519 - val_acc: 0.7752
Epoch 54/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1951 - acc: 0.9282 - val_loss: 0.7176 - val_acc: 0.8073
Epoch 55/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1825 - acc: 0.9379 - val_loss: 0.7114 - val_acc: 0.7844
Epoch 56/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1787 - acc: 0.9357 - val_loss: 0.7652 - val_acc: 0.7798
Epoch 57/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1541 - acc: 0.9429 - val_loss: 0.8025 - val_acc: 0.7890
Epoch 58/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1762 - acc: 0.9348 - val_loss: 0.6868 - val_acc: 0.7844
Epoch 59/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1573 - acc: 0.9430 - val_loss: 0.6965 - val_acc: 0.8073
Epoch 60/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1587 - acc: 0.9414 - val_loss: 0.8865 - val_acc: 0.7569
Epoch 61/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1535 - acc: 0.9415 - val_loss: 0.8049 - val_acc: 0.7844
Epoch 62/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1562 - acc: 0.9442 - val_loss: 0.8549 - val_acc: 0.7844
Epoch 63/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1599 - acc: 0.9436 - val_loss: 0.8668 - val_acc: 0.7844
Epoch 64/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1470 - acc: 0.9458 - val_loss: 0.9332 - val_acc: 0.7431
Epoch 65/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1381 - acc: 0.9512 - val_loss: 0.7894 - val_acc: 0.8028
Epoch 66/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1284 - acc: 0.9518 - val_loss: 0.8904 - val_acc: 0.7890
Epoch 67/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1333 - acc: 0.9514 - val_loss: 1.0124 - val_acc: 0.7661
Epoch 68/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1404 - acc: 0.9535 - val_loss: 1.1382 - val_acc: 0.7523
Epoch 69/500
132/132 [==============================] - 31s 234ms/step - loss: 0.1260 - acc: 0.9518 - val_loss: 0.9398 - val_acc: 0.7752
Epoch 70/500
132/132 [==============================] - 32s 240ms/step - loss: 0.1094 - acc: 0.9591 - val_loss: 1.1117 - val_acc: 0.7661
Epoch 71/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1200 - acc: 0.9570 - val_loss: 0.9844 - val_acc: 0.7661
Epoch 72/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1275 - acc: 0.9533 - val_loss: 1.0262 - val_acc: 0.7798
Epoch 73/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1267 - acc: 0.9551 - val_loss: 0.9854 - val_acc: 0.7569
Epoch 74/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1180 - acc: 0.9559 - val_loss: 0.9601 - val_acc: 0.7936
Epoch 75/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1060 - acc: 0.9635 - val_loss: 1.0178 - val_acc: 0.7844
Epoch 76/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1033 - acc: 0.9614 - val_loss: 1.0832 - val_acc: 0.7339
Epoch 77/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1095 - acc: 0.9639 - val_loss: 1.0915 - val_acc: 0.7569
Epoch 78/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1081 - acc: 0.9592 - val_loss: 1.1198 - val_acc: 0.7706
Epoch 79/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1046 - acc: 0.9639 - val_loss: 1.1740 - val_acc: 0.7569
Epoch 80/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0962 - acc: 0.9647 - val_loss: 1.0515 - val_acc: 0.7615
Epoch 81/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0959 - acc: 0.9661 - val_loss: 1.1223 - val_acc: 0.7615
Epoch 82/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0987 - acc: 0.9641 - val_loss: 1.1532 - val_acc: 0.7569
Epoch 83/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1011 - acc: 0.9621 - val_loss: 1.1070 - val_acc: 0.7798
Epoch 84/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0930 - acc: 0.9673 - val_loss: 0.9888 - val_acc: 0.7477
Epoch 85/500
132/132 [==============================] - 31s 237ms/step - loss: 0.1069 - acc: 0.9615 - val_loss: 1.0100 - val_acc: 0.7752
Epoch 86/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0917 - acc: 0.9665 - val_loss: 1.0643 - val_acc: 0.7752
Epoch 87/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0883 - acc: 0.9674 - val_loss: 1.1714 - val_acc: 0.7339
Epoch 88/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1013 - acc: 0.9605 - val_loss: 1.0219 - val_acc: 0.7890
Epoch 89/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0889 - acc: 0.9693 - val_loss: 1.0895 - val_acc: 0.7706
Epoch 90/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0862 - acc: 0.9695 - val_loss: 1.3182 - val_acc: 0.7248
Epoch 91/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0966 - acc: 0.9630 - val_loss: 1.1402 - val_acc: 0.7569
Epoch 92/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0967 - acc: 0.9639 - val_loss: 1.1219 - val_acc: 0.7569
Epoch 93/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0795 - acc: 0.9711 - val_loss: 1.1646 - val_acc: 0.8073
Epoch 94/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0806 - acc: 0.9697 - val_loss: 1.2709 - val_acc: 0.7615
Epoch 95/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0697 - acc: 0.9733 - val_loss: 1.1811 - val_acc: 0.7661
Epoch 96/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0841 - acc: 0.9673 - val_loss: 1.0818 - val_acc: 0.7523
Epoch 97/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0852 - acc: 0.9691 - val_loss: 1.2824 - val_acc: 0.7294
Epoch 98/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0850 - acc: 0.9680 - val_loss: 1.2548 - val_acc: 0.7615
Epoch 99/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0971 - acc: 0.9656 - val_loss: 1.1800 - val_acc: 0.7844
Epoch 100/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0957 - acc: 0.9682 - val_loss: 1.2631 - val_acc: 0.7569
Epoch 101/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0814 - acc: 0.9689 - val_loss: 1.1478 - val_acc: 0.7798
Epoch 102/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0826 - acc: 0.9714 - val_loss: 1.2999 - val_acc: 0.7477
Epoch 103/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0904 - acc: 0.9676 - val_loss: 1.2782 - val_acc: 0.7798
Epoch 104/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0766 - acc: 0.9726 - val_loss: 1.3682 - val_acc: 0.7339
Epoch 105/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0816 - acc: 0.9708 - val_loss: 1.3566 - val_acc: 0.7431
Epoch 106/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0845 - acc: 0.9682 - val_loss: 1.2604 - val_acc: 0.7431
Epoch 107/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0799 - acc: 0.9711 - val_loss: 1.3576 - val_acc: 0.7569
Epoch 108/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0811 - acc: 0.9689 - val_loss: 1.4515 - val_acc: 0.7615
Epoch 109/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0762 - acc: 0.9732 - val_loss: 1.6032 - val_acc: 0.7431
Epoch 110/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0813 - acc: 0.9683 - val_loss: 1.5373 - val_acc: 0.7706
Epoch 111/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0784 - acc: 0.9700 - val_loss: 1.4871 - val_acc: 0.7156
Epoch 112/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0580 - acc: 0.9768 - val_loss: 1.5892 - val_acc: 0.7339
Epoch 113/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0707 - acc: 0.9731 - val_loss: 1.6342 - val_acc: 0.7385
Epoch 114/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0692 - acc: 0.9735 - val_loss: 1.6565 - val_acc: 0.7339
Epoch 115/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0715 - acc: 0.9735 - val_loss: 1.5158 - val_acc: 0.7661
Epoch 116/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0706 - acc: 0.9732 - val_loss: 1.5715 - val_acc: 0.7202
Epoch 117/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0748 - acc: 0.9738 - val_loss: 1.5778 - val_acc: 0.7385
Epoch 118/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0740 - acc: 0.9742 - val_loss: 1.7383 - val_acc: 0.7156
Epoch 119/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0745 - acc: 0.9742 - val_loss: 1.9643 - val_acc: 0.7569
Epoch 120/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0696 - acc: 0.9732 - val_loss: 1.6949 - val_acc: 0.7248
Epoch 121/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0565 - acc: 0.9788 - val_loss: 1.4983 - val_acc: 0.7477
Epoch 122/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0620 - acc: 0.9752 - val_loss: 1.7683 - val_acc: 0.7202
Epoch 123/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0588 - acc: 0.9786 - val_loss: 1.5904 - val_acc: 0.7385
Epoch 124/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0751 - acc: 0.9729 - val_loss: 1.8461 - val_acc: 0.7064
Epoch 125/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0631 - acc: 0.9764 - val_loss: 1.7049 - val_acc: 0.7156
Epoch 126/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0672 - acc: 0.9745 - val_loss: 1.6678 - val_acc: 0.7294
Epoch 127/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0717 - acc: 0.9755 - val_loss: 1.8081 - val_acc: 0.6972
Epoch 128/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0700 - acc: 0.9739 - val_loss: 1.5788 - val_acc: 0.7431
Epoch 129/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0618 - acc: 0.9773 - val_loss: 1.4797 - val_acc: 0.7385
Epoch 130/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0699 - acc: 0.9752 - val_loss: 1.6789 - val_acc: 0.7477
Epoch 131/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0605 - acc: 0.9773 - val_loss: 1.8969 - val_acc: 0.7294
Epoch 132/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0691 - acc: 0.9742 - val_loss: 1.5051 - val_acc: 0.7385
Epoch 133/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0762 - acc: 0.9720 - val_loss: 1.5023 - val_acc: 0.7385
Epoch 134/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0676 - acc: 0.9780 - val_loss: 1.5186 - val_acc: 0.7523
Epoch 135/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0593 - acc: 0.9782 - val_loss: 1.5831 - val_acc: 0.7339
Epoch 136/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0533 - acc: 0.9798 - val_loss: 1.5367 - val_acc: 0.7294
Epoch 137/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0514 - acc: 0.9808 - val_loss: 1.5987 - val_acc: 0.7615
Epoch 138/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0740 - acc: 0.9752 - val_loss: 1.3186 - val_acc: 0.7431
Epoch 139/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0611 - acc: 0.9755 - val_loss: 1.4729 - val_acc: 0.7706
Epoch 140/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0521 - acc: 0.9808 - val_loss: 1.6559 - val_acc: 0.7385
Epoch 141/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0504 - acc: 0.9824 - val_loss: 1.5976 - val_acc: 0.7385
Epoch 142/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0566 - acc: 0.9798 - val_loss: 1.7758 - val_acc: 0.7294
Epoch 143/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0617 - acc: 0.9793 - val_loss: 1.6027 - val_acc: 0.7294
Epoch 144/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0581 - acc: 0.9791 - val_loss: 1.3961 - val_acc: 0.7569
Epoch 145/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0596 - acc: 0.9794 - val_loss: 1.6675 - val_acc: 0.7339
Epoch 146/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0481 - acc: 0.9829 - val_loss: 1.7066 - val_acc: 0.7294
Epoch 147/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0498 - acc: 0.9821 - val_loss: 1.5960 - val_acc: 0.7385
Epoch 148/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0463 - acc: 0.9830 - val_loss: 1.5807 - val_acc: 0.7294
Epoch 149/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0582 - acc: 0.9768 - val_loss: 1.5773 - val_acc: 0.7385
Epoch 150/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0543 - acc: 0.9809 - val_loss: 1.8141 - val_acc: 0.7661
Epoch 151/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0520 - acc: 0.9808 - val_loss: 1.6006 - val_acc: 0.7385
Epoch 152/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0560 - acc: 0.9800 - val_loss: 1.6500 - val_acc: 0.7385
Epoch 153/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0608 - acc: 0.9778 - val_loss: 1.5356 - val_acc: 0.7385
Epoch 154/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0551 - acc: 0.9795 - val_loss: 1.5699 - val_acc: 0.7431
Epoch 155/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0442 - acc: 0.9824 - val_loss: 1.4665 - val_acc: 0.7569
Epoch 156/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0452 - acc: 0.9845 - val_loss: 1.4092 - val_acc: 0.7661
Epoch 157/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0512 - acc: 0.9814 - val_loss: 1.5766 - val_acc: 0.7569
Epoch 158/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0502 - acc: 0.9817 - val_loss: 1.4547 - val_acc: 0.7615
Epoch 159/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0473 - acc: 0.9839 - val_loss: 1.5393 - val_acc: 0.7431
Epoch 160/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0451 - acc: 0.9815 - val_loss: 1.7248 - val_acc: 0.7339
Epoch 161/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0520 - acc: 0.9809 - val_loss: 1.8998 - val_acc: 0.6927
Epoch 162/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0550 - acc: 0.9800 - val_loss: 1.7797 - val_acc: 0.7156
Epoch 163/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0488 - acc: 0.9811 - val_loss: 1.7420 - val_acc: 0.7569
Epoch 164/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0460 - acc: 0.9828 - val_loss: 1.6905 - val_acc: 0.7339
Epoch 165/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0737 - acc: 0.9729 - val_loss: 1.4907 - val_acc: 0.7752
Epoch 166/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0562 - acc: 0.9782 - val_loss: 1.4150 - val_acc: 0.7523
Epoch 167/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0525 - acc: 0.9814 - val_loss: 1.6370 - val_acc: 0.7339
Epoch 168/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0534 - acc: 0.9817 - val_loss: 1.8255 - val_acc: 0.7431
Epoch 169/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0452 - acc: 0.9845 - val_loss: 2.0394 - val_acc: 0.7431
Epoch 170/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0456 - acc: 0.9829 - val_loss: 1.8344 - val_acc: 0.7294
Epoch 171/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0510 - acc: 0.9814 - val_loss: 1.8005 - val_acc: 0.7615
Epoch 172/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0532 - acc: 0.9808 - val_loss: 1.6577 - val_acc: 0.7569
Epoch 173/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0456 - acc: 0.9838 - val_loss: 1.6550 - val_acc: 0.7615
Epoch 174/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0469 - acc: 0.9827 - val_loss: 1.9758 - val_acc: 0.7431
Epoch 175/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0563 - acc: 0.9798 - val_loss: 1.9374 - val_acc: 0.7110
Epoch 176/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0463 - acc: 0.9826 - val_loss: 1.6287 - val_acc: 0.7615
Epoch 177/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0636 - acc: 0.9780 - val_loss: 1.4599 - val_acc: 0.7431
Epoch 178/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0519 - acc: 0.9810 - val_loss: 1.5591 - val_acc: 0.7294
Epoch 179/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0404 - acc: 0.9859 - val_loss: 1.7109 - val_acc: 0.7615
Epoch 180/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0636 - acc: 0.9777 - val_loss: 1.5713 - val_acc: 0.7385
Epoch 181/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0403 - acc: 0.9844 - val_loss: 1.7166 - val_acc: 0.7569
Epoch 182/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0441 - acc: 0.9826 - val_loss: 1.5325 - val_acc: 0.7385
Epoch 183/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0401 - acc: 0.9864 - val_loss: 1.7144 - val_acc: 0.7248
Epoch 184/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0481 - acc: 0.9838 - val_loss: 1.6083 - val_acc: 0.7431
Epoch 185/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0449 - acc: 0.9839 - val_loss: 1.7882 - val_acc: 0.7477
Epoch 186/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0480 - acc: 0.9823 - val_loss: 1.7669 - val_acc: 0.7110
Epoch 187/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0382 - acc: 0.9859 - val_loss: 1.7447 - val_acc: 0.7477
Epoch 188/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0368 - acc: 0.9865 - val_loss: 1.5073 - val_acc: 0.7569
Epoch 189/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0495 - acc: 0.9835 - val_loss: 1.5511 - val_acc: 0.7706
Epoch 190/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0515 - acc: 0.9832 - val_loss: 1.5953 - val_acc: 0.7431
Epoch 191/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0461 - acc: 0.9833 - val_loss: 1.4416 - val_acc: 0.7569
Epoch 192/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0412 - acc: 0.9838 - val_loss: 1.8462 - val_acc: 0.7064
Epoch 193/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0358 - acc: 0.9868 - val_loss: 1.9981 - val_acc: 0.7248
Epoch 194/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0434 - acc: 0.9833 - val_loss: 1.6141 - val_acc: 0.7294
Epoch 195/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0515 - acc: 0.9824 - val_loss: 1.7521 - val_acc: 0.7569
Epoch 196/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0388 - acc: 0.9859 - val_loss: 1.6874 - val_acc: 0.7431
Epoch 197/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0536 - acc: 0.9792 - val_loss: 1.9006 - val_acc: 0.7248
Epoch 198/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0414 - acc: 0.9836 - val_loss: 1.6312 - val_acc: 0.7431
Epoch 199/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0498 - acc: 0.9829 - val_loss: 1.6641 - val_acc: 0.7752
Epoch 200/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0407 - acc: 0.9852 - val_loss: 1.7554 - val_acc: 0.7385
Epoch 201/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0391 - acc: 0.9845 - val_loss: 1.5845 - val_acc: 0.7385
Epoch 202/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0569 - acc: 0.9809 - val_loss: 1.7489 - val_acc: 0.7248
Epoch 203/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0401 - acc: 0.9847 - val_loss: 1.6178 - val_acc: 0.7294
Epoch 204/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0458 - acc: 0.9827 - val_loss: 1.7603 - val_acc: 0.7706
Epoch 205/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0476 - acc: 0.9824 - val_loss: 1.9155 - val_acc: 0.7294
Epoch 206/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0386 - acc: 0.9855 - val_loss: 1.8425 - val_acc: 0.7431
Epoch 207/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0440 - acc: 0.9834 - val_loss: 1.7224 - val_acc: 0.7523
Epoch 208/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0577 - acc: 0.9808 - val_loss: 1.4803 - val_acc: 0.7615
Epoch 209/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0472 - acc: 0.9832 - val_loss: 1.6493 - val_acc: 0.7339
Epoch 210/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0411 - acc: 0.9856 - val_loss: 1.7630 - val_acc: 0.7156
Epoch 211/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0436 - acc: 0.9845 - val_loss: 1.6773 - val_acc: 0.7477
Epoch 212/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0448 - acc: 0.9849 - val_loss: 1.6918 - val_acc: 0.7431
Epoch 213/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0558 - acc: 0.9798 - val_loss: 1.9022 - val_acc: 0.7156
Epoch 214/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0593 - acc: 0.9829 - val_loss: 1.7612 - val_acc: 0.7248
Epoch 215/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0406 - acc: 0.9848 - val_loss: 1.6259 - val_acc: 0.7798
Epoch 216/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0342 - acc: 0.9877 - val_loss: 1.6607 - val_acc: 0.7569
Epoch 217/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0276 - acc: 0.9899 - val_loss: 1.8203 - val_acc: 0.7431
Epoch 218/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0424 - acc: 0.9858 - val_loss: 2.0180 - val_acc: 0.7110
Epoch 219/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0329 - acc: 0.9886 - val_loss: 1.6891 - val_acc: 0.7339
Epoch 220/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0485 - acc: 0.9838 - val_loss: 2.1075 - val_acc: 0.7477
Epoch 221/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0545 - acc: 0.9812 - val_loss: 1.8929 - val_acc: 0.7569
Epoch 222/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0365 - acc: 0.9862 - val_loss: 1.7563 - val_acc: 0.7661
Epoch 223/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0331 - acc: 0.9871 - val_loss: 1.6038 - val_acc: 0.7844
Epoch 224/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0431 - acc: 0.9832 - val_loss: 1.5931 - val_acc: 0.7477
Epoch 225/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0329 - acc: 0.9873 - val_loss: 1.7447 - val_acc: 0.7431
Epoch 226/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0305 - acc: 0.9891 - val_loss: 2.1371 - val_acc: 0.7110
Epoch 227/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0401 - acc: 0.9867 - val_loss: 1.9234 - val_acc: 0.7431
Epoch 228/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0511 - acc: 0.9824 - val_loss: 1.7959 - val_acc: 0.7294
Epoch 229/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0399 - acc: 0.9856 - val_loss: 1.7012 - val_acc: 0.7661
Epoch 230/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0337 - acc: 0.9897 - val_loss: 1.8467 - val_acc: 0.7202
Epoch 231/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0308 - acc: 0.9883 - val_loss: 2.2905 - val_acc: 0.7339
Epoch 232/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0365 - acc: 0.9873 - val_loss: 1.7290 - val_acc: 0.7752
Epoch 233/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0470 - acc: 0.9855 - val_loss: 1.7175 - val_acc: 0.7661
Epoch 234/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0604 - acc: 0.9777 - val_loss: 1.8741 - val_acc: 0.7156
Epoch 235/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0362 - acc: 0.9870 - val_loss: 1.8629 - val_acc: 0.7706
Epoch 236/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0406 - acc: 0.9850 - val_loss: 1.7602 - val_acc: 0.7385
Epoch 237/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0354 - acc: 0.9865 - val_loss: 1.9326 - val_acc: 0.7615
Epoch 238/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0403 - acc: 0.9859 - val_loss: 1.7243 - val_acc: 0.7523
Epoch 239/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0395 - acc: 0.9876 - val_loss: 1.5925 - val_acc: 0.7706
Epoch 240/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0296 - acc: 0.9889 - val_loss: 1.8088 - val_acc: 0.7615
Epoch 241/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0327 - acc: 0.9895 - val_loss: 1.7601 - val_acc: 0.7569
Epoch 242/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0345 - acc: 0.9873 - val_loss: 1.8273 - val_acc: 0.7569
Epoch 243/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0348 - acc: 0.9882 - val_loss: 2.0377 - val_acc: 0.7202
Epoch 244/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0414 - acc: 0.9867 - val_loss: 1.9932 - val_acc: 0.7156
Epoch 245/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0393 - acc: 0.9868 - val_loss: 1.8168 - val_acc: 0.7706
Epoch 246/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0340 - acc: 0.9861 - val_loss: 1.7184 - val_acc: 0.7523
Epoch 247/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0588 - acc: 0.9805 - val_loss: 1.7863 - val_acc: 0.7661
Epoch 248/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0456 - acc: 0.9836 - val_loss: 2.1485 - val_acc: 0.7523
Epoch 249/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0482 - acc: 0.9848 - val_loss: 1.8454 - val_acc: 0.7431
Epoch 250/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0267 - acc: 0.9906 - val_loss: 2.3220 - val_acc: 0.7202
Epoch 251/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0428 - acc: 0.9847 - val_loss: 2.1748 - val_acc: 0.7248
Epoch 252/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0369 - acc: 0.9841 - val_loss: 2.0213 - val_acc: 0.7431
Epoch 253/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0492 - acc: 0.9816 - val_loss: 1.7994 - val_acc: 0.7431
Epoch 254/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0285 - acc: 0.9902 - val_loss: 1.7815 - val_acc: 0.7569
Epoch 255/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0348 - acc: 0.9879 - val_loss: 1.7223 - val_acc: 0.7523
Epoch 256/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0258 - acc: 0.9903 - val_loss: 2.3399 - val_acc: 0.7294
Epoch 257/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0362 - acc: 0.9891 - val_loss: 2.2601 - val_acc: 0.7202
Epoch 258/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0295 - acc: 0.9900 - val_loss: 2.5309 - val_acc: 0.7294
Epoch 259/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0381 - acc: 0.9861 - val_loss: 1.9240 - val_acc: 0.7477
Epoch 260/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0427 - acc: 0.9858 - val_loss: 2.2211 - val_acc: 0.7385
Epoch 261/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0316 - acc: 0.9892 - val_loss: 2.2438 - val_acc: 0.7385
Epoch 262/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0340 - acc: 0.9879 - val_loss: 2.1968 - val_acc: 0.7431
Epoch 263/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0306 - acc: 0.9882 - val_loss: 2.0068 - val_acc: 0.7477
Epoch 264/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0604 - acc: 0.9788 - val_loss: 2.0898 - val_acc: 0.7385
Epoch 265/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0363 - acc: 0.9871 - val_loss: 2.0140 - val_acc: 0.7385
Epoch 266/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0280 - acc: 0.9902 - val_loss: 1.7354 - val_acc: 0.7752
Epoch 267/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0359 - acc: 0.9865 - val_loss: 1.9552 - val_acc: 0.7477
Epoch 268/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0341 - acc: 0.9877 - val_loss: 2.0641 - val_acc: 0.7385
Epoch 269/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0520 - acc: 0.9818 - val_loss: 2.0669 - val_acc: 0.7523
Epoch 270/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0418 - acc: 0.9872 - val_loss: 2.1326 - val_acc: 0.7569
Epoch 271/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0416 - acc: 0.9855 - val_loss: 2.0630 - val_acc: 0.7248
Epoch 272/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0358 - acc: 0.9859 - val_loss: 2.0706 - val_acc: 0.7294
Epoch 273/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0285 - acc: 0.9914 - val_loss: 2.1166 - val_acc: 0.7248
Epoch 274/500
132/132 [==============================] - 34s 258ms/step - loss: 0.0286 - acc: 0.9912 - val_loss: 2.0190 - val_acc: 0.7294
Epoch 275/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0378 - acc: 0.9871 - val_loss: 2.7007 - val_acc: 0.7064
Epoch 276/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0355 - acc: 0.9874 - val_loss: 2.0525 - val_acc: 0.7385
Epoch 277/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0434 - acc: 0.9847 - val_loss: 2.3277 - val_acc: 0.7248
Epoch 278/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0395 - acc: 0.9867 - val_loss: 1.8885 - val_acc: 0.7294
Epoch 279/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0361 - acc: 0.9880 - val_loss: 2.0688 - val_acc: 0.7248
Epoch 280/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0336 - acc: 0.9879 - val_loss: 2.1241 - val_acc: 0.7248
Epoch 281/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0380 - acc: 0.9868 - val_loss: 2.2616 - val_acc: 0.7431
Epoch 282/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0373 - acc: 0.9868 - val_loss: 1.9479 - val_acc: 0.7339
Epoch 283/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0351 - acc: 0.9871 - val_loss: 1.7576 - val_acc: 0.7523
Epoch 284/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0370 - acc: 0.9891 - val_loss: 1.7624 - val_acc: 0.7569
Epoch 285/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0354 - acc: 0.9885 - val_loss: 2.0107 - val_acc: 0.7294
Epoch 286/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0345 - acc: 0.9882 - val_loss: 2.3340 - val_acc: 0.7248
Epoch 287/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0316 - acc: 0.9895 - val_loss: 1.8717 - val_acc: 0.7569
Epoch 288/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0378 - acc: 0.9870 - val_loss: 2.1321 - val_acc: 0.7202
Epoch 289/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0283 - acc: 0.9888 - val_loss: 2.0779 - val_acc: 0.7477
Epoch 290/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0272 - acc: 0.9894 - val_loss: 2.2171 - val_acc: 0.7477
Epoch 291/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0398 - acc: 0.9876 - val_loss: 1.5727 - val_acc: 0.7706
Epoch 292/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0422 - acc: 0.9853 - val_loss: 1.9629 - val_acc: 0.7477
Epoch 293/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0437 - acc: 0.9861 - val_loss: 2.0494 - val_acc: 0.6835
Epoch 294/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0439 - acc: 0.9859 - val_loss: 2.1657 - val_acc: 0.7431
Epoch 295/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0316 - acc: 0.9880 - val_loss: 2.1048 - val_acc: 0.7385
Epoch 296/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0277 - acc: 0.9905 - val_loss: 2.0242 - val_acc: 0.7431
Epoch 297/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0300 - acc: 0.9898 - val_loss: 1.9756 - val_acc: 0.7431
Epoch 298/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0389 - acc: 0.9859 - val_loss: 2.0851 - val_acc: 0.7248
Epoch 299/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0222 - acc: 0.9920 - val_loss: 2.1150 - val_acc: 0.7385
Epoch 300/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0331 - acc: 0.9894 - val_loss: 2.0452 - val_acc: 0.7248
Epoch 301/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0242 - acc: 0.9908 - val_loss: 1.9959 - val_acc: 0.7569
Epoch 302/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9905 - val_loss: 2.1234 - val_acc: 0.7431
Epoch 303/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0424 - acc: 0.9861 - val_loss: 2.3179 - val_acc: 0.7523
Epoch 304/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0362 - acc: 0.9882 - val_loss: 2.4917 - val_acc: 0.7156
Epoch 305/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0342 - acc: 0.9883 - val_loss: 2.1547 - val_acc: 0.7523
Epoch 306/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0382 - acc: 0.9844 - val_loss: 2.2382 - val_acc: 0.7431
Epoch 307/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0331 - acc: 0.9883 - val_loss: 1.8640 - val_acc: 0.7615
Epoch 308/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0172 - acc: 0.9933 - val_loss: 2.1068 - val_acc: 0.7431
Epoch 309/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0370 - acc: 0.9885 - val_loss: 2.1252 - val_acc: 0.7339
Epoch 310/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0366 - acc: 0.9871 - val_loss: 2.0135 - val_acc: 0.7431
Epoch 311/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0327 - acc: 0.9885 - val_loss: 1.9755 - val_acc: 0.7477
Epoch 312/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0309 - acc: 0.9883 - val_loss: 2.1393 - val_acc: 0.7477
Epoch 313/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0428 - acc: 0.9848 - val_loss: 2.1250 - val_acc: 0.7661
Epoch 314/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0381 - acc: 0.9855 - val_loss: 1.8768 - val_acc: 0.7431
Epoch 315/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0273 - acc: 0.9888 - val_loss: 1.6826 - val_acc: 0.7936
Epoch 316/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0286 - acc: 0.9900 - val_loss: 2.0218 - val_acc: 0.7477
Epoch 317/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0207 - acc: 0.9911 - val_loss: 2.2351 - val_acc: 0.7202
Epoch 318/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0234 - acc: 0.9914 - val_loss: 1.9849 - val_acc: 0.7339
Epoch 319/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0283 - acc: 0.9895 - val_loss: 2.1402 - val_acc: 0.7385
Epoch 320/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0324 - acc: 0.9892 - val_loss: 2.0350 - val_acc: 0.7385
Epoch 321/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0462 - acc: 0.9850 - val_loss: 2.1039 - val_acc: 0.7385
Epoch 322/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0338 - acc: 0.9891 - val_loss: 2.0334 - val_acc: 0.7339
Epoch 323/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0385 - acc: 0.9864 - val_loss: 1.9096 - val_acc: 0.7431
Epoch 324/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0251 - acc: 0.9905 - val_loss: 2.0349 - val_acc: 0.7569
Epoch 325/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0311 - acc: 0.9886 - val_loss: 2.0710 - val_acc: 0.7661
Epoch 326/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0333 - acc: 0.9874 - val_loss: 2.2878 - val_acc: 0.7110
Epoch 327/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0393 - acc: 0.9877 - val_loss: 2.1310 - val_acc: 0.7294
Epoch 328/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0359 - acc: 0.9879 - val_loss: 2.1467 - val_acc: 0.7202
Epoch 329/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0361 - acc: 0.9868 - val_loss: 1.7801 - val_acc: 0.7661
Epoch 330/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0303 - acc: 0.9906 - val_loss: 2.4909 - val_acc: 0.7339
Epoch 331/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0420 - acc: 0.9853 - val_loss: 1.5070 - val_acc: 0.7752
Epoch 332/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0349 - acc: 0.9894 - val_loss: 1.9991 - val_acc: 0.7431
Epoch 333/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0259 - acc: 0.9892 - val_loss: 2.4782 - val_acc: 0.7064
Epoch 334/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0508 - acc: 0.9838 - val_loss: 1.8322 - val_acc: 0.7385
Epoch 335/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0249 - acc: 0.9918 - val_loss: 1.8159 - val_acc: 0.7615
Epoch 336/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0226 - acc: 0.9912 - val_loss: 2.0992 - val_acc: 0.7477
Epoch 337/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0367 - acc: 0.9874 - val_loss: 1.9986 - val_acc: 0.7477
Epoch 338/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0592 - acc: 0.9845 - val_loss: 1.7246 - val_acc: 0.7661
Epoch 339/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0408 - acc: 0.9856 - val_loss: 1.6653 - val_acc: 0.7385
Epoch 340/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0336 - acc: 0.9894 - val_loss: 1.8577 - val_acc: 0.7752
Epoch 341/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0259 - acc: 0.9903 - val_loss: 1.7648 - val_acc: 0.7706
Epoch 342/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0339 - acc: 0.9898 - val_loss: 2.3049 - val_acc: 0.7385
Epoch 343/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0264 - acc: 0.9905 - val_loss: 1.9711 - val_acc: 0.7477
Epoch 344/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0299 - acc: 0.9882 - val_loss: 1.8723 - val_acc: 0.7569
Epoch 345/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0370 - acc: 0.9868 - val_loss: 1.8906 - val_acc: 0.7706
Epoch 346/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0388 - acc: 0.9856 - val_loss: 2.0246 - val_acc: 0.7339
Epoch 347/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0304 - acc: 0.9892 - val_loss: 2.6242 - val_acc: 0.7339
Epoch 348/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0344 - acc: 0.9873 - val_loss: 2.0247 - val_acc: 0.7615
Epoch 349/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0293 - acc: 0.9889 - val_loss: 2.6537 - val_acc: 0.7018
Epoch 350/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0328 - acc: 0.9877 - val_loss: 2.4254 - val_acc: 0.7385
Epoch 351/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0455 - acc: 0.9852 - val_loss: 2.2927 - val_acc: 0.7248
Epoch 352/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0281 - acc: 0.9895 - val_loss: 1.9068 - val_acc: 0.7615
Epoch 353/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0310 - acc: 0.9886 - val_loss: 1.9125 - val_acc: 0.7569
Epoch 354/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0359 - acc: 0.9883 - val_loss: 1.9286 - val_acc: 0.7661
Epoch 355/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0298 - acc: 0.9886 - val_loss: 2.3038 - val_acc: 0.7431
Epoch 356/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0360 - acc: 0.9862 - val_loss: 2.0062 - val_acc: 0.7431
Epoch 357/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0355 - acc: 0.9883 - val_loss: 2.2361 - val_acc: 0.7202
Epoch 358/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0362 - acc: 0.9870 - val_loss: 1.5870 - val_acc: 0.7706
Epoch 359/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0339 - acc: 0.9886 - val_loss: 1.7597 - val_acc: 0.7661
Epoch 360/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0390 - acc: 0.9877 - val_loss: 1.6509 - val_acc: 0.7706
Epoch 361/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0279 - acc: 0.9897 - val_loss: 1.6342 - val_acc: 0.7844
Epoch 362/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0247 - acc: 0.9926 - val_loss: 1.9049 - val_acc: 0.7385
Epoch 363/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9903 - val_loss: 1.8112 - val_acc: 0.7615
Epoch 364/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0315 - acc: 0.9893 - val_loss: 2.1787 - val_acc: 0.7569
Epoch 365/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0478 - acc: 0.9870 - val_loss: 1.7550 - val_acc: 0.7661
Epoch 366/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0440 - acc: 0.9849 - val_loss: 2.1983 - val_acc: 0.7339
Epoch 367/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0317 - acc: 0.9886 - val_loss: 1.9336 - val_acc: 0.7661
Epoch 368/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0297 - acc: 0.9886 - val_loss: 2.0492 - val_acc: 0.7569
Epoch 369/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0527 - acc: 0.9836 - val_loss: 1.6877 - val_acc: 0.7294
Epoch 370/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0279 - acc: 0.9917 - val_loss: 2.0460 - val_acc: 0.7569
Epoch 371/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0283 - acc: 0.9903 - val_loss: 1.9888 - val_acc: 0.7706
Epoch 372/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0232 - acc: 0.9917 - val_loss: 2.0474 - val_acc: 0.7706
Epoch 373/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0271 - acc: 0.9895 - val_loss: 2.0034 - val_acc: 0.7706
Epoch 374/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0258 - acc: 0.9898 - val_loss: 1.9553 - val_acc: 0.7385
Epoch 375/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0365 - acc: 0.9874 - val_loss: 2.1266 - val_acc: 0.7477
Epoch 376/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0208 - acc: 0.9929 - val_loss: 2.1296 - val_acc: 0.7294
Epoch 377/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0212 - acc: 0.9921 - val_loss: 2.0992 - val_acc: 0.7752
Epoch 378/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0277 - acc: 0.9903 - val_loss: 2.1996 - val_acc: 0.7523
Epoch 379/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0371 - acc: 0.9874 - val_loss: 1.9324 - val_acc: 0.7431
Epoch 380/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0299 - acc: 0.9887 - val_loss: 1.9771 - val_acc: 0.7615
Epoch 381/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0299 - acc: 0.9897 - val_loss: 2.3584 - val_acc: 0.7431
Epoch 382/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0217 - acc: 0.9917 - val_loss: 2.1724 - val_acc: 0.7523
Epoch 383/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0522 - acc: 0.9845 - val_loss: 1.8958 - val_acc: 0.7339
Epoch 384/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0419 - acc: 0.9862 - val_loss: 2.1133 - val_acc: 0.7569
Epoch 385/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0347 - acc: 0.9886 - val_loss: 2.3449 - val_acc: 0.7202
Epoch 386/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0175 - acc: 0.9938 - val_loss: 2.1303 - val_acc: 0.7661
Epoch 387/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0194 - acc: 0.9939 - val_loss: 2.1290 - val_acc: 0.7523
Epoch 388/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0273 - acc: 0.9915 - val_loss: 1.9582 - val_acc: 0.7798
Epoch 389/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0231 - acc: 0.9914 - val_loss: 1.9768 - val_acc: 0.7936
Epoch 390/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0382 - acc: 0.9867 - val_loss: 1.8773 - val_acc: 0.7569
Epoch 391/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0319 - acc: 0.9874 - val_loss: 2.0982 - val_acc: 0.7477
Epoch 392/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0410 - acc: 0.9873 - val_loss: 2.0022 - val_acc: 0.7844
Epoch 393/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0200 - acc: 0.9921 - val_loss: 2.1377 - val_acc: 0.7294
Epoch 394/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0279 - acc: 0.9908 - val_loss: 2.2346 - val_acc: 0.7661
Epoch 395/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0241 - acc: 0.9924 - val_loss: 2.4024 - val_acc: 0.7431
Epoch 396/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0301 - acc: 0.9895 - val_loss: 2.0233 - val_acc: 0.7294
Epoch 397/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0270 - acc: 0.9911 - val_loss: 2.3563 - val_acc: 0.7798
Epoch 398/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0312 - acc: 0.9915 - val_loss: 2.0839 - val_acc: 0.7752
Epoch 399/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0346 - acc: 0.9876 - val_loss: 2.2198 - val_acc: 0.7385
Epoch 400/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0282 - acc: 0.9903 - val_loss: 2.0448 - val_acc: 0.7615
Epoch 401/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9912 - val_loss: 1.8864 - val_acc: 0.7706
Epoch 402/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0260 - acc: 0.9905 - val_loss: 2.3783 - val_acc: 0.7339
Epoch 403/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0246 - acc: 0.9915 - val_loss: 2.2988 - val_acc: 0.7248
Epoch 404/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0351 - acc: 0.9874 - val_loss: 2.2196 - val_acc: 0.7523
Epoch 405/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9902 - val_loss: 2.0591 - val_acc: 0.7523
Epoch 406/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0246 - acc: 0.9908 - val_loss: 2.0662 - val_acc: 0.7339
Epoch 407/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0352 - acc: 0.9876 - val_loss: 1.9495 - val_acc: 0.7569
Epoch 408/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0372 - acc: 0.9888 - val_loss: 2.0497 - val_acc: 0.7477
Epoch 409/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0244 - acc: 0.9902 - val_loss: 2.2666 - val_acc: 0.7523
Epoch 410/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0353 - acc: 0.9879 - val_loss: 2.4305 - val_acc: 0.7248
Epoch 411/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0285 - acc: 0.9882 - val_loss: 2.4373 - val_acc: 0.7248
Epoch 412/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0228 - acc: 0.9908 - val_loss: 2.5699 - val_acc: 0.7248
Epoch 413/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9917 - val_loss: 2.3835 - val_acc: 0.7385
Epoch 414/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0232 - acc: 0.9909 - val_loss: 2.0311 - val_acc: 0.7661
Epoch 415/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0300 - acc: 0.9902 - val_loss: 1.9197 - val_acc: 0.7523
Epoch 416/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9898 - val_loss: 2.2117 - val_acc: 0.7569
Epoch 417/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0416 - acc: 0.9874 - val_loss: 2.1320 - val_acc: 0.7385
Epoch 418/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0298 - acc: 0.9901 - val_loss: 2.2242 - val_acc: 0.7431
Epoch 419/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0270 - acc: 0.9908 - val_loss: 2.2912 - val_acc: 0.7202
Epoch 420/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0538 - acc: 0.9827 - val_loss: 2.0445 - val_acc: 0.7706
Epoch 421/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0242 - acc: 0.9905 - val_loss: 2.1947 - val_acc: 0.7661
Epoch 422/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0269 - acc: 0.9914 - val_loss: 2.3270 - val_acc: 0.7431
Epoch 423/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0243 - acc: 0.9927 - val_loss: 2.3757 - val_acc: 0.7661
Epoch 424/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0375 - acc: 0.9903 - val_loss: 2.0836 - val_acc: 0.7385
Epoch 425/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0190 - acc: 0.9927 - val_loss: 2.2266 - val_acc: 0.7752
Epoch 426/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0267 - acc: 0.9909 - val_loss: 2.1181 - val_acc: 0.7569
Epoch 427/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0240 - acc: 0.9915 - val_loss: 2.3338 - val_acc: 0.7523
Epoch 428/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0222 - acc: 0.9918 - val_loss: 2.2608 - val_acc: 0.7752
Epoch 429/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0200 - acc: 0.9918 - val_loss: 2.2592 - val_acc: 0.7477
Epoch 430/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0256 - acc: 0.9918 - val_loss: 2.1343 - val_acc: 0.7339
Epoch 431/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0262 - acc: 0.9898 - val_loss: 2.3575 - val_acc: 0.7431
Epoch 432/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0311 - acc: 0.9895 - val_loss: 2.1274 - val_acc: 0.7523
Epoch 433/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0431 - acc: 0.9867 - val_loss: 2.1678 - val_acc: 0.7248
Epoch 434/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0253 - acc: 0.9905 - val_loss: 1.8056 - val_acc: 0.7890
Epoch 435/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0292 - acc: 0.9908 - val_loss: 1.9190 - val_acc: 0.7661
Epoch 436/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0435 - acc: 0.9873 - val_loss: 2.0889 - val_acc: 0.7569
Epoch 437/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0415 - acc: 0.9883 - val_loss: 2.0200 - val_acc: 0.7477
Epoch 438/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0289 - acc: 0.9894 - val_loss: 1.9863 - val_acc: 0.7431
Epoch 439/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0303 - acc: 0.9889 - val_loss: 1.7516 - val_acc: 0.7752
Epoch 440/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0213 - acc: 0.9917 - val_loss: 2.3748 - val_acc: 0.7294
Epoch 441/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0272 - acc: 0.9918 - val_loss: 1.8330 - val_acc: 0.7523
Epoch 442/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0270 - acc: 0.9895 - val_loss: 2.4473 - val_acc: 0.7294
Epoch 443/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0268 - acc: 0.9903 - val_loss: 2.1228 - val_acc: 0.7385
Epoch 444/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0215 - acc: 0.9909 - val_loss: 2.1025 - val_acc: 0.7477
Epoch 445/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0285 - acc: 0.9895 - val_loss: 1.9090 - val_acc: 0.7752
Epoch 446/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0225 - acc: 0.9915 - val_loss: 2.0702 - val_acc: 0.7385
Epoch 447/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0236 - acc: 0.9920 - val_loss: 2.0793 - val_acc: 0.7661
Epoch 448/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0251 - acc: 0.9912 - val_loss: 2.1253 - val_acc: 0.7752
Epoch 449/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0369 - acc: 0.9900 - val_loss: 1.9887 - val_acc: 0.7523
Epoch 450/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0334 - acc: 0.9883 - val_loss: 2.0484 - val_acc: 0.7523
Epoch 451/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0280 - acc: 0.9908 - val_loss: 2.0102 - val_acc: 0.7615
Epoch 452/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0232 - acc: 0.9929 - val_loss: 2.0867 - val_acc: 0.7661
Epoch 453/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0196 - acc: 0.9926 - val_loss: 2.1525 - val_acc: 0.7706
Epoch 454/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0181 - acc: 0.9932 - val_loss: 2.0777 - val_acc: 0.7706
Epoch 455/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0115 - acc: 0.9942 - val_loss: 2.5077 - val_acc: 0.7523
Epoch 456/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0282 - acc: 0.9900 - val_loss: 2.0919 - val_acc: 0.7752
Epoch 457/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0496 - acc: 0.9826 - val_loss: 1.7761 - val_acc: 0.7936
Epoch 458/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0261 - acc: 0.9906 - val_loss: 2.0221 - val_acc: 0.7569
Epoch 459/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0361 - acc: 0.9884 - val_loss: 2.1662 - val_acc: 0.7431
Epoch 460/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0257 - acc: 0.9897 - val_loss: 2.1039 - val_acc: 0.7615
Epoch 461/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0287 - acc: 0.9894 - val_loss: 2.1599 - val_acc: 0.7661
Epoch 462/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0450 - acc: 0.9865 - val_loss: 2.0106 - val_acc: 0.7569
Epoch 463/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0261 - acc: 0.9902 - val_loss: 1.7740 - val_acc: 0.7569
Epoch 464/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0276 - acc: 0.9902 - val_loss: 1.6865 - val_acc: 0.7936
Epoch 465/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0288 - acc: 0.9909 - val_loss: 2.0317 - val_acc: 0.7615
Epoch 466/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0233 - acc: 0.9936 - val_loss: 2.0751 - val_acc: 0.7615
Epoch 467/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0216 - acc: 0.9930 - val_loss: 2.3861 - val_acc: 0.7431
Epoch 468/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0295 - acc: 0.9902 - val_loss: 2.3187 - val_acc: 0.7477
Epoch 469/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0205 - acc: 0.9933 - val_loss: 2.2062 - val_acc: 0.7569
Epoch 470/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0184 - acc: 0.9933 - val_loss: 1.9985 - val_acc: 0.7798
Epoch 471/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0389 - acc: 0.9885 - val_loss: 2.1428 - val_acc: 0.7615
Epoch 472/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0318 - acc: 0.9886 - val_loss: 2.1559 - val_acc: 0.7615
Epoch 473/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0237 - acc: 0.9927 - val_loss: 2.1303 - val_acc: 0.7798
Epoch 474/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0262 - acc: 0.9900 - val_loss: 2.0129 - val_acc: 0.7706
Epoch 475/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0252 - acc: 0.9906 - val_loss: 1.9951 - val_acc: 0.7706
Epoch 476/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0256 - acc: 0.9929 - val_loss: 1.8908 - val_acc: 0.8028
Epoch 477/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0304 - acc: 0.9908 - val_loss: 2.1360 - val_acc: 0.7752
Epoch 478/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0360 - acc: 0.9906 - val_loss: 2.0295 - val_acc: 0.7339
Epoch 479/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0218 - acc: 0.9920 - val_loss: 2.0374 - val_acc: 0.7569
Epoch 480/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0323 - acc: 0.9885 - val_loss: 1.9252 - val_acc: 0.7661
Epoch 481/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0260 - acc: 0.9914 - val_loss: 2.1972 - val_acc: 0.7385
Epoch 482/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0290 - acc: 0.9908 - val_loss: 2.1449 - val_acc: 0.7615
Epoch 483/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0204 - acc: 0.9926 - val_loss: 2.1922 - val_acc: 0.7661
Epoch 484/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0281 - acc: 0.9911 - val_loss: 2.0221 - val_acc: 0.7706
Epoch 485/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0242 - acc: 0.9911 - val_loss: 2.1672 - val_acc: 0.7339
Epoch 486/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0233 - acc: 0.9927 - val_loss: 1.8082 - val_acc: 0.7615
Epoch 487/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0255 - acc: 0.9920 - val_loss: 1.8552 - val_acc: 0.7477
Epoch 488/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0291 - acc: 0.9906 - val_loss: 1.9892 - val_acc: 0.7523
Epoch 489/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0246 - acc: 0.9923 - val_loss: 2.0256 - val_acc: 0.7569
Epoch 490/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0265 - acc: 0.9912 - val_loss: 2.0865 - val_acc: 0.7752
Epoch 491/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0384 - acc: 0.9889 - val_loss: 1.8411 - val_acc: 0.7661
Epoch 492/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0316 - acc: 0.9894 - val_loss: 1.8232 - val_acc: 0.7706
Epoch 493/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0245 - acc: 0.9921 - val_loss: 2.1024 - val_acc: 0.7569
Epoch 494/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0309 - acc: 0.9889 - val_loss: 1.7618 - val_acc: 0.7844
Epoch 495/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0236 - acc: 0.9932 - val_loss: 1.8037 - val_acc: 0.7844
Epoch 496/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0252 - acc: 0.9918 - val_loss: 2.1368 - val_acc: 0.7477
Epoch 497/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0219 - acc: 0.9933 - val_loss: 2.2068 - val_acc: 0.7569
Epoch 498/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0289 - acc: 0.9902 - val_loss: 2.0211 - val_acc: 0.7431
Epoch 499/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0292 - acc: 0.9918 - val_loss: 1.8347 - val_acc: 0.7798
Epoch 500/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0265 - acc: 0.9897 - val_loss: 1.7367 - val_acc: 0.7844
40/40 [==============================] - 29s 731ms/step
Accuracy: 0.7760284408329101. F1: 0.7735467200258921
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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">make_model2D_S</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;2D_small_2&quot;</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 172s 1s/step - loss: 1.3186 - acc: 0.5022 - val_loss: 1.0449 - val_acc: 0.6055
Epoch 2/500
132/132 [==============================] - 28s 215ms/step - loss: 0.9194 - acc: 0.6523 - val_loss: 0.9006 - val_acc: 0.6743
Epoch 3/500
132/132 [==============================] - 29s 216ms/step - loss: 0.8205 - acc: 0.6957 - val_loss: 0.8033 - val_acc: 0.7110
Epoch 4/500
132/132 [==============================] - 28s 208ms/step - loss: 0.7478 - acc: 0.7182 - val_loss: 0.7884 - val_acc: 0.7202
Epoch 5/500
132/132 [==============================] - 28s 209ms/step - loss: 0.6895 - acc: 0.7419 - val_loss: 0.7391 - val_acc: 0.7523
Epoch 6/500
132/132 [==============================] - 27s 206ms/step - loss: 0.6544 - acc: 0.7503 - val_loss: 0.7000 - val_acc: 0.7569
Epoch 7/500
132/132 [==============================] - 28s 213ms/step - loss: 0.6083 - acc: 0.7774 - val_loss: 0.6733 - val_acc: 0.7523
Epoch 8/500
132/132 [==============================] - 28s 213ms/step - loss: 0.5806 - acc: 0.7805 - val_loss: 0.6648 - val_acc: 0.7569
Epoch 9/500
132/132 [==============================] - 28s 208ms/step - loss: 0.5495 - acc: 0.7935 - val_loss: 0.6452 - val_acc: 0.8073
Epoch 10/500
132/132 [==============================] - 28s 214ms/step - loss: 0.5179 - acc: 0.8048 - val_loss: 0.6552 - val_acc: 0.7936
Epoch 11/500
132/132 [==============================] - 27s 208ms/step - loss: 0.4955 - acc: 0.8142 - val_loss: 0.6305 - val_acc: 0.7752
Epoch 12/500
132/132 [==============================] - 27s 204ms/step - loss: 0.4680 - acc: 0.8230 - val_loss: 0.6303 - val_acc: 0.7890
Epoch 13/500
132/132 [==============================] - 27s 203ms/step - loss: 0.4489 - acc: 0.8304 - val_loss: 0.6284 - val_acc: 0.7844
Epoch 14/500
132/132 [==============================] - 27s 203ms/step - loss: 0.4201 - acc: 0.8370 - val_loss: 0.6561 - val_acc: 0.7752
Epoch 15/500
132/132 [==============================] - 27s 204ms/step - loss: 0.3972 - acc: 0.8516 - val_loss: 0.6440 - val_acc: 0.7844
Epoch 16/500
132/132 [==============================] - 27s 205ms/step - loss: 0.3873 - acc: 0.8492 - val_loss: 0.6417 - val_acc: 0.7890
Epoch 17/500
132/132 [==============================] - 27s 204ms/step - loss: 0.3590 - acc: 0.8670 - val_loss: 0.6803 - val_acc: 0.7798
Epoch 18/500
132/132 [==============================] - 27s 205ms/step - loss: 0.3507 - acc: 0.8637 - val_loss: 0.6504 - val_acc: 0.7752
Epoch 19/500
132/132 [==============================] - 27s 206ms/step - loss: 0.3399 - acc: 0.8711 - val_loss: 0.6420 - val_acc: 0.7982
Epoch 20/500
132/132 [==============================] - 27s 205ms/step - loss: 0.3256 - acc: 0.8767 - val_loss: 0.6650 - val_acc: 0.7798
Epoch 21/500
132/132 [==============================] - 27s 204ms/step - loss: 0.3084 - acc: 0.8793 - val_loss: 0.6685 - val_acc: 0.7798
Epoch 22/500
132/132 [==============================] - 27s 204ms/step - loss: 0.2923 - acc: 0.8867 - val_loss: 0.6734 - val_acc: 0.7982
Epoch 23/500
132/132 [==============================] - 27s 204ms/step - loss: 0.2810 - acc: 0.8906 - val_loss: 0.7088 - val_acc: 0.7752
Epoch 24/500
132/132 [==============================] - 27s 205ms/step - loss: 0.2684 - acc: 0.8958 - val_loss: 0.7319 - val_acc: 0.7798
Epoch 25/500
132/132 [==============================] - 27s 207ms/step - loss: 0.2761 - acc: 0.8942 - val_loss: 0.6921 - val_acc: 0.8028
Epoch 26/500
132/132 [==============================] - 28s 210ms/step - loss: 0.2630 - acc: 0.9007 - val_loss: 0.7013 - val_acc: 0.8211s - loss: 0.266
Epoch 27/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2458 - acc: 0.9044 - val_loss: 0.6657 - val_acc: 0.8165
Epoch 28/500
132/132 [==============================] - 33s 251ms/step - loss: 0.2284 - acc: 0.9162 - val_loss: 0.6593 - val_acc: 0.8257
Epoch 29/500
132/132 [==============================] - 32s 245ms/step - loss: 0.2291 - acc: 0.9133 - val_loss: 0.7523 - val_acc: 0.8165
Epoch 30/500
132/132 [==============================] - 32s 246ms/step - loss: 0.2144 - acc: 0.9156 - val_loss: 0.7214 - val_acc: 0.8165
Epoch 31/500
132/132 [==============================] - 32s 245ms/step - loss: 0.2100 - acc: 0.9178 - val_loss: 0.7312 - val_acc: 0.8303
Epoch 32/500
132/132 [==============================] - 32s 244ms/step - loss: 0.2089 - acc: 0.9171 - val_loss: 0.7225 - val_acc: 0.7982
Epoch 33/500
132/132 [==============================] - 32s 246ms/step - loss: 0.2020 - acc: 0.9234 - val_loss: 0.7840 - val_acc: 0.7936
Epoch 34/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1889 - acc: 0.9297 - val_loss: 0.8093 - val_acc: 0.8028
Epoch 35/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1902 - acc: 0.9279 - val_loss: 0.8067 - val_acc: 0.8028
Epoch 36/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1877 - acc: 0.9295 - val_loss: 0.7947 - val_acc: 0.8211
Epoch 37/500
132/132 [==============================] - 33s 249ms/step - loss: 0.1749 - acc: 0.9317 - val_loss: 0.8526 - val_acc: 0.8119
Epoch 38/500
132/132 [==============================] - 33s 251ms/step - loss: 0.1732 - acc: 0.9359 - val_loss: 0.8144 - val_acc: 0.8028
Epoch 39/500
132/132 [==============================] - 33s 248ms/step - loss: 0.1679 - acc: 0.9373 - val_loss: 0.8546 - val_acc: 0.7982
Epoch 40/500
132/132 [==============================] - 34s 254ms/step - loss: 0.1621 - acc: 0.9423 - val_loss: 0.8447 - val_acc: 0.7798
Epoch 41/500
132/132 [==============================] - 39s 297ms/step - loss: 0.1608 - acc: 0.9385 - val_loss: 0.8267 - val_acc: 0.8028
Epoch 42/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1430 - acc: 0.9451 - val_loss: 0.8215 - val_acc: 0.8028
Epoch 43/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1530 - acc: 0.9397 - val_loss: 0.8465 - val_acc: 0.8028
Epoch 44/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1362 - acc: 0.9512 - val_loss: 0.9194 - val_acc: 0.8119
Epoch 45/500
132/132 [==============================] - 32s 241ms/step - loss: 0.1433 - acc: 0.9476 - val_loss: 0.9556 - val_acc: 0.8073
Epoch 46/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1423 - acc: 0.9451 - val_loss: 0.9159 - val_acc: 0.7798
Epoch 47/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1266 - acc: 0.9520 - val_loss: 0.9697 - val_acc: 0.8119
Epoch 48/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1393 - acc: 0.9465 - val_loss: 0.8269 - val_acc: 0.8349
Epoch 49/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1259 - acc: 0.9536 - val_loss: 0.8866 - val_acc: 0.8257
Epoch 50/500
132/132 [==============================] - 32s 240ms/step - loss: 0.1291 - acc: 0.9526 - val_loss: 0.8923 - val_acc: 0.7844
Epoch 51/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1129 - acc: 0.9574 - val_loss: 0.9911 - val_acc: 0.8119
Epoch 52/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1178 - acc: 0.9588 - val_loss: 0.9778 - val_acc: 0.8073
Epoch 53/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1098 - acc: 0.9595 - val_loss: 1.0788 - val_acc: 0.7982
Epoch 54/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1101 - acc: 0.9586 - val_loss: 0.9609 - val_acc: 0.7982
Epoch 55/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1076 - acc: 0.9589 - val_loss: 1.0069 - val_acc: 0.7936
Epoch 56/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1338 - acc: 0.9515 - val_loss: 0.9623 - val_acc: 0.8073
Epoch 57/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1093 - acc: 0.9603 - val_loss: 0.9877 - val_acc: 0.7936
Epoch 58/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1032 - acc: 0.9614 - val_loss: 1.0643 - val_acc: 0.7844
Epoch 59/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1024 - acc: 0.9612 - val_loss: 1.0508 - val_acc: 0.7936
Epoch 60/500
132/132 [==============================] - 31s 239ms/step - loss: 0.1087 - acc: 0.9598 - val_loss: 1.1217 - val_acc: 0.7936
Epoch 61/500
132/132 [==============================] - 31s 238ms/step - loss: 0.1061 - acc: 0.9615 - val_loss: 1.0096 - val_acc: 0.7982
Epoch 62/500
132/132 [==============================] - 32s 239ms/step - loss: 0.1043 - acc: 0.9620 - val_loss: 1.0908 - val_acc: 0.7844
Epoch 63/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0969 - acc: 0.9639 - val_loss: 1.0079 - val_acc: 0.8211
Epoch 64/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1095 - acc: 0.9620 - val_loss: 0.9788 - val_acc: 0.8165
Epoch 65/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0935 - acc: 0.9670 - val_loss: 0.9999 - val_acc: 0.8165
Epoch 66/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0901 - acc: 0.9662 - val_loss: 1.1859 - val_acc: 0.8073
Epoch 67/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0876 - acc: 0.9688 - val_loss: 1.0028 - val_acc: 0.8257
Epoch 68/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0791 - acc: 0.9718 - val_loss: 1.1370 - val_acc: 0.7890
Epoch 69/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0865 - acc: 0.9694 - val_loss: 1.1212 - val_acc: 0.8119
Epoch 70/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0828 - acc: 0.9703 - val_loss: 1.1576 - val_acc: 0.8119
Epoch 71/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0771 - acc: 0.9724 - val_loss: 1.1612 - val_acc: 0.7844
Epoch 72/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0876 - acc: 0.9689 - val_loss: 1.1607 - val_acc: 0.8028
Epoch 73/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0912 - acc: 0.9671 - val_loss: 1.1174 - val_acc: 0.7844
Epoch 74/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0821 - acc: 0.9698 - val_loss: 1.1121 - val_acc: 0.7936
Epoch 75/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0810 - acc: 0.9702 - val_loss: 0.9841 - val_acc: 0.8073
Epoch 76/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0887 - acc: 0.9692 - val_loss: 1.0571 - val_acc: 0.7982
Epoch 77/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0772 - acc: 0.9714 - val_loss: 1.0506 - val_acc: 0.8073
Epoch 78/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0819 - acc: 0.9705 - val_loss: 1.1036 - val_acc: 0.7706
Epoch 79/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0729 - acc: 0.9726 - val_loss: 1.1196 - val_acc: 0.7706
Epoch 80/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0844 - acc: 0.9692 - val_loss: 1.1677 - val_acc: 0.8073
Epoch 81/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0754 - acc: 0.9738 - val_loss: 1.2385 - val_acc: 0.7936
Epoch 82/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0671 - acc: 0.9765 - val_loss: 1.1887 - val_acc: 0.8028
Epoch 83/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0667 - acc: 0.9765 - val_loss: 1.2695 - val_acc: 0.8028
Epoch 84/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0648 - acc: 0.9759 - val_loss: 1.2781 - val_acc: 0.7982
Epoch 85/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0819 - acc: 0.9694 - val_loss: 1.4506 - val_acc: 0.7569
Epoch 86/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0754 - acc: 0.9732 - val_loss: 1.2400 - val_acc: 0.7798
Epoch 87/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0734 - acc: 0.9773 - val_loss: 1.2795 - val_acc: 0.8028
Epoch 88/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0740 - acc: 0.9730 - val_loss: 1.2163 - val_acc: 0.7844
Epoch 89/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0743 - acc: 0.9738 - val_loss: 1.2421 - val_acc: 0.7982
Epoch 90/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0740 - acc: 0.9764 - val_loss: 1.1901 - val_acc: 0.7936
Epoch 91/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0713 - acc: 0.9752 - val_loss: 1.2478 - val_acc: 0.7936
Epoch 92/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0850 - acc: 0.9720 - val_loss: 1.1356 - val_acc: 0.8028
Epoch 93/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0649 - acc: 0.9783 - val_loss: 1.2921 - val_acc: 0.7844
Epoch 94/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0682 - acc: 0.9739 - val_loss: 1.2941 - val_acc: 0.7982
Epoch 95/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0651 - acc: 0.9779 - val_loss: 1.2635 - val_acc: 0.7798
Epoch 96/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0735 - acc: 0.9757 - val_loss: 1.3484 - val_acc: 0.7982
Epoch 97/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0672 - acc: 0.9759 - val_loss: 1.3266 - val_acc: 0.7844
Epoch 98/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0719 - acc: 0.9742 - val_loss: 1.4227 - val_acc: 0.7890
Epoch 99/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0742 - acc: 0.9765 - val_loss: 1.2257 - val_acc: 0.7890
Epoch 100/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0640 - acc: 0.9777 - val_loss: 1.3340 - val_acc: 0.8073
Epoch 101/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0680 - acc: 0.9761 - val_loss: 1.2415 - val_acc: 0.8073
Epoch 102/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0651 - acc: 0.9777 - val_loss: 1.2869 - val_acc: 0.8119
Epoch 103/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0602 - acc: 0.9792 - val_loss: 1.4259 - val_acc: 0.8165
Epoch 104/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0616 - acc: 0.9780 - val_loss: 1.4116 - val_acc: 0.8028
Epoch 105/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0522 - acc: 0.9806 - val_loss: 1.3311 - val_acc: 0.8028
Epoch 106/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0619 - acc: 0.9792 - val_loss: 1.2815 - val_acc: 0.8073
Epoch 107/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0585 - acc: 0.9805 - val_loss: 1.2517 - val_acc: 0.8211
Epoch 108/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0537 - acc: 0.9808 - val_loss: 1.1360 - val_acc: 0.8028
Epoch 109/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0662 - acc: 0.9797 - val_loss: 1.2122 - val_acc: 0.7890
Epoch 110/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0582 - acc: 0.9815 - val_loss: 1.3164 - val_acc: 0.8073
Epoch 111/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0603 - acc: 0.9806 - val_loss: 1.3706 - val_acc: 0.8073
Epoch 112/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0508 - acc: 0.9814 - val_loss: 1.3210 - val_acc: 0.8165
Epoch 113/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0606 - acc: 0.9811 - val_loss: 1.3982 - val_acc: 0.8119
Epoch 114/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0565 - acc: 0.9805 - val_loss: 1.3457 - val_acc: 0.7752
Epoch 115/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0556 - acc: 0.9811 - val_loss: 1.4086 - val_acc: 0.8028
Epoch 116/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0623 - acc: 0.9795 - val_loss: 1.2906 - val_acc: 0.7936
Epoch 117/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0649 - acc: 0.9786 - val_loss: 1.2083 - val_acc: 0.7844
Epoch 118/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0537 - acc: 0.9815 - val_loss: 1.2394 - val_acc: 0.8165
Epoch 119/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0522 - acc: 0.9812 - val_loss: 1.3457 - val_acc: 0.8028
Epoch 120/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0552 - acc: 0.9826 - val_loss: 1.3559 - val_acc: 0.8028
Epoch 121/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0448 - acc: 0.9827 - val_loss: 1.3862 - val_acc: 0.8073
Epoch 122/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0485 - acc: 0.9821 - val_loss: 1.3778 - val_acc: 0.8073
Epoch 123/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0453 - acc: 0.9848 - val_loss: 1.3042 - val_acc: 0.7982
Epoch 124/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0447 - acc: 0.9847 - val_loss: 1.4090 - val_acc: 0.7844
Epoch 125/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0552 - acc: 0.9815 - val_loss: 1.4002 - val_acc: 0.8165
Epoch 126/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0439 - acc: 0.9861 - val_loss: 1.2765 - val_acc: 0.8073
Epoch 127/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0699 - acc: 0.9770 - val_loss: 1.2805 - val_acc: 0.8073
Epoch 128/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0567 - acc: 0.9798 - val_loss: 1.5111 - val_acc: 0.7798
Epoch 129/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0552 - acc: 0.9820 - val_loss: 1.4113 - val_acc: 0.7936
Epoch 130/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0718 - acc: 0.9786 - val_loss: 1.4615 - val_acc: 0.8028
Epoch 131/500
132/132 [==============================] - 33s 253ms/step - loss: 0.0579 - acc: 0.9817 - val_loss: 1.4497 - val_acc: 0.7798
Epoch 132/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0492 - acc: 0.9823 - val_loss: 1.3487 - val_acc: 0.7890
Epoch 133/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0623 - acc: 0.9780 - val_loss: 1.4411 - val_acc: 0.7752
Epoch 134/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0538 - acc: 0.9824 - val_loss: 1.3624 - val_acc: 0.7706
Epoch 135/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0557 - acc: 0.9820 - val_loss: 1.5443 - val_acc: 0.7890
Epoch 136/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0576 - acc: 0.9792 - val_loss: 1.3953 - val_acc: 0.8028
Epoch 137/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0475 - acc: 0.9815 - val_loss: 1.3243 - val_acc: 0.7982
Epoch 138/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0527 - acc: 0.9824 - val_loss: 1.6219 - val_acc: 0.8028
Epoch 139/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0382 - acc: 0.9877 - val_loss: 1.4234 - val_acc: 0.8073
Epoch 140/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0493 - acc: 0.9838 - val_loss: 1.3075 - val_acc: 0.7890
Epoch 141/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0454 - acc: 0.9855 - val_loss: 1.4268 - val_acc: 0.7982
Epoch 142/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0386 - acc: 0.9882 - val_loss: 1.4722 - val_acc: 0.7890
Epoch 143/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0442 - acc: 0.9858 - val_loss: 1.6564 - val_acc: 0.7706
Epoch 144/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0429 - acc: 0.9864 - val_loss: 1.5260 - val_acc: 0.7982
Epoch 145/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0419 - acc: 0.9867 - val_loss: 1.3362 - val_acc: 0.7982
Epoch 146/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0526 - acc: 0.9836 - val_loss: 1.6791 - val_acc: 0.7890
Epoch 147/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0585 - acc: 0.9809 - val_loss: 1.5687 - val_acc: 0.7890
Epoch 148/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0499 - acc: 0.9830 - val_loss: 1.5362 - val_acc: 0.7890
Epoch 149/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0339 - acc: 0.9895 - val_loss: 1.7160 - val_acc: 0.7661
Epoch 150/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0388 - acc: 0.9873 - val_loss: 1.6333 - val_acc: 0.7936
Epoch 151/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0440 - acc: 0.9845 - val_loss: 1.6072 - val_acc: 0.7752
Epoch 152/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0410 - acc: 0.9848 - val_loss: 1.6823 - val_acc: 0.7706
Epoch 153/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0608 - acc: 0.9823 - val_loss: 1.4620 - val_acc: 0.7890
Epoch 154/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0524 - acc: 0.9808 - val_loss: 1.4652 - val_acc: 0.8073
Epoch 155/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0609 - acc: 0.9809 - val_loss: 1.4128 - val_acc: 0.8028
Epoch 156/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0432 - acc: 0.9853 - val_loss: 1.5986 - val_acc: 0.8028
Epoch 157/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0477 - acc: 0.9848 - val_loss: 1.3354 - val_acc: 0.8257
Epoch 158/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0442 - acc: 0.9868 - val_loss: 1.6052 - val_acc: 0.7798
Epoch 159/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0522 - acc: 0.9829 - val_loss: 1.5553 - val_acc: 0.7706
Epoch 160/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0481 - acc: 0.9852 - val_loss: 1.5054 - val_acc: 0.7982
Epoch 161/500
132/132 [==============================] - 34s 255ms/step - loss: 0.0434 - acc: 0.9855 - val_loss: 1.6916 - val_acc: 0.8028
Epoch 162/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0404 - acc: 0.9859 - val_loss: 1.6940 - val_acc: 0.7936
Epoch 163/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0342 - acc: 0.9880 - val_loss: 1.7285 - val_acc: 0.7936
Epoch 164/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0415 - acc: 0.9867 - val_loss: 1.6560 - val_acc: 0.7936
Epoch 165/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0466 - acc: 0.9853 - val_loss: 1.5222 - val_acc: 0.8028
Epoch 166/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0567 - acc: 0.9820 - val_loss: 1.5955 - val_acc: 0.8119
Epoch 167/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0504 - acc: 0.9833 - val_loss: 1.5699 - val_acc: 0.8073
Epoch 168/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0440 - acc: 0.9848 - val_loss: 1.5437 - val_acc: 0.8119
Epoch 169/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0342 - acc: 0.9888 - val_loss: 1.4411 - val_acc: 0.8211
Epoch 170/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0451 - acc: 0.9848 - val_loss: 1.4648 - val_acc: 0.8211
Epoch 171/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0348 - acc: 0.9885 - val_loss: 1.4559 - val_acc: 0.8028
Epoch 172/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0447 - acc: 0.9836 - val_loss: 1.5519 - val_acc: 0.7936
Epoch 173/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0498 - acc: 0.9856 - val_loss: 1.5204 - val_acc: 0.7936
Epoch 174/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0405 - acc: 0.9870 - val_loss: 1.6147 - val_acc: 0.7936
Epoch 175/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0460 - acc: 0.9861 - val_loss: 1.5001 - val_acc: 0.7936
Epoch 176/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0423 - acc: 0.9873 - val_loss: 1.5354 - val_acc: 0.7936
Epoch 177/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0409 - acc: 0.9874 - val_loss: 1.4332 - val_acc: 0.8028
Epoch 178/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0391 - acc: 0.9853 - val_loss: 1.5583 - val_acc: 0.8028
Epoch 179/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0579 - acc: 0.9824 - val_loss: 1.4679 - val_acc: 0.7890
Epoch 180/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0592 - acc: 0.9820 - val_loss: 1.5092 - val_acc: 0.7982
Epoch 181/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0468 - acc: 0.9864 - val_loss: 1.7540 - val_acc: 0.7798
Epoch 182/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0355 - acc: 0.9886 - val_loss: 1.5746 - val_acc: 0.7936
Epoch 183/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0362 - acc: 0.9874 - val_loss: 1.6865 - val_acc: 0.8028
Epoch 184/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0311 - acc: 0.9885 - val_loss: 1.5245 - val_acc: 0.8073
Epoch 185/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0399 - acc: 0.9879 - val_loss: 1.4458 - val_acc: 0.8303
Epoch 186/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0386 - acc: 0.9874 - val_loss: 1.4980 - val_acc: 0.8028
Epoch 187/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0316 - acc: 0.9894 - val_loss: 1.6139 - val_acc: 0.8073
Epoch 188/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0389 - acc: 0.9864 - val_loss: 1.6284 - val_acc: 0.8028
Epoch 189/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0389 - acc: 0.9867 - val_loss: 1.6775 - val_acc: 0.7936
Epoch 190/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0493 - acc: 0.9841 - val_loss: 1.8570 - val_acc: 0.7844
Epoch 191/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0416 - acc: 0.9880 - val_loss: 1.7756 - val_acc: 0.7936
Epoch 192/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0396 - acc: 0.9867 - val_loss: 1.5501 - val_acc: 0.8073
Epoch 193/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0532 - acc: 0.9832 - val_loss: 1.7151 - val_acc: 0.7798
Epoch 194/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0503 - acc: 0.9835 - val_loss: 1.5137 - val_acc: 0.7890
Epoch 195/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0467 - acc: 0.9855 - val_loss: 1.5742 - val_acc: 0.8028
Epoch 196/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0384 - acc: 0.9888 - val_loss: 1.8047 - val_acc: 0.7798
Epoch 197/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0361 - acc: 0.9877 - val_loss: 1.7210 - val_acc: 0.8073
Epoch 198/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0357 - acc: 0.9876 - val_loss: 1.6473 - val_acc: 0.7982
Epoch 199/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0454 - acc: 0.9845 - val_loss: 1.6676 - val_acc: 0.7982
Epoch 200/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0277 - acc: 0.9912 - val_loss: 1.7725 - val_acc: 0.7615
Epoch 201/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0422 - acc: 0.9871 - val_loss: 1.9924 - val_acc: 0.7706
Epoch 202/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0478 - acc: 0.9852 - val_loss: 1.6072 - val_acc: 0.7752
Epoch 203/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0318 - acc: 0.9894 - val_loss: 1.7226 - val_acc: 0.7798
Epoch 204/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0458 - acc: 0.9868 - val_loss: 1.7643 - val_acc: 0.7844
Epoch 205/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0340 - acc: 0.9879 - val_loss: 1.6992 - val_acc: 0.7844
Epoch 206/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0641 - acc: 0.9822 - val_loss: 1.7971 - val_acc: 0.7890
Epoch 207/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0398 - acc: 0.9873 - val_loss: 1.6387 - val_acc: 0.7982
Epoch 208/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0393 - acc: 0.9885 - val_loss: 1.7152 - val_acc: 0.7890
Epoch 209/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0318 - acc: 0.9911 - val_loss: 1.8103 - val_acc: 0.7706
Epoch 210/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0371 - acc: 0.9876 - val_loss: 1.6392 - val_acc: 0.8119
Epoch 211/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0330 - acc: 0.9895 - val_loss: 1.5403 - val_acc: 0.8119
Epoch 212/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0386 - acc: 0.9874 - val_loss: 1.6456 - val_acc: 0.7982
Epoch 213/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0418 - acc: 0.9862 - val_loss: 1.4942 - val_acc: 0.8119
Epoch 214/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0463 - acc: 0.9871 - val_loss: 1.6249 - val_acc: 0.8165
Epoch 215/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0303 - acc: 0.9905 - val_loss: 1.6232 - val_acc: 0.8119
Epoch 216/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0258 - acc: 0.9918 - val_loss: 1.7571 - val_acc: 0.7982
Epoch 217/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0281 - acc: 0.9905 - val_loss: 1.7408 - val_acc: 0.8119
Epoch 218/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0318 - acc: 0.9905 - val_loss: 1.8587 - val_acc: 0.7569
Epoch 219/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0472 - acc: 0.9864 - val_loss: 1.7446 - val_acc: 0.7890
Epoch 220/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0458 - acc: 0.9868 - val_loss: 1.6022 - val_acc: 0.8028
Epoch 221/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0343 - acc: 0.9877 - val_loss: 1.6659 - val_acc: 0.8165
Epoch 222/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0376 - acc: 0.9877 - val_loss: 1.5231 - val_acc: 0.7982
Epoch 223/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0314 - acc: 0.9903 - val_loss: 1.6329 - val_acc: 0.8165
Epoch 224/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0334 - acc: 0.9892 - val_loss: 1.7170 - val_acc: 0.8073
Epoch 225/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0318 - acc: 0.9902 - val_loss: 1.6855 - val_acc: 0.8211
Epoch 226/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0416 - acc: 0.9871 - val_loss: 1.6292 - val_acc: 0.7982
Epoch 227/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0360 - acc: 0.9897 - val_loss: 1.5841 - val_acc: 0.8257
Epoch 228/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0295 - acc: 0.9903 - val_loss: 1.6791 - val_acc: 0.8211
Epoch 229/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0314 - acc: 0.9894 - val_loss: 1.9665 - val_acc: 0.7844
Epoch 230/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0411 - acc: 0.9891 - val_loss: 1.7017 - val_acc: 0.8073
Epoch 231/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0363 - acc: 0.9898 - val_loss: 1.5966 - val_acc: 0.8028
Epoch 232/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0346 - acc: 0.9883 - val_loss: 1.8771 - val_acc: 0.8028
Epoch 233/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0395 - acc: 0.9858 - val_loss: 1.7296 - val_acc: 0.8028
Epoch 234/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0331 - acc: 0.9888 - val_loss: 1.7675 - val_acc: 0.7844
Epoch 235/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0382 - acc: 0.9879 - val_loss: 1.5272 - val_acc: 0.8119
Epoch 236/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0298 - acc: 0.9891 - val_loss: 1.6572 - val_acc: 0.7890
Epoch 237/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0320 - acc: 0.9900 - val_loss: 1.7096 - val_acc: 0.8028
Epoch 238/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0411 - acc: 0.9874 - val_loss: 1.7795 - val_acc: 0.8028
Epoch 239/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0291 - acc: 0.9895 - val_loss: 1.5970 - val_acc: 0.8211
Epoch 240/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0369 - acc: 0.9889 - val_loss: 1.5164 - val_acc: 0.8119
Epoch 241/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0368 - acc: 0.9880 - val_loss: 1.7539 - val_acc: 0.8119
Epoch 242/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0352 - acc: 0.9894 - val_loss: 1.7114 - val_acc: 0.7982
Epoch 243/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9895 - val_loss: 1.7639 - val_acc: 0.7798
Epoch 244/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0369 - acc: 0.9886 - val_loss: 1.8526 - val_acc: 0.8028
Epoch 245/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0294 - acc: 0.9902 - val_loss: 1.7689 - val_acc: 0.7982
Epoch 246/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0350 - acc: 0.9873 - val_loss: 1.7820 - val_acc: 0.7890
Epoch 247/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0354 - acc: 0.9876 - val_loss: 1.7436 - val_acc: 0.7936
Epoch 248/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0343 - acc: 0.9902 - val_loss: 1.6818 - val_acc: 0.8028
Epoch 249/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0463 - acc: 0.9871 - val_loss: 1.6924 - val_acc: 0.8073
Epoch 250/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0268 - acc: 0.9905 - val_loss: 1.8002 - val_acc: 0.7982
Epoch 251/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0283 - acc: 0.9906 - val_loss: 1.7981 - val_acc: 0.7844
Epoch 252/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0235 - acc: 0.9921 - val_loss: 1.9219 - val_acc: 0.7936
Epoch 253/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0365 - acc: 0.9882 - val_loss: 1.9385 - val_acc: 0.7752
Epoch 254/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0366 - acc: 0.9877 - val_loss: 1.6838 - val_acc: 0.7890
Epoch 255/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0414 - acc: 0.9877 - val_loss: 1.7102 - val_acc: 0.8073
Epoch 256/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0309 - acc: 0.9889 - val_loss: 1.8919 - val_acc: 0.7844
Epoch 257/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0444 - acc: 0.9873 - val_loss: 1.8256 - val_acc: 0.7661
Epoch 258/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0354 - acc: 0.9897 - val_loss: 1.7905 - val_acc: 0.7982
Epoch 259/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0278 - acc: 0.9914 - val_loss: 1.8904 - val_acc: 0.7982
Epoch 260/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0373 - acc: 0.9885 - val_loss: 1.9200 - val_acc: 0.8028
Epoch 261/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0290 - acc: 0.9903 - val_loss: 1.8180 - val_acc: 0.7844
Epoch 262/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0289 - acc: 0.9906 - val_loss: 1.8682 - val_acc: 0.8028
Epoch 263/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0383 - acc: 0.9882 - val_loss: 1.6643 - val_acc: 0.7982
Epoch 264/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0385 - acc: 0.9868 - val_loss: 1.8706 - val_acc: 0.7982
Epoch 265/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0222 - acc: 0.9923 - val_loss: 1.9107 - val_acc: 0.8165
Epoch 266/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0293 - acc: 0.9908 - val_loss: 1.9943 - val_acc: 0.7752
Epoch 267/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0397 - acc: 0.9868 - val_loss: 2.0074 - val_acc: 0.7890
Epoch 268/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0389 - acc: 0.9870 - val_loss: 1.9736 - val_acc: 0.7661
Epoch 269/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0457 - acc: 0.9862 - val_loss: 1.6957 - val_acc: 0.7890
Epoch 270/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0274 - acc: 0.9902 - val_loss: 1.7589 - val_acc: 0.7890
Epoch 271/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0261 - acc: 0.9908 - val_loss: 1.7884 - val_acc: 0.8028
Epoch 272/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0411 - acc: 0.9880 - val_loss: 1.9622 - val_acc: 0.7523
Epoch 273/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0387 - acc: 0.9892 - val_loss: 2.0317 - val_acc: 0.7798
Epoch 274/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0385 - acc: 0.9891 - val_loss: 1.8732 - val_acc: 0.7890
Epoch 275/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9897 - val_loss: 1.9898 - val_acc: 0.7936
Epoch 276/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0281 - acc: 0.9909 - val_loss: 1.9048 - val_acc: 0.7982
Epoch 277/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0316 - acc: 0.9891 - val_loss: 1.9482 - val_acc: 0.7982
Epoch 278/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0317 - acc: 0.9895 - val_loss: 2.0043 - val_acc: 0.7890
Epoch 279/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0215 - acc: 0.9927 - val_loss: 2.1780 - val_acc: 0.7798
Epoch 280/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0336 - acc: 0.9903 - val_loss: 1.9176 - val_acc: 0.7890
Epoch 281/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0341 - acc: 0.9906 - val_loss: 1.9799 - val_acc: 0.7844
Epoch 282/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0231 - acc: 0.9921 - val_loss: 2.0648 - val_acc: 0.7752
Epoch 283/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0286 - acc: 0.9914 - val_loss: 1.8402 - val_acc: 0.8211
Epoch 284/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0300 - acc: 0.9918 - val_loss: 1.8570 - val_acc: 0.7798
Epoch 285/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0299 - acc: 0.9903 - val_loss: 1.8746 - val_acc: 0.7890
Epoch 286/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0308 - acc: 0.9908 - val_loss: 2.0104 - val_acc: 0.7615
Epoch 287/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0367 - acc: 0.9888 - val_loss: 1.9848 - val_acc: 0.7890
Epoch 288/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0320 - acc: 0.9894 - val_loss: 2.0013 - val_acc: 0.7752
Epoch 289/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0305 - acc: 0.9911 - val_loss: 2.0480 - val_acc: 0.7844
Epoch 290/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0450 - acc: 0.9859 - val_loss: 2.0200 - val_acc: 0.7890
Epoch 291/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0370 - acc: 0.9882 - val_loss: 1.8775 - val_acc: 0.7982
Epoch 292/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0339 - acc: 0.9900 - val_loss: 1.9009 - val_acc: 0.8028
Epoch 293/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0317 - acc: 0.9900 - val_loss: 1.9676 - val_acc: 0.7982
Epoch 294/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0270 - acc: 0.9918 - val_loss: 1.7762 - val_acc: 0.8073
Epoch 295/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0344 - acc: 0.9891 - val_loss: 1.7934 - val_acc: 0.7982
Epoch 296/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0316 - acc: 0.9894 - val_loss: 1.7605 - val_acc: 0.8119
Epoch 297/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0311 - acc: 0.9898 - val_loss: 1.7152 - val_acc: 0.8119
Epoch 298/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0297 - acc: 0.9905 - val_loss: 1.6479 - val_acc: 0.8073
Epoch 299/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0353 - acc: 0.9903 - val_loss: 1.5942 - val_acc: 0.8303
Epoch 300/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0289 - acc: 0.9908 - val_loss: 1.9137 - val_acc: 0.7936
Epoch 301/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0238 - acc: 0.9918 - val_loss: 1.9934 - val_acc: 0.7936
Epoch 302/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0275 - acc: 0.9900 - val_loss: 1.9117 - val_acc: 0.7936
Epoch 303/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0248 - acc: 0.9920 - val_loss: 1.8414 - val_acc: 0.7936
Epoch 304/500
132/132 [==============================] - 33s 251ms/step - loss: 0.0307 - acc: 0.9905 - val_loss: 2.0834 - val_acc: 0.7706
Epoch 305/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0273 - acc: 0.9917 - val_loss: 1.7901 - val_acc: 0.7569
Epoch 306/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0260 - acc: 0.9906 - val_loss: 1.7949 - val_acc: 0.7798
Epoch 307/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0301 - acc: 0.9892 - val_loss: 1.8882 - val_acc: 0.7982
Epoch 308/500
132/132 [==============================] - 33s 251ms/step - loss: 0.0303 - acc: 0.9900 - val_loss: 1.7591 - val_acc: 0.7982
Epoch 309/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0370 - acc: 0.9892 - val_loss: 1.9461 - val_acc: 0.7890
Epoch 310/500
132/132 [==============================] - 33s 252ms/step - loss: 0.0336 - acc: 0.9888 - val_loss: 1.9002 - val_acc: 0.7661
Epoch 311/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0231 - acc: 0.9914 - val_loss: 1.8744 - val_acc: 0.7936
Epoch 312/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0259 - acc: 0.9911 - val_loss: 1.7545 - val_acc: 0.7844
Epoch 313/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0351 - acc: 0.9891 - val_loss: 1.8069 - val_acc: 0.7890
Epoch 314/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0259 - acc: 0.9920 - val_loss: 2.0386 - val_acc: 0.7706
Epoch 315/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0327 - acc: 0.9891 - val_loss: 1.9773 - val_acc: 0.7706
Epoch 316/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0328 - acc: 0.9883 - val_loss: 1.5566 - val_acc: 0.8073
Epoch 317/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0386 - acc: 0.9882 - val_loss: 1.8476 - val_acc: 0.7844
Epoch 318/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0324 - acc: 0.9915 - val_loss: 1.9168 - val_acc: 0.7982
Epoch 319/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0300 - acc: 0.9908 - val_loss: 1.9742 - val_acc: 0.7936
Epoch 320/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0336 - acc: 0.9885 - val_loss: 2.0094 - val_acc: 0.7982
Epoch 321/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0343 - acc: 0.9898 - val_loss: 1.8792 - val_acc: 0.8028
Epoch 322/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0307 - acc: 0.9898 - val_loss: 1.8868 - val_acc: 0.7936
Epoch 323/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0287 - acc: 0.9914 - val_loss: 1.7176 - val_acc: 0.7982
Epoch 324/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0278 - acc: 0.9895 - val_loss: 1.7511 - val_acc: 0.8257
Epoch 325/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0257 - acc: 0.9908 - val_loss: 1.7514 - val_acc: 0.7936
Epoch 326/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0271 - acc: 0.9909 - val_loss: 1.7362 - val_acc: 0.7844
Epoch 327/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0301 - acc: 0.9897 - val_loss: 1.7878 - val_acc: 0.8211
Epoch 328/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0285 - acc: 0.9920 - val_loss: 1.7810 - val_acc: 0.8257
Epoch 329/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0236 - acc: 0.9912 - val_loss: 1.8024 - val_acc: 0.7982
Epoch 330/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0190 - acc: 0.9930 - val_loss: 1.8963 - val_acc: 0.8119
Epoch 331/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9917 - val_loss: 1.9461 - val_acc: 0.7844
Epoch 332/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0294 - acc: 0.9902 - val_loss: 1.8065 - val_acc: 0.8303
Epoch 333/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0307 - acc: 0.9897 - val_loss: 1.8588 - val_acc: 0.8073
Epoch 334/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0270 - acc: 0.9915 - val_loss: 1.8268 - val_acc: 0.8119
Epoch 335/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0257 - acc: 0.9921 - val_loss: 1.9434 - val_acc: 0.7982
Epoch 336/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0354 - acc: 0.9894 - val_loss: 1.8367 - val_acc: 0.8028
Epoch 337/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0306 - acc: 0.9909 - val_loss: 1.8627 - val_acc: 0.7890
Epoch 338/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0292 - acc: 0.9898 - val_loss: 2.0161 - val_acc: 0.7982
Epoch 339/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0236 - acc: 0.9914 - val_loss: 1.9333 - val_acc: 0.7936
Epoch 340/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0286 - acc: 0.9908 - val_loss: 1.8807 - val_acc: 0.7752
Epoch 341/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0294 - acc: 0.9895 - val_loss: 1.7670 - val_acc: 0.7798
Epoch 342/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0249 - acc: 0.9917 - val_loss: 1.9926 - val_acc: 0.7798
Epoch 343/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0252 - acc: 0.9924 - val_loss: 1.8272 - val_acc: 0.7798
Epoch 344/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0319 - acc: 0.9903 - val_loss: 1.8251 - val_acc: 0.7890
Epoch 345/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0277 - acc: 0.9909 - val_loss: 1.8842 - val_acc: 0.8028
Epoch 346/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0257 - acc: 0.9911 - val_loss: 1.8796 - val_acc: 0.7936
Epoch 347/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0258 - acc: 0.9912 - val_loss: 1.9161 - val_acc: 0.7890
Epoch 348/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0266 - acc: 0.9912 - val_loss: 2.0860 - val_acc: 0.7844
Epoch 349/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0319 - acc: 0.9912 - val_loss: 1.7025 - val_acc: 0.7936
Epoch 350/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0235 - acc: 0.9923 - val_loss: 1.8969 - val_acc: 0.7844
Epoch 351/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0260 - acc: 0.9914 - val_loss: 1.9228 - val_acc: 0.7982
Epoch 352/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0224 - acc: 0.9923 - val_loss: 1.9123 - val_acc: 0.7982
Epoch 353/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0188 - acc: 0.9950 - val_loss: 1.9920 - val_acc: 0.8165
Epoch 354/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0235 - acc: 0.9930 - val_loss: 1.9438 - val_acc: 0.7936
Epoch 355/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0206 - acc: 0.9935 - val_loss: 1.9313 - val_acc: 0.7752
Epoch 356/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0344 - acc: 0.9900 - val_loss: 2.0636 - val_acc: 0.7936
Epoch 357/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0415 - acc: 0.9883 - val_loss: 2.0207 - val_acc: 0.7752
Epoch 358/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0301 - acc: 0.9900 - val_loss: 1.9038 - val_acc: 0.7890
Epoch 359/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0282 - acc: 0.9914 - val_loss: 2.0674 - val_acc: 0.7706
Epoch 360/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0296 - acc: 0.9912 - val_loss: 2.1318 - val_acc: 0.7706
Epoch 361/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0307 - acc: 0.9902 - val_loss: 1.9900 - val_acc: 0.7752
Epoch 362/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0251 - acc: 0.9914 - val_loss: 2.1893 - val_acc: 0.7706
Epoch 363/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0323 - acc: 0.9897 - val_loss: 2.2470 - val_acc: 0.7706
Epoch 364/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0285 - acc: 0.9908 - val_loss: 1.9385 - val_acc: 0.8073
Epoch 365/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0269 - acc: 0.9906 - val_loss: 1.9820 - val_acc: 0.7890
Epoch 366/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0219 - acc: 0.9929 - val_loss: 2.2226 - val_acc: 0.7752
Epoch 367/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0358 - acc: 0.9912 - val_loss: 2.1258 - val_acc: 0.7706
Epoch 368/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0249 - acc: 0.9914 - val_loss: 2.1584 - val_acc: 0.7706
Epoch 369/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0247 - acc: 0.9918 - val_loss: 2.0074 - val_acc: 0.7936
Epoch 370/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0278 - acc: 0.9912 - val_loss: 1.9158 - val_acc: 0.7706
Epoch 371/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0317 - acc: 0.9911 - val_loss: 1.9685 - val_acc: 0.7982
Epoch 372/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0361 - acc: 0.9908 - val_loss: 1.9630 - val_acc: 0.7982
Epoch 373/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0261 - acc: 0.9915 - val_loss: 1.8262 - val_acc: 0.7890
Epoch 374/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 1.9434 - val_acc: 0.7844
Epoch 375/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0254 - acc: 0.9927 - val_loss: 1.7167 - val_acc: 0.8165
Epoch 376/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0201 - acc: 0.9926 - val_loss: 2.0355 - val_acc: 0.7844
Epoch 377/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0235 - acc: 0.9921 - val_loss: 1.8764 - val_acc: 0.8028
Epoch 378/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0293 - acc: 0.9921 - val_loss: 1.9929 - val_acc: 0.7936
Epoch 379/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0273 - acc: 0.9917 - val_loss: 1.9447 - val_acc: 0.8073
Epoch 380/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0357 - acc: 0.9892 - val_loss: 1.8532 - val_acc: 0.8028
Epoch 381/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0399 - acc: 0.9898 - val_loss: 1.9293 - val_acc: 0.7936
Epoch 382/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0230 - acc: 0.9921 - val_loss: 1.9680 - val_acc: 0.7798
Epoch 383/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0278 - acc: 0.9917 - val_loss: 1.7724 - val_acc: 0.8211
Epoch 384/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0245 - acc: 0.9918 - val_loss: 1.8515 - val_acc: 0.7936
Epoch 385/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0231 - acc: 0.9938 - val_loss: 1.8552 - val_acc: 0.8073
Epoch 386/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0229 - acc: 0.9929 - val_loss: 1.8925 - val_acc: 0.8073
Epoch 387/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0355 - acc: 0.9895 - val_loss: 1.7195 - val_acc: 0.8165
Epoch 388/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0240 - acc: 0.9923 - val_loss: 1.7867 - val_acc: 0.8073
Epoch 389/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0220 - acc: 0.9930 - val_loss: 1.9141 - val_acc: 0.7706
Epoch 390/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0235 - acc: 0.9924 - val_loss: 2.0741 - val_acc: 0.7890
Epoch 391/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0265 - acc: 0.9906 - val_loss: 1.9155 - val_acc: 0.7936
Epoch 392/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0247 - acc: 0.9914 - val_loss: 1.7582 - val_acc: 0.7844
Epoch 393/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0207 - acc: 0.9933 - val_loss: 1.9264 - val_acc: 0.7936
Epoch 394/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0228 - acc: 0.9921 - val_loss: 2.0826 - val_acc: 0.7706
Epoch 395/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0334 - acc: 0.9895 - val_loss: 1.8047 - val_acc: 0.7982
Epoch 396/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0285 - acc: 0.9905 - val_loss: 1.9269 - val_acc: 0.8119
Epoch 397/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0179 - acc: 0.9941 - val_loss: 2.2848 - val_acc: 0.7615
Epoch 398/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0247 - acc: 0.9923 - val_loss: 2.1688 - val_acc: 0.7798
Epoch 399/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0275 - acc: 0.9915 - val_loss: 2.1361 - val_acc: 0.7752
Epoch 400/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0283 - acc: 0.9917 - val_loss: 2.0475 - val_acc: 0.7982
Epoch 401/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0196 - acc: 0.9936 - val_loss: 2.1507 - val_acc: 0.7706
Epoch 402/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0282 - acc: 0.9915 - val_loss: 1.9945 - val_acc: 0.7890
Epoch 403/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0186 - acc: 0.9926 - val_loss: 1.9330 - val_acc: 0.7890
Epoch 404/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0198 - acc: 0.9936 - val_loss: 1.9964 - val_acc: 0.7798
Epoch 405/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0212 - acc: 0.9936 - val_loss: 1.8544 - val_acc: 0.8119
Epoch 406/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0235 - acc: 0.9924 - val_loss: 1.8943 - val_acc: 0.7890
Epoch 407/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0405 - acc: 0.9903 - val_loss: 1.9654 - val_acc: 0.8028
Epoch 408/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0261 - acc: 0.9917 - val_loss: 1.8261 - val_acc: 0.8028
Epoch 409/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0267 - acc: 0.9913 - val_loss: 1.8078 - val_acc: 0.8073
Epoch 410/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0269 - acc: 0.9912 - val_loss: 1.6931 - val_acc: 0.8119
Epoch 411/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0234 - acc: 0.9921 - val_loss: 1.9310 - val_acc: 0.7798
Epoch 412/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0308 - acc: 0.9905 - val_loss: 1.9649 - val_acc: 0.7982
Epoch 413/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0219 - acc: 0.9924 - val_loss: 2.0649 - val_acc: 0.7936
Epoch 414/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0212 - acc: 0.9921 - val_loss: 1.8657 - val_acc: 0.7890
Epoch 415/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0229 - acc: 0.9927 - val_loss: 1.7211 - val_acc: 0.7890
Epoch 416/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0287 - acc: 0.9900 - val_loss: 2.1544 - val_acc: 0.7798
Epoch 417/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0297 - acc: 0.9918 - val_loss: 1.9791 - val_acc: 0.7752
Epoch 418/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0214 - acc: 0.9932 - val_loss: 2.1077 - val_acc: 0.7615
Epoch 419/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0210 - acc: 0.9927 - val_loss: 1.7911 - val_acc: 0.8028
Epoch 420/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0277 - acc: 0.9910 - val_loss: 1.8985 - val_acc: 0.8028
Epoch 421/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0428 - acc: 0.9867 - val_loss: 2.0055 - val_acc: 0.7844
Epoch 422/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0404 - acc: 0.9886 - val_loss: 1.9734 - val_acc: 0.7982
Epoch 423/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0366 - acc: 0.9902 - val_loss: 2.1104 - val_acc: 0.7752
Epoch 424/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0267 - acc: 0.9923 - val_loss: 2.1920 - val_acc: 0.7936
Epoch 425/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0223 - acc: 0.9939 - val_loss: 2.2756 - val_acc: 0.7844
Epoch 426/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0222 - acc: 0.9929 - val_loss: 2.2490 - val_acc: 0.7706
Epoch 427/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0182 - acc: 0.9942 - val_loss: 2.1743 - val_acc: 0.7982
Epoch 428/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0179 - acc: 0.9935 - val_loss: 2.0963 - val_acc: 0.8028
Epoch 429/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0234 - acc: 0.9932 - val_loss: 2.3208 - val_acc: 0.8028
Epoch 430/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0259 - acc: 0.9917 - val_loss: 2.3326 - val_acc: 0.7569
Epoch 431/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0186 - acc: 0.9941 - val_loss: 2.2775 - val_acc: 0.7752
Epoch 432/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0233 - acc: 0.9936 - val_loss: 2.0485 - val_acc: 0.7844
Epoch 433/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0223 - acc: 0.9936 - val_loss: 2.0239 - val_acc: 0.7936
Epoch 434/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0202 - acc: 0.9929 - val_loss: 2.1658 - val_acc: 0.8028
Epoch 435/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0329 - acc: 0.9915 - val_loss: 2.0900 - val_acc: 0.7615
Epoch 436/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0293 - acc: 0.9918 - val_loss: 2.0315 - val_acc: 0.8165
Epoch 437/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0301 - acc: 0.9911 - val_loss: 2.2841 - val_acc: 0.7936
Epoch 438/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0282 - acc: 0.9918 - val_loss: 2.0855 - val_acc: 0.7615
Epoch 439/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0265 - acc: 0.9924 - val_loss: 1.8281 - val_acc: 0.7844
Epoch 440/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0201 - acc: 0.9933 - val_loss: 1.8977 - val_acc: 0.7890
Epoch 441/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0255 - acc: 0.9927 - val_loss: 2.1402 - val_acc: 0.7798
Epoch 442/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0189 - acc: 0.9948 - val_loss: 2.2093 - val_acc: 0.7890
Epoch 443/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0260 - acc: 0.9924 - val_loss: 2.1588 - val_acc: 0.8028
Epoch 444/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0360 - acc: 0.9898 - val_loss: 2.1947 - val_acc: 0.7890
Epoch 445/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0327 - acc: 0.9905 - val_loss: 1.9193 - val_acc: 0.7982
Epoch 446/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0359 - acc: 0.9911 - val_loss: 1.9604 - val_acc: 0.8028
Epoch 447/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0400 - acc: 0.9883 - val_loss: 1.8383 - val_acc: 0.8211
Epoch 448/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0272 - acc: 0.9912 - val_loss: 1.9262 - val_acc: 0.8211
Epoch 449/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0278 - acc: 0.9930 - val_loss: 2.0342 - val_acc: 0.8028
Epoch 450/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0236 - acc: 0.9939 - val_loss: 2.2025 - val_acc: 0.7982
Epoch 451/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0252 - acc: 0.9930 - val_loss: 1.9528 - val_acc: 0.7798
Epoch 452/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0190 - acc: 0.9945 - val_loss: 2.0511 - val_acc: 0.7936
Epoch 453/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0246 - acc: 0.9915 - val_loss: 1.9742 - val_acc: 0.7798
Epoch 454/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0267 - acc: 0.9917 - val_loss: 2.1366 - val_acc: 0.7706
Epoch 455/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0294 - acc: 0.9918 - val_loss: 1.9535 - val_acc: 0.7890
Epoch 456/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0223 - acc: 0.9942 - val_loss: 1.9511 - val_acc: 0.7936
Epoch 457/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0216 - acc: 0.9926 - val_loss: 1.9908 - val_acc: 0.7936
Epoch 458/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0195 - acc: 0.9938 - val_loss: 1.8989 - val_acc: 0.7982
Epoch 459/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0165 - acc: 0.9944 - val_loss: 1.8849 - val_acc: 0.8028
Epoch 460/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0195 - acc: 0.9933 - val_loss: 1.9420 - val_acc: 0.8028
Epoch 461/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0231 - acc: 0.9927 - val_loss: 2.1789 - val_acc: 0.7844
Epoch 462/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0193 - acc: 0.9942 - val_loss: 1.9242 - val_acc: 0.8165
Epoch 463/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0181 - acc: 0.9939 - val_loss: 2.0440 - val_acc: 0.7936
Epoch 464/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0271 - acc: 0.9914 - val_loss: 2.1393 - val_acc: 0.7936
Epoch 465/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0159 - acc: 0.9944 - val_loss: 2.0792 - val_acc: 0.7982
Epoch 466/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0232 - acc: 0.9930 - val_loss: 2.1661 - val_acc: 0.8119
Epoch 467/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0161 - acc: 0.9942 - val_loss: 2.3205 - val_acc: 0.7936
Epoch 468/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0238 - acc: 0.9923 - val_loss: 2.1990 - val_acc: 0.7936
Epoch 469/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0227 - acc: 0.9933 - val_loss: 2.1866 - val_acc: 0.8028
Epoch 470/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0215 - acc: 0.9929 - val_loss: 2.1425 - val_acc: 0.7936
Epoch 471/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0192 - acc: 0.9935 - val_loss: 2.2829 - val_acc: 0.7798
Epoch 472/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0447 - acc: 0.9910 - val_loss: 2.1101 - val_acc: 0.7936
Epoch 473/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0396 - acc: 0.9882 - val_loss: 2.1739 - val_acc: 0.7706
Epoch 474/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0284 - acc: 0.9917 - val_loss: 2.2991 - val_acc: 0.7661
Epoch 475/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0265 - acc: 0.9921 - val_loss: 2.1457 - val_acc: 0.7844
Epoch 476/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0235 - acc: 0.9927 - val_loss: 2.2401 - val_acc: 0.7890
Epoch 477/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0265 - acc: 0.9927 - val_loss: 2.1943 - val_acc: 0.7752
Epoch 478/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0202 - acc: 0.9932 - val_loss: 1.9457 - val_acc: 0.7982
Epoch 479/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0222 - acc: 0.9929 - val_loss: 2.0614 - val_acc: 0.7982
Epoch 480/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0265 - acc: 0.9921 - val_loss: 2.2410 - val_acc: 0.8119
Epoch 481/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0264 - acc: 0.9918 - val_loss: 1.9352 - val_acc: 0.8028
Epoch 482/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0262 - acc: 0.9920 - val_loss: 1.8488 - val_acc: 0.8073
Epoch 483/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0239 - acc: 0.9938 - val_loss: 1.8700 - val_acc: 0.7982
Epoch 484/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0152 - acc: 0.9952 - val_loss: 1.9171 - val_acc: 0.8028
Epoch 485/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0184 - acc: 0.9948 - val_loss: 1.8533 - val_acc: 0.7936
Epoch 486/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0167 - acc: 0.9945 - val_loss: 1.9940 - val_acc: 0.8073
Epoch 487/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0191 - acc: 0.9938 - val_loss: 1.9391 - val_acc: 0.8165
Epoch 488/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0185 - acc: 0.9932 - val_loss: 1.9806 - val_acc: 0.8119
Epoch 489/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0208 - acc: 0.9936 - val_loss: 1.9697 - val_acc: 0.7982
Epoch 490/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0192 - acc: 0.9939 - val_loss: 1.9989 - val_acc: 0.8028
Epoch 491/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0189 - acc: 0.9932 - val_loss: 2.0591 - val_acc: 0.8028
Epoch 492/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0199 - acc: 0.9938 - val_loss: 2.0642 - val_acc: 0.8028
Epoch 493/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0281 - acc: 0.9914 - val_loss: 2.0395 - val_acc: 0.7890
Epoch 494/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0297 - acc: 0.9911 - val_loss: 2.1060 - val_acc: 0.7844
Epoch 495/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0189 - acc: 0.9936 - val_loss: 2.3261 - val_acc: 0.7706
Epoch 496/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0244 - acc: 0.9912 - val_loss: 2.1769 - val_acc: 0.7890
Epoch 497/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0357 - acc: 0.9905 - val_loss: 2.1489 - val_acc: 0.7890
Epoch 498/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0268 - acc: 0.9908 - val_loss: 1.8625 - val_acc: 0.8073
Epoch 499/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0194 - acc: 0.9939 - val_loss: 2.1564 - val_acc: 0.7890
Epoch 500/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0238 - acc: 0.9926 - val_loss: 2.2253 - val_acc: 0.7844
40/40 [==============================] - 50s 1s/step
Accuracy: 0.8166582021330625. F1: 0.812294948495575
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">make_model2D_S</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;2D_small_3&quot;</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 83s 628ms/step - loss: 1.6677 - acc: 0.3698 - val_loss: 1.1795 - val_acc: 0.5596
Epoch 2/500
132/132 [==============================] - 30s 228ms/step - loss: 1.0659 - acc: 0.5942 - val_loss: 0.9469 - val_acc: 0.6606
Epoch 3/500
132/132 [==============================] - 32s 242ms/step - loss: 0.9186 - acc: 0.6553 - val_loss: 0.8398 - val_acc: 0.6972
Epoch 4/500
132/132 [==============================] - 31s 237ms/step - loss: 0.8267 - acc: 0.6936 - val_loss: 0.7937 - val_acc: 0.7018
Epoch 5/500
132/132 [==============================] - 31s 237ms/step - loss: 0.7654 - acc: 0.7234 - val_loss: 0.7509 - val_acc: 0.7294
Epoch 6/500
132/132 [==============================] - 32s 244ms/step - loss: 0.7031 - acc: 0.7441 - val_loss: 0.6537 - val_acc: 0.7385
Epoch 7/500
132/132 [==============================] - 33s 248ms/step - loss: 0.6657 - acc: 0.7612 - val_loss: 0.5802 - val_acc: 0.7890
Epoch 8/500
132/132 [==============================] - 33s 248ms/step - loss: 0.6510 - acc: 0.7686 - val_loss: 0.5583 - val_acc: 0.7523
Epoch 9/500
132/132 [==============================] - 33s 252ms/step - loss: 0.6092 - acc: 0.7819 - val_loss: 0.5453 - val_acc: 0.7890
Epoch 10/500
132/132 [==============================] - 34s 261ms/step - loss: 0.5962 - acc: 0.7771 - val_loss: 0.5113 - val_acc: 0.7982
Epoch 11/500
132/132 [==============================] - 35s 268ms/step - loss: 0.5685 - acc: 0.7942 - val_loss: 0.5280 - val_acc: 0.7798
Epoch 12/500
132/132 [==============================] - 33s 252ms/step - loss: 0.5560 - acc: 0.8024 - val_loss: 0.5037 - val_acc: 0.7890
Epoch 13/500
132/132 [==============================] - 35s 267ms/step - loss: 0.5295 - acc: 0.8080 - val_loss: 0.4781 - val_acc: 0.8073
Epoch 14/500
132/132 [==============================] - 35s 264ms/step - loss: 0.4992 - acc: 0.8171 - val_loss: 0.5202 - val_acc: 0.8073
Epoch 15/500
132/132 [==============================] - 35s 264ms/step - loss: 0.4770 - acc: 0.8302 - val_loss: 0.4893 - val_acc: 0.8028
Epoch 16/500
132/132 [==============================] - 34s 257ms/step - loss: 0.4605 - acc: 0.8327 - val_loss: 0.4721 - val_acc: 0.8257
Epoch 17/500
132/132 [==============================] - 33s 247ms/step - loss: 0.4445 - acc: 0.8345 - val_loss: 0.4639 - val_acc: 0.8119
Epoch 18/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4345 - acc: 0.8380 - val_loss: 0.4896 - val_acc: 0.8211
Epoch 19/500
132/132 [==============================] - 31s 237ms/step - loss: 0.4213 - acc: 0.8432 - val_loss: 0.4687 - val_acc: 0.8119
Epoch 20/500
132/132 [==============================] - 34s 254ms/step - loss: 0.3939 - acc: 0.8517 - val_loss: 0.4105 - val_acc: 0.8440
Epoch 21/500
132/132 [==============================] - 33s 254ms/step - loss: 0.3896 - acc: 0.8563 - val_loss: 0.4469 - val_acc: 0.8165
Epoch 22/500
132/132 [==============================] - 33s 249ms/step - loss: 0.3847 - acc: 0.8541 - val_loss: 0.4627 - val_acc: 0.8119
Epoch 23/500
132/132 [==============================] - 34s 256ms/step - loss: 0.3839 - acc: 0.8586 - val_loss: 0.4236 - val_acc: 0.8394
Epoch 24/500
132/132 [==============================] - 33s 252ms/step - loss: 0.3583 - acc: 0.8711 - val_loss: 0.4224 - val_acc: 0.8394
Epoch 25/500
132/132 [==============================] - 34s 259ms/step - loss: 0.3439 - acc: 0.8710 - val_loss: 0.3908 - val_acc: 0.8578
Epoch 26/500
132/132 [==============================] - 33s 250ms/step - loss: 0.3302 - acc: 0.8823 - val_loss: 0.3874 - val_acc: 0.8486
Epoch 27/500
132/132 [==============================] - 32s 245ms/step - loss: 0.3216 - acc: 0.8801 - val_loss: 0.4344 - val_acc: 0.8257
Epoch 28/500
132/132 [==============================] - 33s 252ms/step - loss: 0.3096 - acc: 0.8824 - val_loss: 0.3900 - val_acc: 0.8578
Epoch 29/500
132/132 [==============================] - 34s 259ms/step - loss: 0.2919 - acc: 0.8941 - val_loss: 0.4114 - val_acc: 0.8578
Epoch 30/500
132/132 [==============================] - 34s 258ms/step - loss: 0.2911 - acc: 0.8901 - val_loss: 0.4233 - val_acc: 0.8670
Epoch 31/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2997 - acc: 0.8883 - val_loss: 0.4367 - val_acc: 0.8303
Epoch 32/500
132/132 [==============================] - 31s 238ms/step - loss: 0.2885 - acc: 0.8922 - val_loss: 0.4040 - val_acc: 0.8532
Epoch 33/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2716 - acc: 0.8958 - val_loss: 0.4369 - val_acc: 0.8303
Epoch 34/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2751 - acc: 0.8956 - val_loss: 0.4103 - val_acc: 0.8532
Epoch 35/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2627 - acc: 0.9003 - val_loss: 0.3942 - val_acc: 0.8670
Epoch 36/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2496 - acc: 0.9052 - val_loss: 0.4128 - val_acc: 0.8532
Epoch 37/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2614 - acc: 0.8995 - val_loss: 0.4083 - val_acc: 0.8440
Epoch 38/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2416 - acc: 0.9088 - val_loss: 0.4295 - val_acc: 0.8440
Epoch 39/500
132/132 [==============================] - 31s 237ms/step - loss: 0.2328 - acc: 0.9122 - val_loss: 0.4251 - val_acc: 0.8532
Epoch 40/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2359 - acc: 0.9114 - val_loss: 0.4082 - val_acc: 0.8349
Epoch 41/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2269 - acc: 0.9150 - val_loss: 0.4002 - val_acc: 0.8624
Epoch 42/500
132/132 [==============================] - 31s 235ms/step - loss: 0.2064 - acc: 0.9229 - val_loss: 0.4258 - val_acc: 0.8578
Epoch 43/500
132/132 [==============================] - 32s 244ms/step - loss: 0.2053 - acc: 0.9220 - val_loss: 0.4535 - val_acc: 0.8349
Epoch 44/500
132/132 [==============================] - 32s 241ms/step - loss: 0.2087 - acc: 0.9217 - val_loss: 0.4191 - val_acc: 0.8486
Epoch 45/500
132/132 [==============================] - 31s 236ms/step - loss: 0.2045 - acc: 0.9217 - val_loss: 0.4468 - val_acc: 0.8670
Epoch 46/500
132/132 [==============================] - 32s 241ms/step - loss: 0.1978 - acc: 0.9245 - val_loss: 0.4443 - val_acc: 0.8624
Epoch 47/500
132/132 [==============================] - 35s 264ms/step - loss: 0.1941 - acc: 0.9267 - val_loss: 0.4397 - val_acc: 0.8578
Epoch 48/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1862 - acc: 0.9264 - val_loss: 0.4411 - val_acc: 0.8486
Epoch 49/500
132/132 [==============================] - 34s 260ms/step - loss: 0.1872 - acc: 0.9288 - val_loss: 0.4824 - val_acc: 0.8670
Epoch 50/500
132/132 [==============================] - 35s 262ms/step - loss: 0.1613 - acc: 0.9359 - val_loss: 0.5081 - val_acc: 0.8440
Epoch 51/500
132/132 [==============================] - 33s 249ms/step - loss: 0.1769 - acc: 0.9300 - val_loss: 0.5150 - val_acc: 0.8624
Epoch 52/500
132/132 [==============================] - 33s 247ms/step - loss: 0.1617 - acc: 0.9376 - val_loss: 0.4704 - val_acc: 0.8578
Epoch 53/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1614 - acc: 0.9412 - val_loss: 0.5435 - val_acc: 0.8211
Epoch 54/500
132/132 [==============================] - 34s 257ms/step - loss: 0.1687 - acc: 0.9361 - val_loss: 0.6075 - val_acc: 0.8303
Epoch 55/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1524 - acc: 0.9411 - val_loss: 0.5612 - val_acc: 0.8257
Epoch 56/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1391 - acc: 0.9449 - val_loss: 0.4625 - val_acc: 0.8532
Epoch 57/500
132/132 [==============================] - 35s 263ms/step - loss: 0.1505 - acc: 0.9423 - val_loss: 0.6258 - val_acc: 0.8440
Epoch 58/500
132/132 [==============================] - 34s 256ms/step - loss: 0.1566 - acc: 0.9401 - val_loss: 0.5103 - val_acc: 0.8578
Epoch 59/500
132/132 [==============================] - 34s 261ms/step - loss: 0.1323 - acc: 0.9485 - val_loss: 0.5505 - val_acc: 0.8578
Epoch 60/500
132/132 [==============================] - 32s 242ms/step - loss: 0.1379 - acc: 0.9466 - val_loss: 0.4878 - val_acc: 0.8716
Epoch 61/500
132/132 [==============================] - 32s 240ms/step - loss: 0.1396 - acc: 0.9458 - val_loss: 0.5611 - val_acc: 0.8624
Epoch 62/500
132/132 [==============================] - 33s 251ms/step - loss: 0.1402 - acc: 0.9486 - val_loss: 0.4917 - val_acc: 0.8578
Epoch 63/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1412 - acc: 0.9494 - val_loss: 0.6134 - val_acc: 0.8165
Epoch 64/500
132/132 [==============================] - 34s 261ms/step - loss: 0.1238 - acc: 0.9547 - val_loss: 0.5525 - val_acc: 0.8624
Epoch 65/500
132/132 [==============================] - 32s 245ms/step - loss: 0.1228 - acc: 0.9515 - val_loss: 0.5772 - val_acc: 0.8624
Epoch 66/500
132/132 [==============================] - 31s 236ms/step - loss: 0.1086 - acc: 0.9559 - val_loss: 0.6473 - val_acc: 0.8716
Epoch 67/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1133 - acc: 0.9565 - val_loss: 0.5563 - val_acc: 0.8578
Epoch 68/500
132/132 [==============================] - 35s 264ms/step - loss: 0.1127 - acc: 0.9583 - val_loss: 0.5683 - val_acc: 0.8670
Epoch 69/500
132/132 [==============================] - 32s 243ms/step - loss: 0.1055 - acc: 0.9586 - val_loss: 0.5733 - val_acc: 0.8624
Epoch 70/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0988 - acc: 0.9621 - val_loss: 0.5832 - val_acc: 0.8486
Epoch 71/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0998 - acc: 0.9636 - val_loss: 0.5855 - val_acc: 0.8394
Epoch 72/500
132/132 [==============================] - 33s 250ms/step - loss: 0.1140 - acc: 0.9568 - val_loss: 0.5254 - val_acc: 0.8624
Epoch 73/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0994 - acc: 0.9626 - val_loss: 0.5056 - val_acc: 0.8624
Epoch 74/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0900 - acc: 0.9661 - val_loss: 0.5527 - val_acc: 0.8853
Epoch 75/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0999 - acc: 0.9626 - val_loss: 0.5523 - val_acc: 0.8761
Epoch 76/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0999 - acc: 0.9613 - val_loss: 0.5663 - val_acc: 0.8670
Epoch 77/500
132/132 [==============================] - 31s 235ms/step - loss: 0.1385 - acc: 0.9488 - val_loss: 0.5874 - val_acc: 0.8624
Epoch 78/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0891 - acc: 0.9653 - val_loss: 0.5255 - val_acc: 0.8899
Epoch 79/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0934 - acc: 0.9665 - val_loss: 0.5911 - val_acc: 0.8486
Epoch 80/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0954 - acc: 0.9626 - val_loss: 0.5732 - val_acc: 0.8624
Epoch 81/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0901 - acc: 0.9667 - val_loss: 0.5846 - val_acc: 0.8578
Epoch 82/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0842 - acc: 0.9680 - val_loss: 0.6011 - val_acc: 0.8486
Epoch 83/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0958 - acc: 0.9650 - val_loss: 0.5349 - val_acc: 0.8761
Epoch 84/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0801 - acc: 0.9698 - val_loss: 0.7330 - val_acc: 0.8440
Epoch 85/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0800 - acc: 0.9694 - val_loss: 0.6742 - val_acc: 0.8486
Epoch 86/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0840 - acc: 0.9708 - val_loss: 0.6269 - val_acc: 0.8624
Epoch 87/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0789 - acc: 0.9692 - val_loss: 0.5917 - val_acc: 0.8624
Epoch 88/500
132/132 [==============================] - 33s 254ms/step - loss: 0.0751 - acc: 0.9714 - val_loss: 0.5783 - val_acc: 0.8578
Epoch 89/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0771 - acc: 0.9695 - val_loss: 0.6123 - val_acc: 0.8670
Epoch 90/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0839 - acc: 0.9700 - val_loss: 0.7275 - val_acc: 0.8670
Epoch 91/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0914 - acc: 0.9679 - val_loss: 0.6232 - val_acc: 0.8578
Epoch 92/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0813 - acc: 0.9698 - val_loss: 0.6272 - val_acc: 0.8624
Epoch 93/500
132/132 [==============================] - 34s 259ms/step - loss: 0.0669 - acc: 0.9770 - val_loss: 0.7024 - val_acc: 0.8486
Epoch 94/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0746 - acc: 0.9723 - val_loss: 0.5374 - val_acc: 0.8578
Epoch 95/500
132/132 [==============================] - 33s 253ms/step - loss: 0.0690 - acc: 0.9750 - val_loss: 0.6271 - val_acc: 0.8670
Epoch 96/500
132/132 [==============================] - 34s 258ms/step - loss: 0.0855 - acc: 0.9698 - val_loss: 0.7590 - val_acc: 0.8532
Epoch 97/500
132/132 [==============================] - 35s 267ms/step - loss: 0.0798 - acc: 0.9688 - val_loss: 0.6380 - val_acc: 0.8716
Epoch 98/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0764 - acc: 0.9725 - val_loss: 0.6218 - val_acc: 0.8578
Epoch 99/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0656 - acc: 0.9756 - val_loss: 0.6322 - val_acc: 0.8716
Epoch 100/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0621 - acc: 0.9780 - val_loss: 0.7663 - val_acc: 0.8440
Epoch 101/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0845 - acc: 0.9676 - val_loss: 0.7384 - val_acc: 0.8257
Epoch 102/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0811 - acc: 0.9706 - val_loss: 0.6613 - val_acc: 0.8532
Epoch 103/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0618 - acc: 0.9794 - val_loss: 0.7658 - val_acc: 0.8486
Epoch 104/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0563 - acc: 0.9761 - val_loss: 0.7534 - val_acc: 0.8532
Epoch 105/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0772 - acc: 0.9702 - val_loss: 0.6975 - val_acc: 0.8578
Epoch 106/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0646 - acc: 0.9753 - val_loss: 0.6858 - val_acc: 0.8440
Epoch 107/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0731 - acc: 0.9718 - val_loss: 0.6455 - val_acc: 0.8578
Epoch 108/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0594 - acc: 0.9789 - val_loss: 0.7168 - val_acc: 0.8624
Epoch 109/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0599 - acc: 0.9771 - val_loss: 0.6973 - val_acc: 0.8624
Epoch 110/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0540 - acc: 0.9808 - val_loss: 0.6560 - val_acc: 0.8761
Epoch 111/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0640 - acc: 0.9779 - val_loss: 0.7615 - val_acc: 0.8486
Epoch 112/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0869 - acc: 0.9665 - val_loss: 0.7635 - val_acc: 0.8624
Epoch 113/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0683 - acc: 0.9738 - val_loss: 0.7093 - val_acc: 0.8532
Epoch 114/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0624 - acc: 0.9773 - val_loss: 0.6897 - val_acc: 0.8624
Epoch 115/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0451 - acc: 0.9814 - val_loss: 0.7371 - val_acc: 0.8670
Epoch 116/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0578 - acc: 0.9773 - val_loss: 0.7491 - val_acc: 0.8670
Epoch 117/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0577 - acc: 0.9786 - val_loss: 0.7704 - val_acc: 0.8578
Epoch 118/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0746 - acc: 0.9744 - val_loss: 0.9144 - val_acc: 0.8716
Epoch 119/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0710 - acc: 0.9733 - val_loss: 0.7521 - val_acc: 0.8624
Epoch 120/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0546 - acc: 0.9802 - val_loss: 0.7837 - val_acc: 0.8670
Epoch 121/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0563 - acc: 0.9805 - val_loss: 0.6411 - val_acc: 0.8624
Epoch 122/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0443 - acc: 0.9833 - val_loss: 0.7739 - val_acc: 0.8761
Epoch 123/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0493 - acc: 0.9803 - val_loss: 0.7207 - val_acc: 0.8670
Epoch 124/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0503 - acc: 0.9818 - val_loss: 0.7945 - val_acc: 0.8532
Epoch 125/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0482 - acc: 0.9827 - val_loss: 0.8800 - val_acc: 0.8440
Epoch 126/500
132/132 [==============================] - 33s 252ms/step - loss: 0.0489 - acc: 0.9814 - val_loss: 0.8158 - val_acc: 0.8578
Epoch 127/500
132/132 [==============================] - 33s 253ms/step - loss: 0.0591 - acc: 0.9791 - val_loss: 1.0348 - val_acc: 0.8578
Epoch 128/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0596 - acc: 0.9783 - val_loss: 0.9605 - val_acc: 0.8670
Epoch 129/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0519 - acc: 0.9782 - val_loss: 0.8483 - val_acc: 0.8716
Epoch 130/500
132/132 [==============================] - 34s 254ms/step - loss: 0.0619 - acc: 0.9753 - val_loss: 0.8374 - val_acc: 0.8761
Epoch 131/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0520 - acc: 0.9792 - val_loss: 0.8108 - val_acc: 0.8670
Epoch 132/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0460 - acc: 0.9820 - val_loss: 0.8356 - val_acc: 0.8716
Epoch 133/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0507 - acc: 0.9809 - val_loss: 0.8994 - val_acc: 0.8486
Epoch 134/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0610 - acc: 0.9762 - val_loss: 0.6906 - val_acc: 0.8716
Epoch 135/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0546 - acc: 0.9815 - val_loss: 0.7684 - val_acc: 0.8899
Epoch 136/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0527 - acc: 0.9809 - val_loss: 0.8643 - val_acc: 0.8578
Epoch 137/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0500 - acc: 0.9812 - val_loss: 0.7944 - val_acc: 0.8624
Epoch 138/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0498 - acc: 0.9826 - val_loss: 0.9194 - val_acc: 0.8486
Epoch 139/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0501 - acc: 0.9827 - val_loss: 0.7983 - val_acc: 0.8761
Epoch 140/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0448 - acc: 0.9829 - val_loss: 0.9033 - val_acc: 0.8624
Epoch 141/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0393 - acc: 0.9858 - val_loss: 0.8335 - val_acc: 0.8578
Epoch 142/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0525 - acc: 0.9814 - val_loss: 0.8081 - val_acc: 0.8716
Epoch 143/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0536 - acc: 0.9802 - val_loss: 0.7624 - val_acc: 0.8807
Epoch 144/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0510 - acc: 0.9812 - val_loss: 0.9232 - val_acc: 0.8624
Epoch 145/500
132/132 [==============================] - 33s 251ms/step - loss: 0.0435 - acc: 0.9836 - val_loss: 0.8820 - val_acc: 0.8624
Epoch 146/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0465 - acc: 0.9844 - val_loss: 0.9241 - val_acc: 0.8486
Epoch 147/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0414 - acc: 0.9850 - val_loss: 0.9078 - val_acc: 0.8394
Epoch 148/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0373 - acc: 0.9862 - val_loss: 0.8999 - val_acc: 0.8578
Epoch 149/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0349 - acc: 0.9860 - val_loss: 0.9805 - val_acc: 0.8761
Epoch 150/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0523 - acc: 0.9792 - val_loss: 1.0490 - val_acc: 0.8440
Epoch 151/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0405 - acc: 0.9846 - val_loss: 0.9227 - val_acc: 0.8532
Epoch 152/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0417 - acc: 0.9847 - val_loss: 0.8898 - val_acc: 0.8578
Epoch 153/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0510 - acc: 0.9817 - val_loss: 0.8941 - val_acc: 0.8578
Epoch 154/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0441 - acc: 0.9842 - val_loss: 0.8996 - val_acc: 0.8624
Epoch 155/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0402 - acc: 0.9850 - val_loss: 0.9134 - val_acc: 0.8486
Epoch 156/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0492 - acc: 0.9839 - val_loss: 0.9851 - val_acc: 0.8349
Epoch 157/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0516 - acc: 0.9823 - val_loss: 0.9295 - val_acc: 0.8486
Epoch 158/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0423 - acc: 0.9834 - val_loss: 0.9387 - val_acc: 0.8578
Epoch 159/500
132/132 [==============================] - 33s 251ms/step - loss: 0.0428 - acc: 0.9838 - val_loss: 1.0293 - val_acc: 0.8349
Epoch 160/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0547 - acc: 0.9816 - val_loss: 0.9005 - val_acc: 0.8578
Epoch 161/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0554 - acc: 0.9792 - val_loss: 0.9215 - val_acc: 0.8440
Epoch 162/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0474 - acc: 0.9818 - val_loss: 0.9615 - val_acc: 0.8532
Epoch 163/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0395 - acc: 0.9858 - val_loss: 0.9708 - val_acc: 0.8532
Epoch 164/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0394 - acc: 0.9865 - val_loss: 1.0865 - val_acc: 0.8440
Epoch 165/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0399 - acc: 0.9852 - val_loss: 0.9644 - val_acc: 0.8624
Epoch 166/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0433 - acc: 0.9833 - val_loss: 0.9910 - val_acc: 0.8394
Epoch 167/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0479 - acc: 0.9842 - val_loss: 0.9442 - val_acc: 0.8670
Epoch 168/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0392 - acc: 0.9871 - val_loss: 0.8864 - val_acc: 0.8670
Epoch 169/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0486 - acc: 0.9811 - val_loss: 0.8395 - val_acc: 0.8578
Epoch 170/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0370 - acc: 0.9862 - val_loss: 0.9980 - val_acc: 0.8578
Epoch 171/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0401 - acc: 0.9862 - val_loss: 1.0235 - val_acc: 0.8486
Epoch 172/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0482 - acc: 0.9824 - val_loss: 1.0310 - val_acc: 0.8716
Epoch 173/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0420 - acc: 0.9842 - val_loss: 0.8958 - val_acc: 0.8578
Epoch 174/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0361 - acc: 0.9880 - val_loss: 0.9574 - val_acc: 0.8486
Epoch 175/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0416 - acc: 0.9855 - val_loss: 1.1609 - val_acc: 0.8440
Epoch 176/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0401 - acc: 0.9853 - val_loss: 0.9665 - val_acc: 0.8716
Epoch 177/500
132/132 [==============================] - 33s 248ms/step - loss: 0.0282 - acc: 0.9886 - val_loss: 1.0445 - val_acc: 0.8624
Epoch 178/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0304 - acc: 0.9885 - val_loss: 1.1043 - val_acc: 0.8532
Epoch 179/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0309 - acc: 0.9880 - val_loss: 1.0346 - val_acc: 0.8486
Epoch 180/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0393 - acc: 0.9855 - val_loss: 0.8399 - val_acc: 0.8716
Epoch 181/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0246 - acc: 0.9908 - val_loss: 1.0831 - val_acc: 0.8394
Epoch 182/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0418 - acc: 0.9838 - val_loss: 1.0091 - val_acc: 0.8486
Epoch 183/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0374 - acc: 0.9865 - val_loss: 0.8674 - val_acc: 0.8716
Epoch 184/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0306 - acc: 0.9906 - val_loss: 1.0643 - val_acc: 0.8624
Epoch 185/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0376 - acc: 0.9855 - val_loss: 0.8712 - val_acc: 0.8853
Epoch 186/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0389 - acc: 0.9871 - val_loss: 0.9158 - val_acc: 0.8532
Epoch 187/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0383 - acc: 0.9879 - val_loss: 0.9774 - val_acc: 0.8716
Epoch 188/500
132/132 [==============================] - 33s 247ms/step - loss: 0.0388 - acc: 0.9862 - val_loss: 1.1004 - val_acc: 0.8532
Epoch 189/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0337 - acc: 0.9880 - val_loss: 1.1040 - val_acc: 0.8486
Epoch 190/500
132/132 [==============================] - 33s 253ms/step - loss: 0.0460 - acc: 0.9844 - val_loss: 0.8050 - val_acc: 0.8670
Epoch 191/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0350 - acc: 0.9868 - val_loss: 1.1062 - val_acc: 0.8349
Epoch 192/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0405 - acc: 0.9858 - val_loss: 0.9877 - val_acc: 0.8716
Epoch 193/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0320 - acc: 0.9895 - val_loss: 0.8517 - val_acc: 0.8853
Epoch 194/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0407 - acc: 0.9844 - val_loss: 1.0278 - val_acc: 0.8624
Epoch 195/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0408 - acc: 0.9857 - val_loss: 1.1557 - val_acc: 0.8578
Epoch 196/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0416 - acc: 0.9852 - val_loss: 0.9602 - val_acc: 0.8532
Epoch 197/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0283 - acc: 0.9886 - val_loss: 0.9557 - val_acc: 0.8624
Epoch 198/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0295 - acc: 0.9897 - val_loss: 1.0849 - val_acc: 0.8486
Epoch 199/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0274 - acc: 0.9895 - val_loss: 1.0359 - val_acc: 0.8761
Epoch 200/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0222 - acc: 0.9912 - val_loss: 1.0268 - val_acc: 0.8761
Epoch 201/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0334 - acc: 0.9879 - val_loss: 1.0936 - val_acc: 0.8670
Epoch 202/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0395 - acc: 0.9859 - val_loss: 0.9785 - val_acc: 0.8532
Epoch 203/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0536 - acc: 0.9814 - val_loss: 0.8786 - val_acc: 0.8578
Epoch 204/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0414 - acc: 0.9862 - val_loss: 1.0248 - val_acc: 0.8578
Epoch 205/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0365 - acc: 0.9895 - val_loss: 1.1494 - val_acc: 0.8394
Epoch 206/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0359 - acc: 0.9874 - val_loss: 0.8393 - val_acc: 0.8761
Epoch 207/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0286 - acc: 0.9902 - val_loss: 0.7575 - val_acc: 0.8624
Epoch 208/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0204 - acc: 0.9924 - val_loss: 1.1539 - val_acc: 0.8578
Epoch 209/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0395 - acc: 0.9855 - val_loss: 1.1012 - val_acc: 0.8716
Epoch 210/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0224 - acc: 0.9909 - val_loss: 1.0744 - val_acc: 0.8624
Epoch 211/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0259 - acc: 0.9924 - val_loss: 1.0702 - val_acc: 0.8761
Epoch 212/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0214 - acc: 0.9923 - val_loss: 1.0352 - val_acc: 0.8486
Epoch 213/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0245 - acc: 0.9905 - val_loss: 1.1934 - val_acc: 0.8486
Epoch 214/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0247 - acc: 0.9891 - val_loss: 1.0277 - val_acc: 0.8716
Epoch 215/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0293 - acc: 0.9895 - val_loss: 1.0839 - val_acc: 0.8578
Epoch 216/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0328 - acc: 0.9880 - val_loss: 0.9953 - val_acc: 0.8670
Epoch 217/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0217 - acc: 0.9912 - val_loss: 0.9669 - val_acc: 0.8624
Epoch 218/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0418 - acc: 0.9861 - val_loss: 0.9251 - val_acc: 0.8670
Epoch 219/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0395 - acc: 0.9864 - val_loss: 0.9731 - val_acc: 0.8624
Epoch 220/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0349 - acc: 0.9883 - val_loss: 0.9611 - val_acc: 0.8532
Epoch 221/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0359 - acc: 0.9882 - val_loss: 0.8350 - val_acc: 0.8716
Epoch 222/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0246 - acc: 0.9903 - val_loss: 0.9274 - val_acc: 0.8670
Epoch 223/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0267 - acc: 0.9915 - val_loss: 0.8627 - val_acc: 0.8624
Epoch 224/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0229 - acc: 0.9915 - val_loss: 1.1943 - val_acc: 0.8670
Epoch 225/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0238 - acc: 0.9909 - val_loss: 1.1001 - val_acc: 0.8670
Epoch 226/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0372 - acc: 0.9864 - val_loss: 0.9795 - val_acc: 0.8486
Epoch 227/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0294 - acc: 0.9888 - val_loss: 0.9817 - val_acc: 0.8761
Epoch 228/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0398 - acc: 0.9868 - val_loss: 1.0380 - val_acc: 0.8394
Epoch 229/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0418 - acc: 0.9838 - val_loss: 1.0747 - val_acc: 0.8440
Epoch 230/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0265 - acc: 0.9891 - val_loss: 0.9846 - val_acc: 0.8670
Epoch 231/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0353 - acc: 0.9876 - val_loss: 0.9610 - val_acc: 0.8716
Epoch 232/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0262 - acc: 0.9905 - val_loss: 0.9793 - val_acc: 0.8670
Epoch 233/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0187 - acc: 0.9923 - val_loss: 0.8826 - val_acc: 0.8761
Epoch 234/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0215 - acc: 0.9918 - val_loss: 1.0573 - val_acc: 0.8578
Epoch 235/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0266 - acc: 0.9905 - val_loss: 0.9917 - val_acc: 0.8716
Epoch 236/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0486 - acc: 0.9859 - val_loss: 0.8153 - val_acc: 0.8624
Epoch 237/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0354 - acc: 0.9858 - val_loss: 0.9442 - val_acc: 0.8670
Epoch 238/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0324 - acc: 0.9883 - val_loss: 0.8336 - val_acc: 0.8578
Epoch 239/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0329 - acc: 0.9889 - val_loss: 0.8453 - val_acc: 0.8853
Epoch 240/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0376 - acc: 0.9883 - val_loss: 0.9641 - val_acc: 0.8578
Epoch 241/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0255 - acc: 0.9894 - val_loss: 0.9770 - val_acc: 0.8716
Epoch 242/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0280 - acc: 0.9900 - val_loss: 0.8943 - val_acc: 0.8853
Epoch 243/500
132/132 [==============================] - 32s 246ms/step - loss: 0.0274 - acc: 0.9897 - val_loss: 0.8758 - val_acc: 0.8899
Epoch 244/500
132/132 [==============================] - 29s 219ms/step - loss: 0.0191 - acc: 0.9917 - val_loss: 1.0095 - val_acc: 0.8899
Epoch 245/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0259 - acc: 0.9911 - val_loss: 1.0754 - val_acc: 0.8670
Epoch 246/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0229 - acc: 0.9932 - val_loss: 1.0039 - val_acc: 0.8761
Epoch 247/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0240 - acc: 0.9911 - val_loss: 1.0385 - val_acc: 0.8853
Epoch 248/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0300 - acc: 0.9894 - val_loss: 0.9974 - val_acc: 0.8761
Epoch 249/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0382 - acc: 0.9880 - val_loss: 0.9304 - val_acc: 0.8670
Epoch 250/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0288 - acc: 0.9909 - val_loss: 1.1046 - val_acc: 0.8624
Epoch 251/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0319 - acc: 0.9877 - val_loss: 0.7928 - val_acc: 0.8991
Epoch 252/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0235 - acc: 0.9915 - val_loss: 1.1124 - val_acc: 0.8440
Epoch 253/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0337 - acc: 0.9885 - val_loss: 0.8684 - val_acc: 0.8670
Epoch 254/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0317 - acc: 0.9892 - val_loss: 1.0258 - val_acc: 0.8761
Epoch 255/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0441 - acc: 0.9844 - val_loss: 0.9258 - val_acc: 0.8761
Epoch 256/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0223 - acc: 0.9915 - val_loss: 0.9499 - val_acc: 0.8624
Epoch 257/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0235 - acc: 0.9911 - val_loss: 0.8813 - val_acc: 0.8578
Epoch 258/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0157 - acc: 0.9935 - val_loss: 0.9777 - val_acc: 0.8761
Epoch 259/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0202 - acc: 0.9930 - val_loss: 1.1069 - val_acc: 0.8761
Epoch 260/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0248 - acc: 0.9902 - val_loss: 1.0700 - val_acc: 0.8853
Epoch 261/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0248 - acc: 0.9911 - val_loss: 0.9342 - val_acc: 0.8807
Epoch 262/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0297 - acc: 0.9900 - val_loss: 1.0245 - val_acc: 0.8349
Epoch 263/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0322 - acc: 0.9888 - val_loss: 0.9876 - val_acc: 0.8716
Epoch 264/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0231 - acc: 0.9912 - val_loss: 1.0122 - val_acc: 0.8807
Epoch 265/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0255 - acc: 0.9903 - val_loss: 1.0115 - val_acc: 0.8670
Epoch 266/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0364 - acc: 0.9880 - val_loss: 1.0727 - val_acc: 0.8624
Epoch 267/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0372 - acc: 0.9885 - val_loss: 0.9936 - val_acc: 0.8899
Epoch 268/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0290 - acc: 0.9909 - val_loss: 1.0361 - val_acc: 0.8670
Epoch 269/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0222 - acc: 0.9936 - val_loss: 1.1648 - val_acc: 0.8670
Epoch 270/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0351 - acc: 0.9874 - val_loss: 0.8661 - val_acc: 0.8991
Epoch 271/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0322 - acc: 0.9882 - val_loss: 1.0256 - val_acc: 0.8761
Epoch 272/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0308 - acc: 0.9882 - val_loss: 1.0875 - val_acc: 0.8807
Epoch 273/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0288 - acc: 0.9902 - val_loss: 0.9883 - val_acc: 0.8807
Epoch 274/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0335 - acc: 0.9891 - val_loss: 1.0665 - val_acc: 0.8440
Epoch 275/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0275 - acc: 0.9915 - val_loss: 1.1629 - val_acc: 0.8807
Epoch 276/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0267 - acc: 0.9914 - val_loss: 1.0351 - val_acc: 0.8761
Epoch 277/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0261 - acc: 0.9909 - val_loss: 0.9689 - val_acc: 0.8670
Epoch 278/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0317 - acc: 0.9883 - val_loss: 0.8575 - val_acc: 0.8716
Epoch 279/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0241 - acc: 0.9914 - val_loss: 1.0340 - val_acc: 0.8716
Epoch 280/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0259 - acc: 0.9905 - val_loss: 1.0142 - val_acc: 0.8578
Epoch 281/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0256 - acc: 0.9915 - val_loss: 1.0707 - val_acc: 0.8670
Epoch 282/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0350 - acc: 0.9874 - val_loss: 0.9133 - val_acc: 0.8853
Epoch 283/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0301 - acc: 0.9892 - val_loss: 0.8837 - val_acc: 0.8624
Epoch 284/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0245 - acc: 0.9920 - val_loss: 0.9450 - val_acc: 0.8716
Epoch 285/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0188 - acc: 0.9932 - val_loss: 1.1255 - val_acc: 0.8670
Epoch 286/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0157 - acc: 0.9944 - val_loss: 1.1089 - val_acc: 0.8761
Epoch 287/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0302 - acc: 0.9898 - val_loss: 0.9962 - val_acc: 0.8578
Epoch 288/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0224 - acc: 0.9920 - val_loss: 1.0378 - val_acc: 0.8761
Epoch 289/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0193 - acc: 0.9935 - val_loss: 0.9975 - val_acc: 0.8670
Epoch 290/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0199 - acc: 0.9923 - val_loss: 0.9330 - val_acc: 0.8899
Epoch 291/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0261 - acc: 0.9909 - val_loss: 0.9373 - val_acc: 0.8761
Epoch 292/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0298 - acc: 0.9894 - val_loss: 0.9084 - val_acc: 0.8899
Epoch 293/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0284 - acc: 0.9908 - val_loss: 1.0262 - val_acc: 0.8761
Epoch 294/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0264 - acc: 0.9917 - val_loss: 1.0266 - val_acc: 0.8761
Epoch 295/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0218 - acc: 0.9920 - val_loss: 0.8664 - val_acc: 0.8761
Epoch 296/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0235 - acc: 0.9908 - val_loss: 0.9950 - val_acc: 0.8945
Epoch 297/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0353 - acc: 0.9897 - val_loss: 0.8590 - val_acc: 0.8853
Epoch 298/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0350 - acc: 0.9908 - val_loss: 0.9297 - val_acc: 0.8624
Epoch 299/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0263 - acc: 0.9912 - val_loss: 0.8815 - val_acc: 0.8945
Epoch 300/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0283 - acc: 0.9911 - val_loss: 0.9500 - val_acc: 0.8761
Epoch 301/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0300 - acc: 0.9900 - val_loss: 0.9754 - val_acc: 0.8899
Epoch 302/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0319 - acc: 0.9875 - val_loss: 1.2514 - val_acc: 0.8578
Epoch 303/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0218 - acc: 0.9912 - val_loss: 1.0164 - val_acc: 0.8899
Epoch 304/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0333 - acc: 0.9888 - val_loss: 0.8426 - val_acc: 0.8853
Epoch 305/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0278 - acc: 0.9909 - val_loss: 1.2031 - val_acc: 0.8807
Epoch 306/500
132/132 [==============================] - 33s 250ms/step - loss: 0.0357 - acc: 0.9912 - val_loss: 0.7954 - val_acc: 0.8899
Epoch 307/500
132/132 [==============================] - 34s 256ms/step - loss: 0.0213 - acc: 0.9923 - val_loss: 0.8547 - val_acc: 0.8899
Epoch 308/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0205 - acc: 0.9926 - val_loss: 1.0941 - val_acc: 0.8807
Epoch 309/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0179 - acc: 0.9932 - val_loss: 0.9141 - val_acc: 0.8761
Epoch 310/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0180 - acc: 0.9941 - val_loss: 1.0561 - val_acc: 0.8670
Epoch 311/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0190 - acc: 0.9924 - val_loss: 0.8290 - val_acc: 0.8899
Epoch 312/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0419 - acc: 0.9885 - val_loss: 1.0086 - val_acc: 0.8578
Epoch 313/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0228 - acc: 0.9917 - val_loss: 1.0089 - val_acc: 0.8670
Epoch 314/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0168 - acc: 0.9933 - val_loss: 0.8843 - val_acc: 0.8807
Epoch 315/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0231 - acc: 0.9926 - val_loss: 1.0553 - val_acc: 0.8945
Epoch 316/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0549 - acc: 0.9836 - val_loss: 1.0491 - val_acc: 0.8807
Epoch 317/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0308 - acc: 0.9892 - val_loss: 0.8465 - val_acc: 0.8945
Epoch 318/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0153 - acc: 0.9947 - val_loss: 0.9692 - val_acc: 0.8991
Epoch 319/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0194 - acc: 0.9936 - val_loss: 0.9146 - val_acc: 0.8761
Epoch 320/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0333 - acc: 0.9902 - val_loss: 0.9694 - val_acc: 0.8945
Epoch 321/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0280 - acc: 0.9891 - val_loss: 0.9155 - val_acc: 0.8624
Epoch 322/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0273 - acc: 0.9905 - val_loss: 0.9646 - val_acc: 0.8670
Epoch 323/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0179 - acc: 0.9929 - val_loss: 1.0179 - val_acc: 0.8761
Epoch 324/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0177 - acc: 0.9919 - val_loss: 1.0455 - val_acc: 0.8991
Epoch 325/500
132/132 [==============================] - 27s 203ms/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.9941 - val_acc: 0.8991
Epoch 326/500
132/132 [==============================] - 30s 228ms/step - loss: 0.0198 - acc: 0.9918 - val_loss: 1.1271 - val_acc: 0.8991
Epoch 327/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0196 - acc: 0.9921 - val_loss: 1.0975 - val_acc: 0.8761
Epoch 328/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0234 - acc: 0.9923 - val_loss: 1.0200 - val_acc: 0.8716
Epoch 329/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 1.0603 - val_acc: 0.8624
Epoch 330/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0380 - acc: 0.9895 - val_loss: 1.1811 - val_acc: 0.8624
Epoch 331/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0194 - acc: 0.9924 - val_loss: 0.9750 - val_acc: 0.8807
Epoch 332/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0347 - acc: 0.9898 - val_loss: 1.0690 - val_acc: 0.8761
Epoch 333/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0310 - acc: 0.9911 - val_loss: 1.0873 - val_acc: 0.8807
Epoch 334/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 0.7814 - val_acc: 0.8945
Epoch 335/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0196 - acc: 0.9930 - val_loss: 1.0076 - val_acc: 0.8807
Epoch 336/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0150 - acc: 0.9961 - val_loss: 1.0105 - val_acc: 0.8761
Epoch 337/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0207 - acc: 0.9938 - val_loss: 1.0291 - val_acc: 0.8761
Epoch 338/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0231 - acc: 0.9915 - val_loss: 1.1148 - val_acc: 0.8670
Epoch 339/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0217 - acc: 0.9926 - val_loss: 1.2244 - val_acc: 0.8761
Epoch 340/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0206 - acc: 0.9927 - val_loss: 0.8372 - val_acc: 0.8899
Epoch 341/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0128 - acc: 0.9939 - val_loss: 0.8842 - val_acc: 0.8945
Epoch 342/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0256 - acc: 0.9911 - val_loss: 1.1189 - val_acc: 0.8716
Epoch 343/500
132/132 [==============================] - 31s 234ms/step - loss: 0.0332 - acc: 0.9889 - val_loss: 1.0874 - val_acc: 0.8716
Epoch 344/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0248 - acc: 0.9914 - val_loss: 1.0786 - val_acc: 0.8761
Epoch 345/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0222 - acc: 0.9912 - val_loss: 0.9893 - val_acc: 0.8807
Epoch 346/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0261 - acc: 0.9921 - val_loss: 0.9373 - val_acc: 0.8899
Epoch 347/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0216 - acc: 0.9926 - val_loss: 1.0944 - val_acc: 0.8578
Epoch 348/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0176 - acc: 0.9948 - val_loss: 0.9630 - val_acc: 0.8807
Epoch 349/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0134 - acc: 0.9950 - val_loss: 0.9812 - val_acc: 0.8853
Epoch 350/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0232 - acc: 0.9921 - val_loss: 1.0570 - val_acc: 0.8670
Epoch 351/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0231 - acc: 0.9932 - val_loss: 0.9043 - val_acc: 0.8945
Epoch 352/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0205 - acc: 0.9927 - val_loss: 1.0411 - val_acc: 0.8670
Epoch 353/500
132/132 [==============================] - 34s 257ms/step - loss: 0.0183 - acc: 0.9939 - val_loss: 1.1187 - val_acc: 0.8578
Epoch 354/500
132/132 [==============================] - 33s 246ms/step - loss: 0.0205 - acc: 0.9941 - val_loss: 1.0180 - val_acc: 0.8624
Epoch 355/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0338 - acc: 0.9895 - val_loss: 0.9698 - val_acc: 0.8716
Epoch 356/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0200 - acc: 0.9922 - val_loss: 0.9339 - val_acc: 0.8853
Epoch 357/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0145 - acc: 0.9942 - val_loss: 0.9763 - val_acc: 0.8899
Epoch 358/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0155 - acc: 0.9952 - val_loss: 0.9112 - val_acc: 0.8945
Epoch 359/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0225 - acc: 0.9927 - val_loss: 0.7811 - val_acc: 0.9083
Epoch 360/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0587 - acc: 0.9826 - val_loss: 1.2150 - val_acc: 0.8624
Epoch 361/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0333 - acc: 0.9891 - val_loss: 1.1147 - val_acc: 0.8761
Epoch 362/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0237 - acc: 0.9920 - val_loss: 0.8950 - val_acc: 0.8807
Epoch 363/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0270 - acc: 0.9926 - val_loss: 1.0317 - val_acc: 0.8899
Epoch 364/500
132/132 [==============================] - 31s 236ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 0.8438 - val_acc: 0.8899
Epoch 365/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0189 - acc: 0.9941 - val_loss: 0.8527 - val_acc: 0.8899
Epoch 366/500
132/132 [==============================] - 34s 261ms/step - loss: 0.0198 - acc: 0.9933 - val_loss: 1.0543 - val_acc: 0.8853
Epoch 367/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0158 - acc: 0.9944 - val_loss: 1.0352 - val_acc: 0.8807
Epoch 368/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0212 - acc: 0.9920 - val_loss: 1.0866 - val_acc: 0.8761
Epoch 369/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0391 - acc: 0.9861 - val_loss: 1.0616 - val_acc: 0.8716
Epoch 370/500
132/132 [==============================] - 31s 237ms/step - loss: 0.0301 - acc: 0.9898 - val_loss: 1.1332 - val_acc: 0.8349
Epoch 371/500
132/132 [==============================] - 31s 235ms/step - loss: 0.0334 - acc: 0.9908 - val_loss: 1.0374 - val_acc: 0.8716
Epoch 372/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0218 - acc: 0.9932 - val_loss: 1.2855 - val_acc: 0.8670
Epoch 373/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0228 - acc: 0.9921 - val_loss: 1.2579 - val_acc: 0.8624
Epoch 374/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0283 - acc: 0.9906 - val_loss: 1.1278 - val_acc: 0.8624
Epoch 375/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0184 - acc: 0.9938 - val_loss: 1.2217 - val_acc: 0.8716
Epoch 376/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0176 - acc: 0.9953 - val_loss: 1.0948 - val_acc: 0.8761
Epoch 377/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0115 - acc: 0.9948 - val_loss: 1.2377 - val_acc: 0.8670
Epoch 378/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0198 - acc: 0.9924 - val_loss: 1.0906 - val_acc: 0.8807
Epoch 379/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0199 - acc: 0.9923 - val_loss: 1.1558 - val_acc: 0.8807
Epoch 380/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0190 - acc: 0.9943 - val_loss: 1.2211 - val_acc: 0.8899
Epoch 381/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0264 - acc: 0.9905 - val_loss: 1.1717 - val_acc: 0.8670
Epoch 382/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0185 - acc: 0.9935 - val_loss: 1.1802 - val_acc: 0.8761
Epoch 383/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0184 - acc: 0.9939 - val_loss: 1.1194 - val_acc: 0.8624
Epoch 384/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0149 - acc: 0.9953 - val_loss: 0.9930 - val_acc: 0.8761
Epoch 385/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0211 - acc: 0.9929 - val_loss: 1.2870 - val_acc: 0.8532
Epoch 386/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0185 - acc: 0.9933 - val_loss: 1.2167 - val_acc: 0.8670
Epoch 387/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0241 - acc: 0.9917 - val_loss: 1.0663 - val_acc: 0.8716
Epoch 388/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0222 - acc: 0.9917 - val_loss: 1.0245 - val_acc: 0.8761
Epoch 389/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0174 - acc: 0.9947 - val_loss: 1.1158 - val_acc: 0.8761
Epoch 390/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0233 - acc: 0.9917 - val_loss: 0.9645 - val_acc: 0.8807
Epoch 391/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0175 - acc: 0.9936 - val_loss: 1.1208 - val_acc: 0.8624
Epoch 392/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0289 - acc: 0.9909 - val_loss: 1.1149 - val_acc: 0.8899
Epoch 393/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0203 - acc: 0.9933 - val_loss: 1.2493 - val_acc: 0.8761
Epoch 394/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0128 - acc: 0.9947 - val_loss: 1.1662 - val_acc: 0.8991
Epoch 395/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0389 - acc: 0.9889 - val_loss: 1.0892 - val_acc: 0.8853
Epoch 396/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0236 - acc: 0.9926 - val_loss: 0.8824 - val_acc: 0.8807
Epoch 397/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0216 - acc: 0.9921 - val_loss: 1.0723 - val_acc: 0.8761
Epoch 398/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0208 - acc: 0.9927 - val_loss: 1.1333 - val_acc: 0.8853
Epoch 399/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0298 - acc: 0.9918 - val_loss: 0.9555 - val_acc: 0.8761
Epoch 400/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0208 - acc: 0.9936 - val_loss: 1.2371 - val_acc: 0.8761
Epoch 401/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0346 - acc: 0.9915 - val_loss: 1.0581 - val_acc: 0.8899
Epoch 402/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0240 - acc: 0.9926 - val_loss: 0.9754 - val_acc: 0.8899
Epoch 403/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0174 - acc: 0.9944 - val_loss: 0.8866 - val_acc: 0.8853
Epoch 404/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0147 - acc: 0.9939 - val_loss: 0.8984 - val_acc: 0.8807
Epoch 405/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0139 - acc: 0.9956 - val_loss: 0.9012 - val_acc: 0.8899
Epoch 406/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0179 - acc: 0.9941 - val_loss: 1.0435 - val_acc: 0.8991
Epoch 407/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0183 - acc: 0.9936 - val_loss: 1.1155 - val_acc: 0.8761
Epoch 408/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0191 - acc: 0.9933 - val_loss: 0.9830 - val_acc: 0.8945
Epoch 409/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0181 - acc: 0.9939 - val_loss: 1.0213 - val_acc: 0.8899
Epoch 410/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0243 - acc: 0.9930 - val_loss: 1.0728 - val_acc: 0.8532
Epoch 411/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0118 - acc: 0.9967 - val_loss: 1.0987 - val_acc: 0.8807
Epoch 412/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0241 - acc: 0.9935 - val_loss: 1.1924 - val_acc: 0.8670
Epoch 413/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0276 - acc: 0.9926 - val_loss: 1.3105 - val_acc: 0.8670
Epoch 414/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0309 - acc: 0.9894 - val_loss: 1.0818 - val_acc: 0.8853
Epoch 415/500
132/132 [==============================] - 33s 249ms/step - loss: 0.0160 - acc: 0.9939 - val_loss: 0.9284 - val_acc: 0.8991
Epoch 416/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0255 - acc: 0.9923 - val_loss: 1.0982 - val_acc: 0.8624
Epoch 417/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0400 - acc: 0.9888 - val_loss: 0.8111 - val_acc: 0.8991
Epoch 418/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0164 - acc: 0.9944 - val_loss: 0.9451 - val_acc: 0.8991
Epoch 419/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0290 - acc: 0.9898 - val_loss: 0.8581 - val_acc: 0.8899
Epoch 420/500
132/132 [==============================] - 32s 245ms/step - loss: 0.0163 - acc: 0.9941 - val_loss: 0.8713 - val_acc: 0.8761
Epoch 421/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0196 - acc: 0.9942 - val_loss: 0.8478 - val_acc: 0.8991
Epoch 422/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0248 - acc: 0.9934 - val_loss: 0.9492 - val_acc: 0.8670
Epoch 423/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0426 - acc: 0.9880 - val_loss: 1.0524 - val_acc: 0.8807
Epoch 424/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0162 - acc: 0.9948 - val_loss: 1.1056 - val_acc: 0.8899
Epoch 425/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0160 - acc: 0.9941 - val_loss: 1.0454 - val_acc: 0.8807
Epoch 426/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0199 - acc: 0.9932 - val_loss: 0.9025 - val_acc: 0.8945
Epoch 427/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0105 - acc: 0.9950 - val_loss: 1.0686 - val_acc: 0.8945
Epoch 428/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0229 - acc: 0.9915 - val_loss: 1.3303 - val_acc: 0.8578
Epoch 429/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0145 - acc: 0.9942 - val_loss: 1.0347 - val_acc: 0.9037
Epoch 430/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0183 - acc: 0.9938 - val_loss: 0.9372 - val_acc: 0.8853
Epoch 431/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0364 - acc: 0.9894 - val_loss: 1.0185 - val_acc: 0.8761
Epoch 432/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0214 - acc: 0.9926 - val_loss: 1.0016 - val_acc: 0.8853
Epoch 433/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0203 - acc: 0.9936 - val_loss: 1.0949 - val_acc: 0.8670
Epoch 434/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0186 - acc: 0.9939 - val_loss: 1.0074 - val_acc: 0.8853
Epoch 435/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0212 - acc: 0.9930 - val_loss: 1.2531 - val_acc: 0.8670
Epoch 436/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0174 - acc: 0.9935 - val_loss: 1.0486 - val_acc: 0.8716
Epoch 437/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0116 - acc: 0.9956 - val_loss: 1.2374 - val_acc: 0.8716
Epoch 438/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0165 - acc: 0.9944 - val_loss: 1.0898 - val_acc: 0.8853
Epoch 439/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0137 - acc: 0.9958 - val_loss: 1.1949 - val_acc: 0.8624
Epoch 440/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0170 - acc: 0.9939 - val_loss: 1.1211 - val_acc: 0.8578
Epoch 441/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0181 - acc: 0.9950 - val_loss: 1.1143 - val_acc: 0.8716
Epoch 442/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0188 - acc: 0.9947 - val_loss: 1.3011 - val_acc: 0.8716
Epoch 443/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0143 - acc: 0.9958 - val_loss: 1.0542 - val_acc: 0.8761
Epoch 444/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0298 - acc: 0.9906 - val_loss: 1.1088 - val_acc: 0.8807
Epoch 445/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0230 - acc: 0.9930 - val_loss: 1.0888 - val_acc: 0.8761
Epoch 446/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0181 - acc: 0.9933 - val_loss: 1.2397 - val_acc: 0.8624
Epoch 447/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0223 - acc: 0.9932 - val_loss: 1.2040 - val_acc: 0.8853
Epoch 448/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0281 - acc: 0.9917 - val_loss: 1.0547 - val_acc: 0.8807
Epoch 449/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0286 - acc: 0.9911 - val_loss: 1.0973 - val_acc: 0.8761
Epoch 450/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0206 - acc: 0.9933 - val_loss: 1.0549 - val_acc: 0.8853
Epoch 451/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0388 - acc: 0.9891 - val_loss: 0.9950 - val_acc: 0.8807
Epoch 452/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0227 - acc: 0.9926 - val_loss: 1.1298 - val_acc: 0.8853
Epoch 453/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0134 - acc: 0.9956 - val_loss: 0.9993 - val_acc: 0.8853
Epoch 454/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0159 - acc: 0.9947 - val_loss: 1.2146 - val_acc: 0.8761
Epoch 455/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0149 - acc: 0.9944 - val_loss: 1.2575 - val_acc: 0.8624
Epoch 456/500
132/132 [==============================] - 31s 238ms/step - loss: 0.0155 - acc: 0.9952 - val_loss: 1.1870 - val_acc: 0.8578
Epoch 457/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0165 - acc: 0.9942 - val_loss: 1.2153 - val_acc: 0.8670
Epoch 458/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0285 - acc: 0.9923 - val_loss: 1.0371 - val_acc: 0.8991
Epoch 459/500
132/132 [==============================] - 32s 244ms/step - loss: 0.0232 - acc: 0.9926 - val_loss: 1.2726 - val_acc: 0.8853
Epoch 460/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0154 - acc: 0.9942 - val_loss: 1.1131 - val_acc: 0.8991
Epoch 461/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0201 - acc: 0.9926 - val_loss: 1.2989 - val_acc: 0.8624
Epoch 462/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0182 - acc: 0.9942 - val_loss: 1.2037 - val_acc: 0.8670
Epoch 463/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0346 - acc: 0.9902 - val_loss: 1.1694 - val_acc: 0.8761
Epoch 464/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0288 - acc: 0.9921 - val_loss: 1.0682 - val_acc: 0.8853
Epoch 465/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0267 - acc: 0.9911 - val_loss: 1.1194 - val_acc: 0.8624
Epoch 466/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0175 - acc: 0.9926 - val_loss: 1.1155 - val_acc: 0.8807
Epoch 467/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0178 - acc: 0.9939 - val_loss: 1.3111 - val_acc: 0.8578
Epoch 468/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0150 - acc: 0.9952 - val_loss: 1.2845 - val_acc: 0.8624
Epoch 469/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0117 - acc: 0.9950 - val_loss: 1.3131 - val_acc: 0.8670
Epoch 470/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0121 - acc: 0.9964 - val_loss: 1.3009 - val_acc: 0.8670
Epoch 471/500
132/132 [==============================] - 32s 241ms/step - loss: 0.0184 - acc: 0.9945 - val_loss: 1.1885 - val_acc: 0.8807
Epoch 472/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0157 - acc: 0.9950 - val_loss: 1.2235 - val_acc: 0.8716
Epoch 473/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0236 - acc: 0.9924 - val_loss: 1.3560 - val_acc: 0.8670
Epoch 474/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0242 - acc: 0.9934 - val_loss: 1.3435 - val_acc: 0.8532
Epoch 475/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0409 - acc: 0.9879 - val_loss: 1.3228 - val_acc: 0.8670
Epoch 476/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0238 - acc: 0.9936 - val_loss: 1.4163 - val_acc: 0.8486
Epoch 477/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0181 - acc: 0.9948 - val_loss: 1.1392 - val_acc: 0.8807
Epoch 478/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0157 - acc: 0.9947 - val_loss: 1.0756 - val_acc: 0.8807
Epoch 479/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0127 - acc: 0.9947 - val_loss: 1.1019 - val_acc: 0.8716
Epoch 480/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0191 - acc: 0.9926 - val_loss: 0.9561 - val_acc: 0.8991
Epoch 481/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0141 - acc: 0.9942 - val_loss: 1.1202 - val_acc: 0.8899
Epoch 482/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0188 - acc: 0.9935 - val_loss: 1.1515 - val_acc: 0.8899
Epoch 483/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0125 - acc: 0.9947 - val_loss: 1.1955 - val_acc: 0.8807
Epoch 484/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0247 - acc: 0.9932 - val_loss: 1.2016 - val_acc: 0.8578
Epoch 485/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0252 - acc: 0.9935 - val_loss: 1.2017 - val_acc: 0.8716
Epoch 486/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0149 - acc: 0.9944 - val_loss: 1.1620 - val_acc: 0.8761
Epoch 487/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0108 - acc: 0.9956 - val_loss: 1.0615 - val_acc: 0.8716
Epoch 488/500
132/132 [==============================] - 31s 239ms/step - loss: 0.0176 - acc: 0.9935 - val_loss: 1.0743 - val_acc: 0.8945
Epoch 489/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0316 - acc: 0.9908 - val_loss: 1.0359 - val_acc: 0.8716
Epoch 490/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0303 - acc: 0.9926 - val_loss: 1.1538 - val_acc: 0.8624
Epoch 491/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0225 - acc: 0.9927 - val_loss: 1.0616 - val_acc: 0.8761
Epoch 492/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0100 - acc: 0.9953 - val_loss: 1.0632 - val_acc: 0.8807
Epoch 493/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0111 - acc: 0.9958 - val_loss: 1.1383 - val_acc: 0.8807
Epoch 494/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0150 - acc: 0.9950 - val_loss: 1.0115 - val_acc: 0.8761
Epoch 495/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0124 - acc: 0.9962 - val_loss: 1.0834 - val_acc: 0.8853
Epoch 496/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0081 - acc: 0.9967 - val_loss: 1.0289 - val_acc: 0.8761
Epoch 497/500
132/132 [==============================] - 32s 239ms/step - loss: 0.0157 - acc: 0.9939 - val_loss: 1.2737 - val_acc: 0.8624
Epoch 498/500
132/132 [==============================] - 32s 243ms/step - loss: 0.0163 - acc: 0.9950 - val_loss: 1.2649 - val_acc: 0.8670
Epoch 499/500
132/132 [==============================] - 32s 242ms/step - loss: 0.0194 - acc: 0.9947 - val_loss: 1.3581 - val_acc: 0.8578
Epoch 500/500
132/132 [==============================] - 32s 240ms/step - loss: 0.0200 - acc: 0.9932 - val_loss: 0.9841 - val_acc: 0.8624
40/40 [==============================] - 25s 617ms/step
Accuracy: 0.8699847638395124. F1: 0.8686719230910561
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<div class="prompt input_prompt">In&nbsp;[29]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">make_model1D</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;1D_norm&quot;</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 78s 588ms/step - loss: 6.0036 - acc: 0.2390 - val_loss: 1.4845 - val_acc: 0.4174
Epoch 2/500
132/132 [==============================] - 81s 615ms/step - loss: 1.6086 - acc: 0.3918 - val_loss: 1.3040 - val_acc: 0.4358
Epoch 3/500
132/132 [==============================] - 81s 613ms/step - loss: 1.4592 - acc: 0.4322 - val_loss: 1.2189 - val_acc: 0.4862
Epoch 4/500
132/132 [==============================] - 77s 586ms/step - loss: 1.3583 - acc: 0.4678 - val_loss: 1.1723 - val_acc: 0.5505
Epoch 5/500
132/132 [==============================] - 77s 587ms/step - loss: 1.3060 - acc: 0.5016 - val_loss: 1.1102 - val_acc: 0.5321
Epoch 6/500
132/132 [==============================] - 78s 593ms/step - loss: 1.2664 - acc: 0.5153 - val_loss: 1.0963 - val_acc: 0.5550
Epoch 7/500
132/132 [==============================] - 77s 585ms/step - loss: 1.2230 - acc: 0.5340 - val_loss: 1.0858 - val_acc: 0.5642
Epoch 8/500
132/132 [==============================] - 78s 590ms/step - loss: 1.1956 - acc: 0.5438 - val_loss: 1.0254 - val_acc: 0.5917
Epoch 9/500
132/132 [==============================] - 77s 586ms/step - loss: 1.1570 - acc: 0.5583 - val_loss: 0.9900 - val_acc: 0.5826
Epoch 10/500
132/132 [==============================] - 78s 593ms/step - loss: 1.1200 - acc: 0.5718 - val_loss: 1.0314 - val_acc: 0.5780
Epoch 11/500
132/132 [==============================] - 77s 587ms/step - loss: 1.0909 - acc: 0.5800 - val_loss: 0.9421 - val_acc: 0.6193
Epoch 12/500
132/132 [==============================] - 77s 585ms/step - loss: 1.0663 - acc: 0.5897 - val_loss: 0.9287 - val_acc: 0.6147
Epoch 13/500
132/132 [==============================] - 77s 586ms/step - loss: 1.0600 - acc: 0.5934 - val_loss: 0.9378 - val_acc: 0.6055
Epoch 14/500
132/132 [==============================] - 78s 593ms/step - loss: 1.0259 - acc: 0.6031 - val_loss: 0.9125 - val_acc: 0.6009
Epoch 15/500
132/132 [==============================] - 78s 588ms/step - loss: 1.0014 - acc: 0.6108 - val_loss: 0.8788 - val_acc: 0.6193
Epoch 16/500
132/132 [==============================] - 78s 588ms/step - loss: 0.9867 - acc: 0.6213 - val_loss: 0.8769 - val_acc: 0.6284
Epoch 17/500
132/132 [==============================] - 77s 586ms/step - loss: 0.9460 - acc: 0.6291 - val_loss: 0.8581 - val_acc: 0.6422
Epoch 18/500
132/132 [==============================] - 77s 586ms/step - loss: 0.9339 - acc: 0.6306 - val_loss: 0.8733 - val_acc: 0.6651
Epoch 19/500
132/132 [==============================] - 78s 587ms/step - loss: 0.9235 - acc: 0.6402 - val_loss: 0.8364 - val_acc: 0.6330
Epoch 20/500
132/132 [==============================] - 77s 584ms/step - loss: 0.8993 - acc: 0.6533 - val_loss: 0.8456 - val_acc: 0.6422
Epoch 21/500
132/132 [==============================] - 78s 591ms/step - loss: 0.8910 - acc: 0.6572 - val_loss: 0.8342 - val_acc: 0.6376
Epoch 22/500
132/132 [==============================] - 78s 594ms/step - loss: 0.8487 - acc: 0.6692 - val_loss: 0.7923 - val_acc: 0.6835
Epoch 23/500
132/132 [==============================] - 78s 591ms/step - loss: 0.8559 - acc: 0.6644 - val_loss: 0.7832 - val_acc: 0.7064
Epoch 24/500
132/132 [==============================] - 78s 591ms/step - loss: 0.8214 - acc: 0.6803 - val_loss: 0.7269 - val_acc: 0.7110
Epoch 25/500
132/132 [==============================] - 77s 586ms/step - loss: 0.8231 - acc: 0.6883 - val_loss: 0.8098 - val_acc: 0.6697
Epoch 26/500
132/132 [==============================] - 79s 602ms/step - loss: 0.8132 - acc: 0.6851 - val_loss: 0.7685 - val_acc: 0.7156
Epoch 27/500
132/132 [==============================] - 78s 587ms/step - loss: 0.7811 - acc: 0.6966 - val_loss: 0.7503 - val_acc: 0.6972
Epoch 28/500
132/132 [==============================] - 81s 616ms/step - loss: 0.7656 - acc: 0.7033 - val_loss: 0.7821 - val_acc: 0.6835
Epoch 29/500
132/132 [==============================] - 84s 637ms/step - loss: 0.7549 - acc: 0.7116 - val_loss: 0.7446 - val_acc: 0.7156
Epoch 30/500
132/132 [==============================] - 86s 651ms/step - loss: 0.7594 - acc: 0.7082 - val_loss: 0.7459 - val_acc: 0.7064
Epoch 31/500
132/132 [==============================] - 79s 596ms/step - loss: 0.7431 - acc: 0.7137 - val_loss: 0.7422 - val_acc: 0.7064
Epoch 32/500
132/132 [==============================] - 78s 593ms/step - loss: 0.7310 - acc: 0.7233 - val_loss: 0.7370 - val_acc: 0.7294
Epoch 33/500
132/132 [==============================] - 79s 598ms/step - loss: 0.7085 - acc: 0.7310 - val_loss: 0.7276 - val_acc: 0.7202
Epoch 34/500
132/132 [==============================] - 77s 586ms/step - loss: 0.6985 - acc: 0.7274 - val_loss: 0.7085 - val_acc: 0.7294
Epoch 35/500
132/132 [==============================] - 80s 610ms/step - loss: 0.6949 - acc: 0.7325 - val_loss: 0.7032 - val_acc: 0.7248
Epoch 36/500
132/132 [==============================] - 77s 585ms/step - loss: 0.6744 - acc: 0.7379 - val_loss: 0.6937 - val_acc: 0.7523
Epoch 37/500
132/132 [==============================] - 77s 583ms/step - loss: 0.6691 - acc: 0.7382 - val_loss: 0.6926 - val_acc: 0.7202
Epoch 38/500
132/132 [==============================] - 78s 593ms/step - loss: 0.6466 - acc: 0.7499 - val_loss: 0.7123 - val_acc: 0.7202
Epoch 39/500
132/132 [==============================] - 78s 588ms/step - loss: 0.6359 - acc: 0.7488 - val_loss: 0.6796 - val_acc: 0.7431
Epoch 40/500
132/132 [==============================] - 79s 598ms/step - loss: 0.6499 - acc: 0.7536 - val_loss: 0.6606 - val_acc: 0.7477
Epoch 41/500
132/132 [==============================] - 80s 602ms/step - loss: 0.6095 - acc: 0.7604 - val_loss: 0.6829 - val_acc: 0.7294
Epoch 42/500
132/132 [==============================] - 88s 670ms/step - loss: 0.6140 - acc: 0.7619 - val_loss: 0.6523 - val_acc: 0.7615
Epoch 43/500
132/132 [==============================] - 86s 650ms/step - loss: 0.6059 - acc: 0.7697 - val_loss: 0.6886 - val_acc: 0.7110
Epoch 44/500
132/132 [==============================] - 90s 680ms/step - loss: 0.5905 - acc: 0.7721 - val_loss: 0.6324 - val_acc: 0.7477
Epoch 45/500
132/132 [==============================] - 86s 651ms/step - loss: 0.5745 - acc: 0.7751 - val_loss: 0.6524 - val_acc: 0.7385
Epoch 46/500
132/132 [==============================] - 82s 618ms/step - loss: 0.5767 - acc: 0.7765 - val_loss: 0.6372 - val_acc: 0.7615
Epoch 47/500
132/132 [==============================] - 83s 632ms/step - loss: 0.5613 - acc: 0.7803 - val_loss: 0.6246 - val_acc: 0.7615
Epoch 48/500
132/132 [==============================] - 86s 648ms/step - loss: 0.5555 - acc: 0.7859 - val_loss: 0.6321 - val_acc: 0.7477
Epoch 49/500
132/132 [==============================] - 84s 636ms/step - loss: 0.5420 - acc: 0.7838 - val_loss: 0.5992 - val_acc: 0.7569
Epoch 50/500
132/132 [==============================] - 84s 637ms/step - loss: 0.5334 - acc: 0.7879 - val_loss: 0.5895 - val_acc: 0.7706
Epoch 51/500
132/132 [==============================] - 84s 638ms/step - loss: 0.5320 - acc: 0.7945 - val_loss: 0.6125 - val_acc: 0.7431
Epoch 52/500
132/132 [==============================] - 79s 598ms/step - loss: 0.5442 - acc: 0.7846 - val_loss: 0.5849 - val_acc: 0.7844
Epoch 53/500
132/132 [==============================] - 80s 607ms/step - loss: 0.5135 - acc: 0.8038 - val_loss: 0.5953 - val_acc: 0.7706
Epoch 54/500
132/132 [==============================] - 81s 612ms/step - loss: 0.5126 - acc: 0.7983 - val_loss: 0.6243 - val_acc: 0.7706
Epoch 55/500
132/132 [==============================] - 81s 614ms/step - loss: 0.5065 - acc: 0.8010 - val_loss: 0.5983 - val_acc: 0.7706
Epoch 56/500
132/132 [==============================] - 80s 608ms/step - loss: 0.4947 - acc: 0.8066 - val_loss: 0.5824 - val_acc: 0.7798
Epoch 57/500
132/132 [==============================] - 78s 587ms/step - loss: 0.4878 - acc: 0.8106 - val_loss: 0.6322 - val_acc: 0.7615
Epoch 58/500
132/132 [==============================] - 78s 590ms/step - loss: 0.4750 - acc: 0.8145 - val_loss: 0.6227 - val_acc: 0.7431
Epoch 59/500
132/132 [==============================] - 78s 589ms/step - loss: 0.4760 - acc: 0.8134 - val_loss: 0.6122 - val_acc: 0.7706
Epoch 60/500
132/132 [==============================] - 80s 605ms/step - loss: 0.4733 - acc: 0.8142 - val_loss: 0.6077 - val_acc: 0.7752
Epoch 61/500
132/132 [==============================] - 79s 598ms/step - loss: 0.4682 - acc: 0.8164 - val_loss: 0.5651 - val_acc: 0.7936
Epoch 62/500
132/132 [==============================] - 84s 638ms/step - loss: 0.4574 - acc: 0.8252 - val_loss: 0.5637 - val_acc: 0.7890
Epoch 63/500
132/132 [==============================] - 83s 631ms/step - loss: 0.4519 - acc: 0.8218 - val_loss: 0.5568 - val_acc: 0.8119
Epoch 64/500
132/132 [==============================] - 85s 642ms/step - loss: 0.4478 - acc: 0.8270 - val_loss: 0.5784 - val_acc: 0.7752
Epoch 65/500
132/132 [==============================] - 83s 629ms/step - loss: 0.4409 - acc: 0.8288 - val_loss: 0.5776 - val_acc: 0.8028
Epoch 66/500
132/132 [==============================] - 83s 633ms/step - loss: 0.4220 - acc: 0.8357 - val_loss: 0.5574 - val_acc: 0.8119
Epoch 67/500
132/132 [==============================] - 81s 612ms/step - loss: 0.4231 - acc: 0.8355 - val_loss: 0.5582 - val_acc: 0.7890
Epoch 68/500
132/132 [==============================] - 82s 624ms/step - loss: 0.4127 - acc: 0.8453 - val_loss: 0.6107 - val_acc: 0.8028
Epoch 69/500
132/132 [==============================] - 80s 605ms/step - loss: 0.3964 - acc: 0.8436 - val_loss: 0.5481 - val_acc: 0.7890
Epoch 70/500
132/132 [==============================] - 80s 605ms/step - loss: 0.3878 - acc: 0.8476 - val_loss: 0.5521 - val_acc: 0.8211
Epoch 71/500
132/132 [==============================] - 78s 588ms/step - loss: 0.3990 - acc: 0.8485 - val_loss: 0.5274 - val_acc: 0.7844
Epoch 72/500
132/132 [==============================] - 83s 628ms/step - loss: 0.3788 - acc: 0.8521 - val_loss: 0.5415 - val_acc: 0.8119
Epoch 73/500
132/132 [==============================] - 80s 605ms/step - loss: 0.3794 - acc: 0.8550 - val_loss: 0.5446 - val_acc: 0.7936
Epoch 74/500
132/132 [==============================] - 85s 645ms/step - loss: 0.3797 - acc: 0.8544 - val_loss: 0.6159 - val_acc: 0.7706
Epoch 75/500
132/132 [==============================] - 79s 601ms/step - loss: 0.3708 - acc: 0.8563 - val_loss: 0.5525 - val_acc: 0.7936
Epoch 76/500
132/132 [==============================] - 80s 610ms/step - loss: 0.3703 - acc: 0.8561 - val_loss: 0.5214 - val_acc: 0.8119
Epoch 77/500
132/132 [==============================] - 79s 596ms/step - loss: 0.3642 - acc: 0.8571 - val_loss: 0.5120 - val_acc: 0.8119
Epoch 78/500
132/132 [==============================] - 78s 592ms/step - loss: 0.3649 - acc: 0.8633 - val_loss: 0.5500 - val_acc: 0.7706
Epoch 79/500
132/132 [==============================] - 82s 621ms/step - loss: 0.3569 - acc: 0.8624 - val_loss: 0.5304 - val_acc: 0.7936
Epoch 80/500
132/132 [==============================] - 79s 599ms/step - loss: 0.3503 - acc: 0.8678 - val_loss: 0.5389 - val_acc: 0.8165
Epoch 81/500
132/132 [==============================] - 82s 623ms/step - loss: 0.3415 - acc: 0.8712 - val_loss: 0.5261 - val_acc: 0.8028
Epoch 82/500
132/132 [==============================] - 77s 585ms/step - loss: 0.3370 - acc: 0.8731 - val_loss: 0.5226 - val_acc: 0.8119
Epoch 83/500
132/132 [==============================] - 81s 612ms/step - loss: 0.3420 - acc: 0.8692 - val_loss: 0.5198 - val_acc: 0.8119
Epoch 84/500
132/132 [==============================] - 81s 616ms/step - loss: 0.3363 - acc: 0.8727 - val_loss: 0.5882 - val_acc: 0.7752
Epoch 85/500
132/132 [==============================] - 83s 626ms/step - loss: 0.3249 - acc: 0.8745 - val_loss: 0.5191 - val_acc: 0.7890
Epoch 86/500
132/132 [==============================] - 85s 641ms/step - loss: 0.3252 - acc: 0.8782 - val_loss: 0.4667 - val_acc: 0.8394
Epoch 87/500
132/132 [==============================] - 86s 648ms/step - loss: 0.3110 - acc: 0.8821 - val_loss: 0.5298 - val_acc: 0.8073
Epoch 88/500
132/132 [==============================] - 84s 639ms/step - loss: 0.3242 - acc: 0.8745 - val_loss: 0.5433 - val_acc: 0.7982
Epoch 89/500
132/132 [==============================] - 86s 652ms/step - loss: 0.3098 - acc: 0.8808 - val_loss: 0.4955 - val_acc: 0.8257
Epoch 90/500
132/132 [==============================] - 84s 639ms/step - loss: 0.3282 - acc: 0.8701 - val_loss: 0.5021 - val_acc: 0.8028
Epoch 91/500
132/132 [==============================] - 82s 622ms/step - loss: 0.3072 - acc: 0.8824 - val_loss: 0.5289 - val_acc: 0.8073
Epoch 92/500
132/132 [==============================] - 78s 594ms/step - loss: 0.3053 - acc: 0.8842 - val_loss: 0.5059 - val_acc: 0.8119
Epoch 93/500
132/132 [==============================] - 78s 593ms/step - loss: 0.2935 - acc: 0.8865 - val_loss: 0.5210 - val_acc: 0.8211
Epoch 94/500
132/132 [==============================] - 79s 600ms/step - loss: 0.2919 - acc: 0.8861 - val_loss: 0.5127 - val_acc: 0.8486
Epoch 95/500
132/132 [==============================] - 85s 645ms/step - loss: 0.2958 - acc: 0.8850 - val_loss: 0.5444 - val_acc: 0.7890
Epoch 96/500
132/132 [==============================] - 82s 624ms/step - loss: 0.2872 - acc: 0.8889 - val_loss: 0.4957 - val_acc: 0.8257
Epoch 97/500
132/132 [==============================] - 82s 618ms/step - loss: 0.2819 - acc: 0.8926 - val_loss: 0.4980 - val_acc: 0.8211
Epoch 98/500
132/132 [==============================] - 79s 600ms/step - loss: 0.2807 - acc: 0.8893 - val_loss: 0.4935 - val_acc: 0.8486
Epoch 99/500
132/132 [==============================] - 79s 600ms/step - loss: 0.2795 - acc: 0.8915 - val_loss: 0.4927 - val_acc: 0.8119
Epoch 100/500
132/132 [==============================] - 78s 587ms/step - loss: 0.2646 - acc: 0.8976 - val_loss: 0.5159 - val_acc: 0.8394
Epoch 101/500
132/132 [==============================] - 79s 597ms/step - loss: 0.2777 - acc: 0.8971 - val_loss: 0.5327 - val_acc: 0.8165
Epoch 102/500
132/132 [==============================] - 79s 599ms/step - loss: 0.2636 - acc: 0.9011 - val_loss: 0.5059 - val_acc: 0.8394
Epoch 103/500
132/132 [==============================] - 82s 624ms/step - loss: 0.2725 - acc: 0.8998 - val_loss: 0.5304 - val_acc: 0.8440
Epoch 104/500
132/132 [==============================] - 81s 617ms/step - loss: 0.2548 - acc: 0.9048 - val_loss: 0.5337 - val_acc: 0.8303
Epoch 105/500
132/132 [==============================] - 83s 627ms/step - loss: 0.2560 - acc: 0.9051 - val_loss: 0.4798 - val_acc: 0.8349
Epoch 106/500
132/132 [==============================] - 81s 615ms/step - loss: 0.2510 - acc: 0.9012 - val_loss: 0.4839 - val_acc: 0.8486
Epoch 107/500
132/132 [==============================] - 82s 618ms/step - loss: 0.2588 - acc: 0.8976 - val_loss: 0.4840 - val_acc: 0.8349
Epoch 108/500
132/132 [==============================] - 81s 612ms/step - loss: 0.2437 - acc: 0.9106 - val_loss: 0.4942 - val_acc: 0.8257
Epoch 109/500
132/132 [==============================] - 81s 615ms/step - loss: 0.2548 - acc: 0.9039 - val_loss: 0.5219 - val_acc: 0.8394
Epoch 110/500
132/132 [==============================] - 82s 622ms/step - loss: 0.2377 - acc: 0.9088 - val_loss: 0.5436 - val_acc: 0.8165
Epoch 111/500
132/132 [==============================] - 81s 610ms/step - loss: 0.2488 - acc: 0.9047 - val_loss: 0.5066 - val_acc: 0.8303
Epoch 112/500
132/132 [==============================] - 80s 605ms/step - loss: 0.2391 - acc: 0.9068 - val_loss: 0.4938 - val_acc: 0.8394
Epoch 113/500
132/132 [==============================] - 78s 593ms/step - loss: 0.2348 - acc: 0.9082 - val_loss: 0.5184 - val_acc: 0.8440
Epoch 114/500
132/132 [==============================] - 82s 622ms/step - loss: 0.2278 - acc: 0.9133 - val_loss: 0.5188 - val_acc: 0.8257
Epoch 115/500
132/132 [==============================] - 79s 596ms/step - loss: 0.2329 - acc: 0.9106 - val_loss: 0.5229 - val_acc: 0.8119
Epoch 116/500
132/132 [==============================] - 79s 595ms/step - loss: 0.2315 - acc: 0.9121 - val_loss: 0.5026 - val_acc: 0.8349
Epoch 117/500
132/132 [==============================] - 79s 597ms/step - loss: 0.2299 - acc: 0.9171 - val_loss: 0.5353 - val_acc: 0.8119
Epoch 118/500
132/132 [==============================] - 85s 646ms/step - loss: 0.2283 - acc: 0.9135 - val_loss: 0.5711 - val_acc: 0.7890
Epoch 119/500
132/132 [==============================] - 83s 625ms/step - loss: 0.2337 - acc: 0.9115 - val_loss: 0.5033 - val_acc: 0.8349
Epoch 120/500
132/132 [==============================] - 84s 635ms/step - loss: 0.2096 - acc: 0.9189 - val_loss: 0.5194 - val_acc: 0.8211
Epoch 121/500
132/132 [==============================] - 83s 631ms/step - loss: 0.2161 - acc: 0.9175 - val_loss: 0.5080 - val_acc: 0.8257
Epoch 122/500
132/132 [==============================] - 84s 634ms/step - loss: 0.2165 - acc: 0.9161 - val_loss: 0.5291 - val_acc: 0.7936
Epoch 123/500
132/132 [==============================] - 90s 679ms/step - loss: 0.2179 - acc: 0.9170 - val_loss: 0.4927 - val_acc: 0.8394
Epoch 124/500
132/132 [==============================] - 85s 646ms/step - loss: 0.2047 - acc: 0.9208 - val_loss: 0.5225 - val_acc: 0.8119
Epoch 125/500
132/132 [==============================] - 86s 655ms/step - loss: 0.2131 - acc: 0.9201 - val_loss: 0.4964 - val_acc: 0.8165
Epoch 126/500
132/132 [==============================] - 90s 680ms/step - loss: 0.2072 - acc: 0.9201 - val_loss: 0.5043 - val_acc: 0.8257
Epoch 127/500
132/132 [==============================] - 94s 709ms/step - loss: 0.2034 - acc: 0.9265 - val_loss: 0.4547 - val_acc: 0.8257
Epoch 128/500
132/132 [==============================] - 86s 652ms/step - loss: 0.2039 - acc: 0.9242 - val_loss: 0.5198 - val_acc: 0.8119
Epoch 129/500
132/132 [==============================] - 86s 654ms/step - loss: 0.1986 - acc: 0.9270 - val_loss: 0.5317 - val_acc: 0.8303
Epoch 130/500
132/132 [==============================] - 81s 611ms/step - loss: 0.1999 - acc: 0.9246 - val_loss: 0.4843 - val_acc: 0.8394
Epoch 131/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1944 - acc: 0.9271 - val_loss: 0.4808 - val_acc: 0.8532
Epoch 132/500
132/132 [==============================] - 77s 586ms/step - loss: 0.2073 - acc: 0.9200 - val_loss: 0.5290 - val_acc: 0.8303
Epoch 133/500
132/132 [==============================] - 79s 595ms/step - loss: 0.1898 - acc: 0.9261 - val_loss: 0.5085 - val_acc: 0.8211
Epoch 134/500
132/132 [==============================] - 79s 595ms/step - loss: 0.1941 - acc: 0.9280 - val_loss: 0.4551 - val_acc: 0.8440
Epoch 135/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1868 - acc: 0.9285 - val_loss: 0.4616 - val_acc: 0.8394
Epoch 136/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1958 - acc: 0.9277 - val_loss: 0.4635 - val_acc: 0.8578
Epoch 137/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1835 - acc: 0.9323 - val_loss: 0.5176 - val_acc: 0.8165
Epoch 138/500
132/132 [==============================] - 78s 588ms/step - loss: 0.1836 - acc: 0.9341 - val_loss: 0.4821 - val_acc: 0.8303
Epoch 139/500
132/132 [==============================] - 78s 587ms/step - loss: 0.1846 - acc: 0.9312 - val_loss: 0.4970 - val_acc: 0.8394
Epoch 140/500
132/132 [==============================] - 78s 587ms/step - loss: 0.1761 - acc: 0.9355 - val_loss: 0.4992 - val_acc: 0.8257
Epoch 141/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1792 - acc: 0.9342 - val_loss: 0.4748 - val_acc: 0.8303
Epoch 142/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1791 - acc: 0.9335 - val_loss: 0.4867 - val_acc: 0.8303
Epoch 143/500
132/132 [==============================] - 79s 596ms/step - loss: 0.1813 - acc: 0.9317 - val_loss: 0.5802 - val_acc: 0.8119
Epoch 144/500
132/132 [==============================] - 78s 588ms/step - loss: 0.1796 - acc: 0.9331 - val_loss: 0.5244 - val_acc: 0.8165
Epoch 145/500
132/132 [==============================] - 79s 597ms/step - loss: 0.1669 - acc: 0.9367 - val_loss: 0.5349 - val_acc: 0.8211
Epoch 146/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1645 - acc: 0.9401 - val_loss: 0.4999 - val_acc: 0.8394
Epoch 147/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1697 - acc: 0.9373 - val_loss: 0.4979 - val_acc: 0.8349
Epoch 148/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1690 - acc: 0.9395 - val_loss: 0.5618 - val_acc: 0.8211
Epoch 149/500
132/132 [==============================] - 79s 597ms/step - loss: 0.1866 - acc: 0.9329 - val_loss: 0.5349 - val_acc: 0.8211
Epoch 150/500
132/132 [==============================] - 79s 596ms/step - loss: 0.1773 - acc: 0.9344 - val_loss: 0.4944 - val_acc: 0.8303
Epoch 151/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1600 - acc: 0.9417 - val_loss: 0.5123 - val_acc: 0.8440
Epoch 152/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1658 - acc: 0.9353 - val_loss: 0.4770 - val_acc: 0.8394
Epoch 153/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1613 - acc: 0.9424 - val_loss: 0.5189 - val_acc: 0.8165
Epoch 154/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1683 - acc: 0.9356 - val_loss: 0.5008 - val_acc: 0.8257
Epoch 155/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1556 - acc: 0.9458 - val_loss: 0.5073 - val_acc: 0.8486
Epoch 156/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1542 - acc: 0.9426 - val_loss: 0.5431 - val_acc: 0.8349
Epoch 157/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1424 - acc: 0.9464 - val_loss: 0.5017 - val_acc: 0.8486
Epoch 158/500
132/132 [==============================] - 79s 595ms/step - loss: 0.1631 - acc: 0.9398 - val_loss: 0.5131 - val_acc: 0.8303
Epoch 159/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1480 - acc: 0.9479 - val_loss: 0.5090 - val_acc: 0.8303
Epoch 160/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1461 - acc: 0.9464 - val_loss: 0.5082 - val_acc: 0.8486
Epoch 161/500
132/132 [==============================] - 79s 597ms/step - loss: 0.1624 - acc: 0.9377 - val_loss: 0.5180 - val_acc: 0.8578
Epoch 162/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1507 - acc: 0.9453 - val_loss: 0.4904 - val_acc: 0.8349
Epoch 163/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1369 - acc: 0.9544 - val_loss: 0.5303 - val_acc: 0.8440
Epoch 164/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1443 - acc: 0.9476 - val_loss: 0.5356 - val_acc: 0.8486
Epoch 165/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1361 - acc: 0.9506 - val_loss: 0.5823 - val_acc: 0.8119
Epoch 166/500
132/132 [==============================] - 78s 594ms/step - loss: 0.1402 - acc: 0.9489 - val_loss: 0.5487 - val_acc: 0.8440
Epoch 167/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1596 - acc: 0.9409 - val_loss: 0.5081 - val_acc: 0.8394
Epoch 168/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1516 - acc: 0.9445 - val_loss: 0.5500 - val_acc: 0.8486
Epoch 169/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1343 - acc: 0.9532 - val_loss: 0.5240 - val_acc: 0.8532
Epoch 170/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1354 - acc: 0.9535 - val_loss: 0.5260 - val_acc: 0.8394
Epoch 171/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1367 - acc: 0.9497 - val_loss: 0.4999 - val_acc: 0.8486
Epoch 172/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1299 - acc: 0.9521 - val_loss: 0.4885 - val_acc: 0.8532
Epoch 173/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1431 - acc: 0.9471 - val_loss: 0.5418 - val_acc: 0.8394
Epoch 174/500
132/132 [==============================] - 79s 596ms/step - loss: 0.1473 - acc: 0.9461 - val_loss: 0.5411 - val_acc: 0.8486
Epoch 175/500
132/132 [==============================] - 80s 602ms/step - loss: 0.1263 - acc: 0.9526 - val_loss: 0.5644 - val_acc: 0.8486
Epoch 176/500
132/132 [==============================] - 79s 602ms/step - loss: 0.1269 - acc: 0.9542 - val_loss: 0.5137 - val_acc: 0.8532
Epoch 177/500
132/132 [==============================] - 79s 600ms/step - loss: 0.1303 - acc: 0.9547 - val_loss: 0.5678 - val_acc: 0.8624
Epoch 178/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1342 - acc: 0.9494 - val_loss: 0.5914 - val_acc: 0.8532
Epoch 179/500
132/132 [==============================] - 79s 599ms/step - loss: 0.1231 - acc: 0.9571 - val_loss: 0.5077 - val_acc: 0.8853
Epoch 180/500
132/132 [==============================] - 79s 600ms/step - loss: 0.1222 - acc: 0.9588 - val_loss: 0.5724 - val_acc: 0.8578
Epoch 181/500
132/132 [==============================] - 79s 598ms/step - loss: 0.1192 - acc: 0.9585 - val_loss: 0.5471 - val_acc: 0.8440
Epoch 182/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1132 - acc: 0.9616 - val_loss: 0.5525 - val_acc: 0.8257
Epoch 183/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1197 - acc: 0.9570 - val_loss: 0.5516 - val_acc: 0.8532
Epoch 184/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1085 - acc: 0.9618 - val_loss: 0.5758 - val_acc: 0.8532
Epoch 185/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1101 - acc: 0.9617 - val_loss: 0.5719 - val_acc: 0.8716
Epoch 186/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1178 - acc: 0.9568 - val_loss: 0.5736 - val_acc: 0.8486
Epoch 187/500
132/132 [==============================] - 78s 593ms/step - loss: 0.1161 - acc: 0.9606 - val_loss: 0.5343 - val_acc: 0.8578
Epoch 188/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1112 - acc: 0.9600 - val_loss: 0.6016 - val_acc: 0.8073
Epoch 189/500
132/132 [==============================] - 79s 597ms/step - loss: 0.1094 - acc: 0.9639 - val_loss: 0.5277 - val_acc: 0.8716
Epoch 190/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1035 - acc: 0.9642 - val_loss: 0.5825 - val_acc: 0.8624
Epoch 191/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1127 - acc: 0.9601 - val_loss: 0.5454 - val_acc: 0.8624
Epoch 192/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1030 - acc: 0.9648 - val_loss: 0.5598 - val_acc: 0.8578
Epoch 193/500
132/132 [==============================] - 78s 590ms/step - loss: 0.1058 - acc: 0.9639 - val_loss: 0.5674 - val_acc: 0.8716
Epoch 194/500
132/132 [==============================] - 78s 591ms/step - loss: 0.1011 - acc: 0.9650 - val_loss: 0.5027 - val_acc: 0.8624
Epoch 195/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1074 - acc: 0.9621 - val_loss: 0.4915 - val_acc: 0.8670
Epoch 196/500
132/132 [==============================] - 78s 589ms/step - loss: 0.1031 - acc: 0.9632 - val_loss: 0.5720 - val_acc: 0.8761
Epoch 197/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0995 - acc: 0.9642 - val_loss: 0.5683 - val_acc: 0.8624
Epoch 198/500
132/132 [==============================] - 78s 592ms/step - loss: 0.1011 - acc: 0.9645 - val_loss: 0.5751 - val_acc: 0.8853
Epoch 199/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0975 - acc: 0.9665 - val_loss: 0.5645 - val_acc: 0.8670
Epoch 200/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0916 - acc: 0.9709 - val_loss: 0.5437 - val_acc: 0.8440
Epoch 201/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0906 - acc: 0.9682 - val_loss: 0.5678 - val_acc: 0.8578
Epoch 202/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0921 - acc: 0.9679 - val_loss: 0.5569 - val_acc: 0.8761
Epoch 203/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0948 - acc: 0.9651 - val_loss: 0.5042 - val_acc: 0.8670
Epoch 204/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0874 - acc: 0.9744 - val_loss: 0.5274 - val_acc: 0.8578
Epoch 205/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0943 - acc: 0.9673 - val_loss: 0.5606 - val_acc: 0.8716
Epoch 206/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0934 - acc: 0.9686 - val_loss: 0.5718 - val_acc: 0.8945
Epoch 207/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0978 - acc: 0.9668 - val_loss: 0.5492 - val_acc: 0.8578
Epoch 208/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0900 - acc: 0.9684 - val_loss: 0.5879 - val_acc: 0.8761
Epoch 209/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0929 - acc: 0.9676 - val_loss: 0.5547 - val_acc: 0.8670
Epoch 210/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0831 - acc: 0.9708 - val_loss: 0.5874 - val_acc: 0.8716
Epoch 211/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0824 - acc: 0.9741 - val_loss: 0.6227 - val_acc: 0.8853
Epoch 212/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0826 - acc: 0.9723 - val_loss: 0.6065 - val_acc: 0.8716
Epoch 213/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0938 - acc: 0.9655 - val_loss: 0.4996 - val_acc: 0.8670
Epoch 214/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0852 - acc: 0.9741 - val_loss: 0.6019 - val_acc: 0.8761
Epoch 215/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0978 - acc: 0.9680 - val_loss: 0.5266 - val_acc: 0.8578
Epoch 216/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0852 - acc: 0.9729 - val_loss: 0.6121 - val_acc: 0.8303
Epoch 217/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0725 - acc: 0.9776 - val_loss: 0.6001 - val_acc: 0.8670
Epoch 218/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0763 - acc: 0.9733 - val_loss: 0.5631 - val_acc: 0.8761
Epoch 219/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0738 - acc: 0.9756 - val_loss: 0.5289 - val_acc: 0.8761
Epoch 220/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0760 - acc: 0.9753 - val_loss: 0.5379 - val_acc: 0.8532
Epoch 221/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0792 - acc: 0.9745 - val_loss: 0.5650 - val_acc: 0.8716
Epoch 222/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0732 - acc: 0.9773 - val_loss: 0.5967 - val_acc: 0.9037
Epoch 223/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0791 - acc: 0.9717 - val_loss: 0.5953 - val_acc: 0.8761
Epoch 224/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0702 - acc: 0.9765 - val_loss: 0.5803 - val_acc: 0.8670
Epoch 225/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0741 - acc: 0.9735 - val_loss: 0.5646 - val_acc: 0.8670
Epoch 226/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0757 - acc: 0.9767 - val_loss: 0.5664 - val_acc: 0.8716
Epoch 227/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0640 - acc: 0.9782 - val_loss: 0.6186 - val_acc: 0.8532
Epoch 228/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0778 - acc: 0.9744 - val_loss: 0.5558 - val_acc: 0.8670
Epoch 229/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0735 - acc: 0.9786 - val_loss: 0.6307 - val_acc: 0.8807
Epoch 230/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0741 - acc: 0.9779 - val_loss: 0.5326 - val_acc: 0.8716
Epoch 231/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0697 - acc: 0.9756 - val_loss: 0.4980 - val_acc: 0.8761
Epoch 232/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0700 - acc: 0.9758 - val_loss: 0.5959 - val_acc: 0.8716
Epoch 233/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0638 - acc: 0.9792 - val_loss: 0.5446 - val_acc: 0.8578
Epoch 234/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0638 - acc: 0.9787 - val_loss: 0.5125 - val_acc: 0.8624
Epoch 235/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0718 - acc: 0.9759 - val_loss: 0.5742 - val_acc: 0.8807
Epoch 236/500
132/132 [==============================] - 79s 598ms/step - loss: 0.0671 - acc: 0.9788 - val_loss: 0.6913 - val_acc: 0.8532
Epoch 237/500
132/132 [==============================] - 79s 601ms/step - loss: 0.0691 - acc: 0.9782 - val_loss: 0.5902 - val_acc: 0.8670
Epoch 238/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0623 - acc: 0.9808 - val_loss: 0.5996 - val_acc: 0.8440
Epoch 239/500
132/132 [==============================] - 79s 600ms/step - loss: 0.0590 - acc: 0.9800 - val_loss: 0.6090 - val_acc: 0.8716
Epoch 240/500
132/132 [==============================] - 80s 604ms/step - loss: 0.0703 - acc: 0.9783 - val_loss: 0.5552 - val_acc: 0.8532
Epoch 241/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0579 - acc: 0.9812 - val_loss: 0.7433 - val_acc: 0.8670
Epoch 242/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0620 - acc: 0.9806 - val_loss: 0.5367 - val_acc: 0.8807
Epoch 243/500
132/132 [==============================] - 79s 598ms/step - loss: 0.0587 - acc: 0.9829 - val_loss: 0.5882 - val_acc: 0.8670
Epoch 244/500
132/132 [==============================] - 80s 603ms/step - loss: 0.0571 - acc: 0.9826 - val_loss: 0.5972 - val_acc: 0.8624
Epoch 245/500
132/132 [==============================] - 80s 606ms/step - loss: 0.0604 - acc: 0.9806 - val_loss: 0.5442 - val_acc: 0.8761
Epoch 246/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0544 - acc: 0.9830 - val_loss: 0.6204 - val_acc: 0.8578
Epoch 247/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0495 - acc: 0.9845 - val_loss: 0.5949 - val_acc: 0.8670
Epoch 248/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0573 - acc: 0.9812 - val_loss: 0.6172 - val_acc: 0.8624
Epoch 249/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0500 - acc: 0.9848 - val_loss: 0.5814 - val_acc: 0.8670
Epoch 250/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0638 - acc: 0.9786 - val_loss: 0.6404 - val_acc: 0.8394
Epoch 251/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0600 - acc: 0.9812 - val_loss: 0.6238 - val_acc: 0.8807
Epoch 252/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0493 - acc: 0.9844 - val_loss: 0.5720 - val_acc: 0.8624
Epoch 253/500
132/132 [==============================] - 79s 601ms/step - loss: 0.0494 - acc: 0.9861 - val_loss: 0.5655 - val_acc: 0.8624
Epoch 254/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0501 - acc: 0.9820 - val_loss: 0.5726 - val_acc: 0.8578
Epoch 255/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0522 - acc: 0.9830 - val_loss: 0.5941 - val_acc: 0.8670
Epoch 256/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0513 - acc: 0.9855 - val_loss: 0.5510 - val_acc: 0.8624
Epoch 257/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0508 - acc: 0.9827 - val_loss: 0.6830 - val_acc: 0.8853
Epoch 258/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0452 - acc: 0.9865 - val_loss: 0.5819 - val_acc: 0.8670
Epoch 259/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0459 - acc: 0.9879 - val_loss: 0.6080 - val_acc: 0.8624
Epoch 260/500
132/132 [==============================] - 79s 598ms/step - loss: 0.0515 - acc: 0.9842 - val_loss: 0.6005 - val_acc: 0.8624
Epoch 261/500
132/132 [==============================] - 80s 603ms/step - loss: 0.0508 - acc: 0.9844 - val_loss: 0.6102 - val_acc: 0.8670
Epoch 262/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0472 - acc: 0.9848 - val_loss: 0.6390 - val_acc: 0.8853
Epoch 263/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0423 - acc: 0.9880 - val_loss: 0.6762 - val_acc: 0.8761
Epoch 264/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0526 - acc: 0.9829 - val_loss: 0.5131 - val_acc: 0.8899
Epoch 265/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0515 - acc: 0.9852 - val_loss: 0.5857 - val_acc: 0.8440
Epoch 266/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0443 - acc: 0.9864 - val_loss: 0.5834 - val_acc: 0.8899
Epoch 267/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0422 - acc: 0.9871 - val_loss: 0.5524 - val_acc: 0.8899
Epoch 268/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0431 - acc: 0.9865 - val_loss: 0.5104 - val_acc: 0.8624
Epoch 269/500
132/132 [==============================] - 79s 600ms/step - loss: 0.0430 - acc: 0.9880 - val_loss: 0.5631 - val_acc: 0.8991
Epoch 270/500
132/132 [==============================] - 78s 595ms/step - loss: 0.0458 - acc: 0.9856 - val_loss: 0.5582 - val_acc: 0.8578
Epoch 271/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0411 - acc: 0.9879 - val_loss: 0.5406 - val_acc: 0.8899
Epoch 272/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0418 - acc: 0.9885 - val_loss: 0.5453 - val_acc: 0.8807
Epoch 273/500
132/132 [==============================] - 78s 595ms/step - loss: 0.0454 - acc: 0.9874 - val_loss: 0.5770 - val_acc: 0.8624
Epoch 274/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0391 - acc: 0.9888 - val_loss: 0.6036 - val_acc: 0.8716
Epoch 275/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0426 - acc: 0.9870 - val_loss: 0.5756 - val_acc: 0.8761
Epoch 276/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0367 - acc: 0.9909 - val_loss: 0.5853 - val_acc: 0.8853
Epoch 277/500
132/132 [==============================] - 80s 602ms/step - loss: 0.0353 - acc: 0.9892 - val_loss: 0.6264 - val_acc: 0.8945
Epoch 278/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0437 - acc: 0.9885 - val_loss: 0.5871 - val_acc: 0.8807
Epoch 279/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0461 - acc: 0.9867 - val_loss: 0.5707 - val_acc: 0.8853
Epoch 280/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0377 - acc: 0.9886 - val_loss: 0.6260 - val_acc: 0.8670
Epoch 281/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0429 - acc: 0.9870 - val_loss: 0.6402 - val_acc: 0.8670
Epoch 282/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0401 - acc: 0.9870 - val_loss: 0.6215 - val_acc: 0.8624
Epoch 283/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0411 - acc: 0.9868 - val_loss: 0.6722 - val_acc: 0.8807
Epoch 284/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0397 - acc: 0.9867 - val_loss: 0.6893 - val_acc: 0.8578
Epoch 285/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0384 - acc: 0.9874 - val_loss: 0.5248 - val_acc: 0.8807
Epoch 286/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0375 - acc: 0.9891 - val_loss: 0.6613 - val_acc: 0.8624
Epoch 287/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0308 - acc: 0.9917 - val_loss: 0.5543 - val_acc: 0.8624
Epoch 288/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0418 - acc: 0.9879 - val_loss: 0.5734 - val_acc: 0.8578
Epoch 289/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0327 - acc: 0.9892 - val_loss: 0.5766 - val_acc: 0.8716
Epoch 290/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0379 - acc: 0.9876 - val_loss: 0.5594 - val_acc: 0.8624
Epoch 291/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0443 - acc: 0.9858 - val_loss: 0.5175 - val_acc: 0.8807
Epoch 292/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0392 - acc: 0.9874 - val_loss: 0.5255 - val_acc: 0.8716
Epoch 293/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0385 - acc: 0.9882 - val_loss: 0.5746 - val_acc: 0.8670
Epoch 294/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0429 - acc: 0.9862 - val_loss: 0.6072 - val_acc: 0.8807
Epoch 295/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0336 - acc: 0.9911 - val_loss: 0.5982 - val_acc: 0.8761
Epoch 296/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0362 - acc: 0.9903 - val_loss: 0.7062 - val_acc: 0.8853
Epoch 297/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0347 - acc: 0.9906 - val_loss: 0.5426 - val_acc: 0.8899
Epoch 298/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0312 - acc: 0.9921 - val_loss: 0.5573 - val_acc: 0.8532
Epoch 299/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0351 - acc: 0.9886 - val_loss: 0.4975 - val_acc: 0.8853
Epoch 300/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0302 - acc: 0.9923 - val_loss: 0.6258 - val_acc: 0.9128
Epoch 301/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0350 - acc: 0.9900 - val_loss: 0.5320 - val_acc: 0.8716
Epoch 302/500
132/132 [==============================] - 78s 595ms/step - loss: 0.0346 - acc: 0.9892 - val_loss: 0.5615 - val_acc: 0.8991
Epoch 303/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0316 - acc: 0.9897 - val_loss: 0.6170 - val_acc: 0.8578
Epoch 304/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0331 - acc: 0.9897 - val_loss: 0.6553 - val_acc: 0.8761
Epoch 305/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0288 - acc: 0.9912 - val_loss: 0.5801 - val_acc: 0.8761
Epoch 306/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0306 - acc: 0.9921 - val_loss: 0.6598 - val_acc: 0.8486
Epoch 307/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0396 - acc: 0.9876 - val_loss: 0.5232 - val_acc: 0.8807
Epoch 308/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0295 - acc: 0.9926 - val_loss: 0.5706 - val_acc: 0.8761
Epoch 309/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0291 - acc: 0.9911 - val_loss: 0.6290 - val_acc: 0.8716
Epoch 310/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0251 - acc: 0.9932 - val_loss: 0.5833 - val_acc: 0.8853
Epoch 311/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0361 - acc: 0.9889 - val_loss: 0.6359 - val_acc: 0.8624
Epoch 312/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0361 - acc: 0.9883 - val_loss: 0.5807 - val_acc: 0.8716
Epoch 313/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0279 - acc: 0.9932 - val_loss: 0.5255 - val_acc: 0.8807
Epoch 314/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0300 - acc: 0.9911 - val_loss: 0.5133 - val_acc: 0.8578
Epoch 315/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0300 - acc: 0.9914 - val_loss: 0.6051 - val_acc: 0.8899
Epoch 316/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0284 - acc: 0.9918 - val_loss: 0.5949 - val_acc: 0.8716
Epoch 317/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0292 - acc: 0.9908 - val_loss: 0.5719 - val_acc: 0.8761
Epoch 318/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0236 - acc: 0.9935 - val_loss: 0.6146 - val_acc: 0.8945
Epoch 319/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0239 - acc: 0.9942 - val_loss: 0.5850 - val_acc: 0.8624
Epoch 320/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0349 - acc: 0.9894 - val_loss: 0.5864 - val_acc: 0.8899
Epoch 321/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0260 - acc: 0.9927 - val_loss: 0.6070 - val_acc: 0.8899
Epoch 322/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0227 - acc: 0.9938 - val_loss: 0.6603 - val_acc: 0.8670
Epoch 323/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0252 - acc: 0.9932 - val_loss: 0.6144 - val_acc: 0.8670
Epoch 324/500
132/132 [==============================] - 79s 601ms/step - loss: 0.0306 - acc: 0.9906 - val_loss: 0.7452 - val_acc: 0.8761
Epoch 325/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0273 - acc: 0.9918 - val_loss: 0.5550 - val_acc: 0.8991
Epoch 326/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0234 - acc: 0.9930 - val_loss: 0.6818 - val_acc: 0.8716
Epoch 327/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0257 - acc: 0.9927 - val_loss: 0.5642 - val_acc: 0.8945
Epoch 328/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0377 - acc: 0.9880 - val_loss: 0.6507 - val_acc: 0.8945
Epoch 329/500
132/132 [==============================] - 78s 589ms/step - loss: 0.0314 - acc: 0.9909 - val_loss: 0.5586 - val_acc: 0.8807
Epoch 330/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0292 - acc: 0.9918 - val_loss: 0.5741 - val_acc: 0.8761
Epoch 331/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0313 - acc: 0.9902 - val_loss: 0.6457 - val_acc: 0.8807
Epoch 332/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0345 - acc: 0.9889 - val_loss: 0.6648 - val_acc: 0.8899
Epoch 333/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0245 - acc: 0.9929 - val_loss: 0.5378 - val_acc: 0.8899
Epoch 334/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0211 - acc: 0.9950 - val_loss: 0.6183 - val_acc: 0.8716
Epoch 335/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0244 - acc: 0.9936 - val_loss: 0.6664 - val_acc: 0.8945
Epoch 336/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0233 - acc: 0.9935 - val_loss: 0.7040 - val_acc: 0.8670
Epoch 337/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0251 - acc: 0.9930 - val_loss: 0.6759 - val_acc: 0.8991
Epoch 338/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0206 - acc: 0.9938 - val_loss: 0.5803 - val_acc: 0.8899
Epoch 339/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0235 - acc: 0.9929 - val_loss: 0.6043 - val_acc: 0.8899
Epoch 340/500
132/132 [==============================] - 79s 600ms/step - loss: 0.0311 - acc: 0.9906 - val_loss: 0.6814 - val_acc: 0.8761
Epoch 341/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0251 - acc: 0.9930 - val_loss: 0.6652 - val_acc: 0.8807
Epoch 342/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0258 - acc: 0.9941 - val_loss: 0.6195 - val_acc: 0.8853
Epoch 343/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0236 - acc: 0.9935 - val_loss: 0.6531 - val_acc: 0.8853
Epoch 344/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0244 - acc: 0.9930 - val_loss: 0.6381 - val_acc: 0.8853
Epoch 345/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0242 - acc: 0.9935 - val_loss: 0.6100 - val_acc: 0.8853
Epoch 346/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0371 - acc: 0.9868 - val_loss: 0.7375 - val_acc: 0.8624
Epoch 347/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0248 - acc: 0.9933 - val_loss: 0.6622 - val_acc: 0.8853
Epoch 348/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0211 - acc: 0.9947 - val_loss: 0.6939 - val_acc: 0.8853
Epoch 349/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0218 - acc: 0.9939 - val_loss: 0.7256 - val_acc: 0.8716
Epoch 350/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0194 - acc: 0.9942 - val_loss: 0.6598 - val_acc: 0.8761
Epoch 351/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0230 - acc: 0.9932 - val_loss: 0.7143 - val_acc: 0.8670
Epoch 352/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0202 - acc: 0.9941 - val_loss: 0.6338 - val_acc: 0.8807
Epoch 353/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0187 - acc: 0.9947 - val_loss: 0.6316 - val_acc: 0.8716
Epoch 354/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0183 - acc: 0.9945 - val_loss: 0.6661 - val_acc: 0.8807
Epoch 355/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0231 - acc: 0.9939 - val_loss: 0.6963 - val_acc: 0.8716
Epoch 356/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0239 - acc: 0.9926 - val_loss: 0.6957 - val_acc: 0.8853
Epoch 357/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0188 - acc: 0.9962 - val_loss: 0.6806 - val_acc: 0.8899
Epoch 358/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0201 - acc: 0.9944 - val_loss: 0.6548 - val_acc: 0.8853
Epoch 359/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0228 - acc: 0.9935 - val_loss: 0.5859 - val_acc: 0.8716
Epoch 360/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0184 - acc: 0.9947 - val_loss: 0.6832 - val_acc: 0.8761
Epoch 361/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0177 - acc: 0.9953 - val_loss: 0.6689 - val_acc: 0.8670
Epoch 362/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0170 - acc: 0.9958 - val_loss: 0.7842 - val_acc: 0.8624
Epoch 363/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0239 - acc: 0.9932 - val_loss: 0.6317 - val_acc: 0.8761
Epoch 364/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0309 - acc: 0.9911 - val_loss: 0.8896 - val_acc: 0.8440
Epoch 365/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0257 - acc: 0.9915 - val_loss: 0.7989 - val_acc: 0.8761
Epoch 366/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0213 - acc: 0.9938 - val_loss: 0.6768 - val_acc: 0.8761
Epoch 367/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0188 - acc: 0.9964 - val_loss: 0.6211 - val_acc: 0.8670
Epoch 368/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0261 - acc: 0.9923 - val_loss: 0.6709 - val_acc: 0.8899
Epoch 369/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0248 - acc: 0.9927 - val_loss: 0.7706 - val_acc: 0.8761
Epoch 370/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0154 - acc: 0.9961 - val_loss: 0.6477 - val_acc: 0.8670
Epoch 371/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0185 - acc: 0.9938 - val_loss: 0.8390 - val_acc: 0.8532
Epoch 372/500
132/132 [==============================] - 80s 603ms/step - loss: 0.0213 - acc: 0.9932 - val_loss: 0.6290 - val_acc: 0.8807
Epoch 373/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0244 - acc: 0.9929 - val_loss: 0.6316 - val_acc: 0.8899
Epoch 374/500
132/132 [==============================] - 79s 600ms/step - loss: 0.0295 - acc: 0.9903 - val_loss: 0.7818 - val_acc: 0.8807
Epoch 375/500
132/132 [==============================] - 79s 595ms/step - loss: 0.0246 - acc: 0.9920 - val_loss: 0.6684 - val_acc: 0.8670
Epoch 376/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0172 - acc: 0.9948 - val_loss: 0.6995 - val_acc: 0.8670
Epoch 377/500
132/132 [==============================] - 79s 599ms/step - loss: 0.0161 - acc: 0.9948 - val_loss: 0.7610 - val_acc: 0.8761
Epoch 378/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0247 - acc: 0.9918 - val_loss: 0.8199 - val_acc: 0.8624
Epoch 379/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0232 - acc: 0.9938 - val_loss: 0.7131 - val_acc: 0.8761
Epoch 380/500
132/132 [==============================] - 80s 606ms/step - loss: 0.0223 - acc: 0.9945 - val_loss: 0.6437 - val_acc: 0.8670
Epoch 381/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0215 - acc: 0.9945 - val_loss: 0.7001 - val_acc: 0.8716
Epoch 382/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0208 - acc: 0.9950 - val_loss: 0.6277 - val_acc: 0.8624
Epoch 383/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0252 - acc: 0.9924 - val_loss: 0.6039 - val_acc: 0.8716
Epoch 384/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0200 - acc: 0.9936 - val_loss: 0.6959 - val_acc: 0.8807
Epoch 385/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0138 - acc: 0.9965 - val_loss: 0.7150 - val_acc: 0.8716
Epoch 386/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0190 - acc: 0.9939 - val_loss: 0.6460 - val_acc: 0.8853
Epoch 387/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0230 - acc: 0.9944 - val_loss: 0.5767 - val_acc: 0.8945
Epoch 388/500
132/132 [==============================] - 79s 601ms/step - loss: 0.0143 - acc: 0.9964 - val_loss: 0.6533 - val_acc: 0.8853
Epoch 389/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0167 - acc: 0.9953 - val_loss: 0.6225 - val_acc: 0.8853
Epoch 390/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0185 - acc: 0.9950 - val_loss: 0.6410 - val_acc: 0.8761
Epoch 391/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0156 - acc: 0.9959 - val_loss: 0.7466 - val_acc: 0.8670
Epoch 392/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0291 - acc: 0.9920 - val_loss: 0.5990 - val_acc: 0.8716
Epoch 393/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0188 - acc: 0.9953 - val_loss: 0.5956 - val_acc: 0.8716
Epoch 394/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0133 - acc: 0.9974 - val_loss: 0.6270 - val_acc: 0.8624
Epoch 395/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0113 - acc: 0.9973 - val_loss: 0.7331 - val_acc: 0.8761
Epoch 396/500
132/132 [==============================] - 79s 598ms/step - loss: 0.0168 - acc: 0.9953 - val_loss: 0.6694 - val_acc: 0.8761
Epoch 397/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0245 - acc: 0.9929 - val_loss: 0.6209 - val_acc: 0.8578
Epoch 398/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0230 - acc: 0.9935 - val_loss: 0.6091 - val_acc: 0.8257
Epoch 399/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0162 - acc: 0.9953 - val_loss: 0.7675 - val_acc: 0.8899
Epoch 400/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0200 - acc: 0.9945 - val_loss: 0.6487 - val_acc: 0.8624
Epoch 401/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0188 - acc: 0.9939 - val_loss: 0.6565 - val_acc: 0.8807
Epoch 402/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0158 - acc: 0.9962 - val_loss: 0.6747 - val_acc: 0.8899
Epoch 403/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0136 - acc: 0.9973 - val_loss: 0.7645 - val_acc: 0.8578
Epoch 404/500
132/132 [==============================] - 79s 602ms/step - loss: 0.0151 - acc: 0.9964 - val_loss: 0.7649 - val_acc: 0.8532
Epoch 405/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0136 - acc: 0.9962 - val_loss: 0.6781 - val_acc: 0.8532
Epoch 406/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0172 - acc: 0.9953 - val_loss: 0.6342 - val_acc: 0.8670
Epoch 407/500
132/132 [==============================] - 80s 605ms/step - loss: 0.0487 - acc: 0.9856 - val_loss: 0.7176 - val_acc: 0.8578
Epoch 408/500
132/132 [==============================] - 80s 603ms/step - loss: 0.0174 - acc: 0.9952 - val_loss: 0.6226 - val_acc: 0.8761
Epoch 409/500
132/132 [==============================] - 81s 613ms/step - loss: 0.0204 - acc: 0.9947 - val_loss: 0.6274 - val_acc: 0.8807
Epoch 410/500
132/132 [==============================] - 81s 613ms/step - loss: 0.0176 - acc: 0.9956 - val_loss: 0.6225 - val_acc: 0.8624
Epoch 411/500
132/132 [==============================] - 79s 597ms/step - loss: 0.0140 - acc: 0.9961 - val_loss: 0.6759 - val_acc: 0.8624
Epoch 412/500
132/132 [==============================] - 80s 606ms/step - loss: 0.0142 - acc: 0.9964 - val_loss: 0.5903 - val_acc: 0.8899
Epoch 413/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0212 - acc: 0.9938 - val_loss: 0.6293 - val_acc: 0.8440
Epoch 414/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0165 - acc: 0.9953 - val_loss: 0.6146 - val_acc: 0.8807
Epoch 415/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0165 - acc: 0.9959 - val_loss: 0.6114 - val_acc: 0.8761
Epoch 416/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0116 - acc: 0.9970 - val_loss: 0.6662 - val_acc: 0.8624
Epoch 417/500
132/132 [==============================] - 78s 590ms/step - loss: 0.0173 - acc: 0.9947 - val_loss: 0.5744 - val_acc: 0.8807
Epoch 418/500
132/132 [==============================] - 79s 596ms/step - loss: 0.0149 - acc: 0.9952 - val_loss: 0.7394 - val_acc: 0.8853
Epoch 419/500
132/132 [==============================] - 78s 594ms/step - loss: 0.0186 - acc: 0.9945 - val_loss: 0.6595 - val_acc: 0.8807
Epoch 420/500
132/132 [==============================] - 80s 604ms/step - loss: 0.0142 - acc: 0.9959 - val_loss: 0.6941 - val_acc: 0.9037
Epoch 421/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0187 - acc: 0.9944 - val_loss: 0.7151 - val_acc: 0.8532
Epoch 422/500
132/132 [==============================] - 78s 591ms/step - loss: 0.0185 - acc: 0.9945 - val_loss: 0.6625 - val_acc: 0.8670
Epoch 423/500
132/132 [==============================] - 78s 592ms/step - loss: 0.0148 - acc: 0.9950 - val_loss: 0.6843 - val_acc: 0.8761
Epoch 424/500
132/132 [==============================] - 78s 593ms/step - loss: 0.0271 - acc: 0.9906 - val_loss: 0.6741 - val_acc: 0.8578
Epoch 425/500
132/132 [==============================] - 78s 595ms/step - loss: 0.0108 - acc: 0.9977 - val_loss: 0.6369 - val_acc: 0.8716
Epoch 426/500
132/132 [==============================] - 79s 598ms/step - loss: 0.0110 - acc: 0.9971 - val_loss: 0.7618 - val_acc: 0.8761
Epoch 427/500
 25/132 [====&gt;.........................] - ETA: 1:03 - loss: 0.0122 - acc: 0.9960</pre>
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<pre>
<span class="ansi-red-intense-fg ansi-bold">---------------------------------------------------------------------------</span>
<span class="ansi-red-intense-fg ansi-bold">KeyboardInterrupt</span>                         Traceback (most recent call last)
<span class="ansi-green-intense-fg ansi-bold">&lt;ipython-input-29-80d5c84ae883&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-intense-fg ansi-bold">()</span>
<span class="ansi-green-intense-fg ansi-bold">----&gt; 1</span><span class="ansi-yellow-intense-fg ansi-bold"> </span>cnn<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-cyan-intense-fg ansi-bold">4</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> make_model1D<span class="ansi-yellow-intense-fg ansi-bold">,</span> epochs <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-cyan-intense-fg ansi-bold">500</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> save_model <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-green-intense-fg ansi-bold">True</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> name <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-blue-intense-fg ansi-bold">&#34;1D_norm&#34;</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> file_type <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-cyan-intense-fg ansi-bold">1</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">&lt;ipython-input-14-efb27c30c8b8&gt;</span> in <span class="ansi-cyan-fg">cnn</span><span class="ansi-blue-intense-fg ansi-bold">(index, use_model, n_folds, batch_size, verbose, sel_fold, epochs, save_model, val_split, file_type, name)</span>
<span class="ansi-green-fg">     22</span>                             callbacks <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-yellow-intense-fg ansi-bold">[</span>checkpoint<span class="ansi-yellow-intense-fg ansi-bold">]</span><span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-fg">     23</span>                             validation_data <span class="ansi-yellow-intense-fg ansi-bold">=</span> data_loader<span class="ansi-yellow-intense-fg ansi-bold">(</span>list<span class="ansi-yellow-intense-fg ansi-bold">(</span>zip<span class="ansi-yellow-intense-fg ansi-bold">(</span>val_X<span class="ansi-yellow-intense-fg ansi-bold">,</span> val_Y<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> batch_size<span class="ansi-yellow-intense-fg ansi-bold">,</span> file_type<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-intense-fg ansi-bold">---&gt; 24</span><span class="ansi-yellow-intense-fg ansi-bold">                             validation_steps = batch_per_val)
</span><span class="ansi-green-fg">     25</span>     <span class="ansi-green-intense-fg ansi-bold">else</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">     26</span>         model.fit_generator(data_loader(list(zip(trX, trY)), batch_size, file_type),

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\legacy\interfaces.py</span> in <span class="ansi-cyan-fg">wrapper</span><span class="ansi-blue-intense-fg ansi-bold">(*args, **kwargs)</span>
<span class="ansi-green-fg">     89</span>                 warnings.warn(&#39;Update your `&#39; + object_name +
<span class="ansi-green-fg">     90</span>                               &#39;` call to the Keras 2 API: &#39; + signature, stacklevel=2)
<span class="ansi-green-intense-fg ansi-bold">---&gt; 91</span><span class="ansi-yellow-intense-fg ansi-bold">             </span><span class="ansi-green-intense-fg ansi-bold">return</span> func<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">*</span>args<span class="ansi-yellow-intense-fg ansi-bold">,</span> <span class="ansi-yellow-intense-fg ansi-bold">**</span>kwargs<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">     92</span>         wrapper<span class="ansi-yellow-intense-fg ansi-bold">.</span>_original_function <span class="ansi-yellow-intense-fg ansi-bold">=</span> func
<span class="ansi-green-fg">     93</span>         <span class="ansi-green-intense-fg ansi-bold">return</span> wrapper

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\engine\training.py</span> in <span class="ansi-cyan-fg">fit_generator</span><span class="ansi-blue-intense-fg ansi-bold">(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)</span>
<span class="ansi-green-fg">   1424</span>             use_multiprocessing<span class="ansi-yellow-intense-fg ansi-bold">=</span>use_multiprocessing<span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-fg">   1425</span>             shuffle<span class="ansi-yellow-intense-fg ansi-bold">=</span>shuffle<span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1426</span><span class="ansi-yellow-intense-fg ansi-bold">             initial_epoch=initial_epoch)
</span><span class="ansi-green-fg">   1427</span> 
<span class="ansi-green-fg">   1428</span>     <span class="ansi-yellow-intense-fg ansi-bold">@</span>interfaces<span class="ansi-yellow-intense-fg ansi-bold">.</span>legacy_generator_methods_support

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\engine\training_generator.py</span> in <span class="ansi-cyan-fg">fit_generator</span><span class="ansi-blue-intense-fg ansi-bold">(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)</span>
<span class="ansi-green-fg">    189</span>                 outs = model.train_on_batch(x, y,
<span class="ansi-green-fg">    190</span>                                             sample_weight<span class="ansi-yellow-intense-fg ansi-bold">=</span>sample_weight<span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-intense-fg ansi-bold">--&gt; 191</span><span class="ansi-yellow-intense-fg ansi-bold">                                             class_weight=class_weight)
</span><span class="ansi-green-fg">    192</span> 
<span class="ansi-green-fg">    193</span>                 <span class="ansi-green-intense-fg ansi-bold">if</span> <span class="ansi-green-intense-fg ansi-bold">not</span> isinstance<span class="ansi-yellow-intense-fg ansi-bold">(</span>outs<span class="ansi-yellow-intense-fg ansi-bold">,</span> list<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\engine\training.py</span> in <span class="ansi-cyan-fg">train_on_batch</span><span class="ansi-blue-intense-fg ansi-bold">(self, x, y, sample_weight, class_weight)</span>
<span class="ansi-green-fg">   1218</span>             ins <span class="ansi-yellow-intense-fg ansi-bold">=</span> x <span class="ansi-yellow-intense-fg ansi-bold">+</span> y <span class="ansi-yellow-intense-fg ansi-bold">+</span> sample_weights
<span class="ansi-green-fg">   1219</span>         self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_make_train_function<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1220</span><span class="ansi-yellow-intense-fg ansi-bold">         </span>outputs <span class="ansi-yellow-intense-fg ansi-bold">=</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>train_function<span class="ansi-yellow-intense-fg ansi-bold">(</span>ins<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   1221</span>         <span class="ansi-green-intense-fg ansi-bold">if</span> len<span class="ansi-yellow-intense-fg ansi-bold">(</span>outputs<span class="ansi-yellow-intense-fg ansi-bold">)</span> <span class="ansi-yellow-intense-fg ansi-bold">==</span> <span class="ansi-cyan-intense-fg ansi-bold">1</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1222</span>             <span class="ansi-green-intense-fg ansi-bold">return</span> outputs<span class="ansi-yellow-intense-fg ansi-bold">[</span><span class="ansi-cyan-intense-fg ansi-bold">0</span><span class="ansi-yellow-intense-fg ansi-bold">]</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\backend\tensorflow_backend.py</span> in <span class="ansi-cyan-fg">__call__</span><span class="ansi-blue-intense-fg ansi-bold">(self, inputs)</span>
<span class="ansi-green-fg">   2665</span>                     <span class="ansi-blue-intense-fg ansi-bold">&#39;In order to feed symbolic tensors to a Keras model &#39;</span>
<span class="ansi-green-fg">   2666</span>                     &#39;in TensorFlow, you need tensorflow 1.8 or higher.&#39;)
<span class="ansi-green-intense-fg ansi-bold">-&gt; 2667</span><span class="ansi-yellow-intense-fg ansi-bold">             </span><span class="ansi-green-intense-fg ansi-bold">return</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_legacy_call<span class="ansi-yellow-intense-fg ansi-bold">(</span>inputs<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   2668</span> 
<span class="ansi-green-fg">   2669</span> 

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\keras\backend\tensorflow_backend.py</span> in <span class="ansi-cyan-fg">_legacy_call</span><span class="ansi-blue-intense-fg ansi-bold">(self, inputs)</span>
<span class="ansi-green-fg">   2647</span>         session <span class="ansi-yellow-intense-fg ansi-bold">=</span> get_session<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   2648</span>         updated = session.run(fetches=fetches, feed_dict=feed_dict,
<span class="ansi-green-intense-fg ansi-bold">-&gt; 2649</span><span class="ansi-yellow-intense-fg ansi-bold">                               **self.session_kwargs)
</span><span class="ansi-green-fg">   2650</span>         <span class="ansi-green-intense-fg ansi-bold">return</span> updated<span class="ansi-yellow-intense-fg ansi-bold">[</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>len<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>outputs<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">]</span>
<span class="ansi-green-fg">   2651</span> 

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">run</span><span class="ansi-blue-intense-fg ansi-bold">(self, fetches, feed_dict, options, run_metadata)</span>
<span class="ansi-green-fg">    903</span>     <span class="ansi-green-intense-fg ansi-bold">try</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">    904</span>       result = self._run(None, fetches, feed_dict, options_ptr,
<span class="ansi-green-intense-fg ansi-bold">--&gt; 905</span><span class="ansi-yellow-intense-fg ansi-bold">                          run_metadata_ptr)
</span><span class="ansi-green-fg">    906</span>       <span class="ansi-green-intense-fg ansi-bold">if</span> run_metadata<span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">    907</span>         proto_data <span class="ansi-yellow-intense-fg ansi-bold">=</span> tf_session<span class="ansi-yellow-intense-fg ansi-bold">.</span>TF_GetBuffer<span class="ansi-yellow-intense-fg ansi-bold">(</span>run_metadata_ptr<span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">_run</span><span class="ansi-blue-intense-fg ansi-bold">(self, handle, fetches, feed_dict, options, run_metadata)</span>
<span class="ansi-green-fg">   1138</span>     <span class="ansi-green-intense-fg ansi-bold">if</span> final_fetches <span class="ansi-green-intense-fg ansi-bold">or</span> final_targets <span class="ansi-green-intense-fg ansi-bold">or</span> <span class="ansi-yellow-intense-fg ansi-bold">(</span>handle <span class="ansi-green-intense-fg ansi-bold">and</span> feed_dict_tensor<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1139</span>       results = self._do_run(handle, final_targets, final_fetches,
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1140</span><span class="ansi-yellow-intense-fg ansi-bold">                              feed_dict_tensor, options, run_metadata)
</span><span class="ansi-green-fg">   1141</span>     <span class="ansi-green-intense-fg ansi-bold">else</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1142</span>       results <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-yellow-intense-fg ansi-bold">[</span><span class="ansi-yellow-intense-fg ansi-bold">]</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">_do_run</span><span class="ansi-blue-intense-fg ansi-bold">(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)</span>
<span class="ansi-green-fg">   1319</span>     <span class="ansi-green-intense-fg ansi-bold">if</span> handle <span class="ansi-green-intense-fg ansi-bold">is</span> <span class="ansi-green-intense-fg ansi-bold">None</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1320</span>       return self._do_call(_run_fn, feeds, fetches, targets, options,
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1321</span><span class="ansi-yellow-intense-fg ansi-bold">                            run_metadata)
</span><span class="ansi-green-fg">   1322</span>     <span class="ansi-green-intense-fg ansi-bold">else</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1323</span>       <span class="ansi-green-intense-fg ansi-bold">return</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_do_call<span class="ansi-yellow-intense-fg ansi-bold">(</span>_prun_fn<span class="ansi-yellow-intense-fg ansi-bold">,</span> handle<span class="ansi-yellow-intense-fg ansi-bold">,</span> feeds<span class="ansi-yellow-intense-fg ansi-bold">,</span> fetches<span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">_do_call</span><span class="ansi-blue-intense-fg ansi-bold">(self, fn, *args)</span>
<span class="ansi-green-fg">   1325</span>   <span class="ansi-green-intense-fg ansi-bold">def</span> _do_call<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">,</span> fn<span class="ansi-yellow-intense-fg ansi-bold">,</span> <span class="ansi-yellow-intense-fg ansi-bold">*</span>args<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1326</span>     <span class="ansi-green-intense-fg ansi-bold">try</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1327</span><span class="ansi-yellow-intense-fg ansi-bold">       </span><span class="ansi-green-intense-fg ansi-bold">return</span> fn<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">*</span>args<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   1328</span>     <span class="ansi-green-intense-fg ansi-bold">except</span> errors<span class="ansi-yellow-intense-fg ansi-bold">.</span>OpError <span class="ansi-green-intense-fg ansi-bold">as</span> e<span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1329</span>       message <span class="ansi-yellow-intense-fg ansi-bold">=</span> compat<span class="ansi-yellow-intense-fg ansi-bold">.</span>as_text<span class="ansi-yellow-intense-fg ansi-bold">(</span>e<span class="ansi-yellow-intense-fg ansi-bold">.</span>message<span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">_run_fn</span><span class="ansi-blue-intense-fg ansi-bold">(feed_dict, fetch_list, target_list, options, run_metadata)</span>
<span class="ansi-green-fg">   1310</span>       self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_extend_graph<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   1311</span>       return self._call_tf_sessionrun(
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1312</span><span class="ansi-yellow-intense-fg ansi-bold">           options, feed_dict, fetch_list, target_list, run_metadata)
</span><span class="ansi-green-fg">   1313</span> 
<span class="ansi-green-fg">   1314</span>     <span class="ansi-green-intense-fg ansi-bold">def</span> _prun_fn<span class="ansi-yellow-intense-fg ansi-bold">(</span>handle<span class="ansi-yellow-intense-fg ansi-bold">,</span> feed_dict<span class="ansi-yellow-intense-fg ansi-bold">,</span> fetch_list<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-green-intense-fg ansi-bold">c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\client\session.py</span> in <span class="ansi-cyan-fg">_call_tf_sessionrun</span><span class="ansi-blue-intense-fg ansi-bold">(self, options, feed_dict, fetch_list, target_list, run_metadata)</span>
<span class="ansi-green-fg">   1418</span>         return tf_session.TF_Run(
<span class="ansi-green-fg">   1419</span>             self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_session<span class="ansi-yellow-intense-fg ansi-bold">,</span> options<span class="ansi-yellow-intense-fg ansi-bold">,</span> feed_dict<span class="ansi-yellow-intense-fg ansi-bold">,</span> fetch_list<span class="ansi-yellow-intense-fg ansi-bold">,</span> target_list<span class="ansi-yellow-intense-fg ansi-bold">,</span>
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1420</span><span class="ansi-yellow-intense-fg ansi-bold">             status, run_metadata)
</span><span class="ansi-green-fg">   1421</span> 
<span class="ansi-green-fg">   1422</span>   <span class="ansi-green-intense-fg ansi-bold">def</span> _call_tf_sessionprun<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">,</span> handle<span class="ansi-yellow-intense-fg ansi-bold">,</span> feed_dict<span class="ansi-yellow-intense-fg ansi-bold">,</span> fetch_list<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-red-intense-fg ansi-bold">KeyboardInterrupt</span>: </pre>
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<div class="prompt input_prompt">In&nbsp;[26]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="s2">&quot;models/model_1D_norm.hdf5&quot;</span><span class="p">)</span>
<span class="n">train_model</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;1D_norm&quot;</span><span class="p">)</span>
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<pre>WARNING:tensorflow:From c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\util\deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
Epoch 1/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0358 - acc: 0.9879 - val_loss: 0.5196 - val_acc: 0.8991
Epoch 2/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0368 - acc: 0.9889 - val_loss: 0.5279 - val_acc: 0.8670
Epoch 3/100
132/132 [==============================] - 77s 583ms/step - loss: 0.0329 - acc: 0.9905 - val_loss: 0.5390 - val_acc: 0.8991
Epoch 4/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0324 - acc: 0.9889 - val_loss: 0.6341 - val_acc: 0.8670
Epoch 5/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0332 - acc: 0.9908 - val_loss: 0.6258 - val_acc: 0.8807
Epoch 6/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0402 - acc: 0.9882 - val_loss: 0.5604 - val_acc: 0.8670
Epoch 7/100
132/132 [==============================] - 79s 598ms/step - loss: 0.0250 - acc: 0.9948 - val_loss: 0.5984 - val_acc: 0.8899
Epoch 8/100
132/132 [==============================] - 79s 596ms/step - loss: 0.0316 - acc: 0.9900 - val_loss: 0.5923 - val_acc: 0.8578
Epoch 9/100
132/132 [==============================] - 79s 597ms/step - loss: 0.0368 - acc: 0.9902 - val_loss: 0.6497 - val_acc: 0.8716
Epoch 10/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0297 - acc: 0.9918 - val_loss: 0.5934 - val_acc: 0.8624
Epoch 11/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0341 - acc: 0.9907 - val_loss: 0.5626 - val_acc: 0.8945
Epoch 12/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0417 - acc: 0.9868 - val_loss: 0.6556 - val_acc: 0.8624
Epoch 13/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0361 - acc: 0.9892 - val_loss: 0.6176 - val_acc: 0.8670
Epoch 14/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0351 - acc: 0.9894 - val_loss: 0.5738 - val_acc: 0.8945
Epoch 15/100
132/132 [==============================] - 79s 597ms/step - loss: 0.0265 - acc: 0.9924 - val_loss: 0.5922 - val_acc: 0.8716
Epoch 16/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0270 - acc: 0.9921 - val_loss: 0.5998 - val_acc: 0.8899
Epoch 17/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0370 - acc: 0.9879 - val_loss: 0.5618 - val_acc: 0.8807
Epoch 18/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0267 - acc: 0.9920 - val_loss: 0.6622 - val_acc: 0.8716
Epoch 19/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0233 - acc: 0.9941 - val_loss: 0.6618 - val_acc: 0.8761
Epoch 20/100
132/132 [==============================] - 78s 589ms/step - loss: 0.0322 - acc: 0.9900 - val_loss: 0.7249 - val_acc: 0.8716
Epoch 21/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0260 - acc: 0.9927 - val_loss: 0.6183 - val_acc: 0.8945
Epoch 22/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0305 - acc: 0.9915 - val_loss: 0.6146 - val_acc: 0.8624
Epoch 23/100
132/132 [==============================] - 79s 596ms/step - loss: 0.0291 - acc: 0.9918 - val_loss: 0.6191 - val_acc: 0.8624
Epoch 24/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0269 - acc: 0.9941 - val_loss: 0.5978 - val_acc: 0.8761
Epoch 25/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0225 - acc: 0.9938 - val_loss: 0.7897 - val_acc: 0.8716
Epoch 26/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0261 - acc: 0.9924 - val_loss: 0.5597 - val_acc: 0.8853
Epoch 27/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0227 - acc: 0.9942 - val_loss: 0.7143 - val_acc: 0.8532
Epoch 28/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0257 - acc: 0.9932 - val_loss: 0.6000 - val_acc: 0.8945
Epoch 29/100
132/132 [==============================] - 78s 587ms/step - loss: 0.0266 - acc: 0.9914 - val_loss: 0.6118 - val_acc: 0.8670
Epoch 30/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0333 - acc: 0.9906 - val_loss: 0.5971 - val_acc: 0.8761
Epoch 31/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0248 - acc: 0.9935 - val_loss: 0.5546 - val_acc: 0.8716
Epoch 32/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0280 - acc: 0.9920 - val_loss: 0.7028 - val_acc: 0.8578
Epoch 33/100
132/132 [==============================] - 79s 596ms/step - loss: 0.0299 - acc: 0.9909 - val_loss: 0.5270 - val_acc: 0.8624
Epoch 34/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0250 - acc: 0.9920 - val_loss: 0.6029 - val_acc: 0.8807
Epoch 35/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0242 - acc: 0.9926 - val_loss: 0.7862 - val_acc: 0.8761
Epoch 36/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0223 - acc: 0.9938 - val_loss: 0.7077 - val_acc: 0.8807
Epoch 37/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0215 - acc: 0.9950 - val_loss: 0.6493 - val_acc: 0.8670
Epoch 38/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0273 - acc: 0.9917 - val_loss: 0.6820 - val_acc: 0.8807
Epoch 39/100
132/132 [==============================] - 79s 595ms/step - loss: 0.0225 - acc: 0.9948 - val_loss: 0.6664 - val_acc: 0.8945
Epoch 40/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0249 - acc: 0.9930 - val_loss: 0.5996 - val_acc: 0.8761
Epoch 41/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0253 - acc: 0.9920 - val_loss: 0.7107 - val_acc: 0.8624
Epoch 42/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0323 - acc: 0.9902 - val_loss: 0.7366 - val_acc: 0.8670
Epoch 43/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0232 - acc: 0.9936 - val_loss: 0.5950 - val_acc: 0.8716
Epoch 44/100
132/132 [==============================] - 79s 595ms/step - loss: 0.0242 - acc: 0.9929 - val_loss: 0.6480 - val_acc: 0.8716
Epoch 45/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0236 - acc: 0.9921 - val_loss: 0.5832 - val_acc: 0.8945
Epoch 46/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0225 - acc: 0.9920 - val_loss: 0.6868 - val_acc: 0.8578
Epoch 47/100
132/132 [==============================] - 77s 581ms/step - loss: 0.0206 - acc: 0.9944 - val_loss: 0.7327 - val_acc: 0.8624
Epoch 48/100
132/132 [==============================] - 77s 583ms/step - loss: 0.0196 - acc: 0.9942 - val_loss: 0.6338 - val_acc: 0.8899
Epoch 49/100
132/132 [==============================] - 77s 583ms/step - loss: 0.0200 - acc: 0.9941 - val_loss: 0.6406 - val_acc: 0.8761
Epoch 50/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0237 - acc: 0.9917 - val_loss: 0.5943 - val_acc: 0.8853
Epoch 51/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0213 - acc: 0.9945 - val_loss: 0.6790 - val_acc: 0.8853
Epoch 52/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0253 - acc: 0.9923 - val_loss: 0.6493 - val_acc: 0.8670
Epoch 53/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0234 - acc: 0.9927 - val_loss: 0.6139 - val_acc: 0.8716
Epoch 54/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0294 - acc: 0.9918 - val_loss: 0.7456 - val_acc: 0.8670
Epoch 55/100
132/132 [==============================] - 77s 585ms/step - loss: 0.0207 - acc: 0.9948 - val_loss: 0.6135 - val_acc: 0.8945
Epoch 56/100
132/132 [==============================] - 78s 589ms/step - loss: 0.0224 - acc: 0.9927 - val_loss: 0.5020 - val_acc: 0.8853
Epoch 57/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0191 - acc: 0.9947 - val_loss: 0.5357 - val_acc: 0.8807
Epoch 58/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0195 - acc: 0.9952 - val_loss: 0.6167 - val_acc: 0.8761
Epoch 59/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0265 - acc: 0.9900 - val_loss: 0.6206 - val_acc: 0.8853
Epoch 60/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0241 - acc: 0.9935 - val_loss: 0.5309 - val_acc: 0.8761
Epoch 61/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0250 - acc: 0.9908 - val_loss: 0.7685 - val_acc: 0.8486
Epoch 62/100
132/132 [==============================] - 78s 594ms/step - loss: 0.0411 - acc: 0.9858 - val_loss: 0.7001 - val_acc: 0.8624
Epoch 63/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0231 - acc: 0.9926 - val_loss: 0.5914 - val_acc: 0.8761
Epoch 64/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0204 - acc: 0.9942 - val_loss: 0.5970 - val_acc: 0.8670
Epoch 65/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0207 - acc: 0.9947 - val_loss: 0.5698 - val_acc: 0.8899
Epoch 66/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0208 - acc: 0.9926 - val_loss: 0.6255 - val_acc: 0.8945
Epoch 67/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0242 - acc: 0.9930 - val_loss: 0.6580 - val_acc: 0.8899
Epoch 68/100
132/132 [==============================] - 77s 583ms/step - loss: 0.0266 - acc: 0.9926 - val_loss: 0.7111 - val_acc: 0.8991
Epoch 69/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0179 - acc: 0.9952 - val_loss: 0.6328 - val_acc: 0.8945
Epoch 70/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0192 - acc: 0.9945 - val_loss: 0.6533 - val_acc: 0.8716
Epoch 71/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0187 - acc: 0.9947 - val_loss: 0.6593 - val_acc: 0.8716
Epoch 72/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0313 - acc: 0.9898 - val_loss: 0.6003 - val_acc: 0.8578
Epoch 73/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0193 - acc: 0.9947 - val_loss: 0.6097 - val_acc: 0.8807
Epoch 74/100
132/132 [==============================] - 77s 587ms/step - loss: 0.0150 - acc: 0.9962 - val_loss: 0.6034 - val_acc: 0.8761
Epoch 75/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0223 - acc: 0.9940 - val_loss: 0.6202 - val_acc: 0.8991
Epoch 76/100
132/132 [==============================] - 77s 585ms/step - loss: 0.0238 - acc: 0.9929 - val_loss: 0.6197 - val_acc: 0.8945
Epoch 77/100
132/132 [==============================] - 78s 588ms/step - loss: 0.0229 - acc: 0.9933 - val_loss: 0.6850 - val_acc: 0.8716
Epoch 78/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0300 - acc: 0.9920 - val_loss: 0.6726 - val_acc: 0.8716
Epoch 79/100
132/132 [==============================] - 78s 588ms/step - loss: 0.0176 - acc: 0.9942 - val_loss: 0.6480 - val_acc: 0.8670
Epoch 80/100
132/132 [==============================] - 78s 589ms/step - loss: 0.0200 - acc: 0.9947 - val_loss: 0.5865 - val_acc: 0.8716
Epoch 81/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0208 - acc: 0.9942 - val_loss: 0.6508 - val_acc: 0.8670
Epoch 82/100
132/132 [==============================] - 77s 585ms/step - loss: 0.0246 - acc: 0.9923 - val_loss: 0.5814 - val_acc: 0.8716
Epoch 83/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0174 - acc: 0.9952 - val_loss: 0.6696 - val_acc: 0.8807
Epoch 84/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0213 - acc: 0.9938 - val_loss: 0.6872 - val_acc: 0.8853
Epoch 85/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0129 - acc: 0.9965 - val_loss: 0.7105 - val_acc: 0.8761
Epoch 86/100
132/132 [==============================] - 79s 595ms/step - loss: 0.0148 - acc: 0.9959 - val_loss: 0.6290 - val_acc: 0.9037
Epoch 87/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0262 - acc: 0.9941 - val_loss: 0.6625 - val_acc: 0.8945
Epoch 88/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0209 - acc: 0.9933 - val_loss: 0.6209 - val_acc: 0.8716
Epoch 89/100
132/132 [==============================] - 78s 591ms/step - loss: 0.0212 - acc: 0.9941 - val_loss: 0.7767 - val_acc: 0.8761
Epoch 90/100
132/132 [==============================] - 78s 589ms/step - loss: 0.0261 - acc: 0.9915 - val_loss: 0.6874 - val_acc: 0.8624
Epoch 91/100
132/132 [==============================] - 78s 587ms/step - loss: 0.0219 - acc: 0.9921 - val_loss: 0.5792 - val_acc: 0.8945
Epoch 92/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0153 - acc: 0.9974 - val_loss: 0.6037 - val_acc: 0.8716
Epoch 93/100
132/132 [==============================] - 78s 590ms/step - loss: 0.0163 - acc: 0.9961 - val_loss: 0.6518 - val_acc: 0.8716
Epoch 94/100
132/132 [==============================] - 78s 595ms/step - loss: 0.0258 - acc: 0.9920 - val_loss: 0.6390 - val_acc: 0.8716
Epoch 95/100
132/132 [==============================] - 77s 585ms/step - loss: 0.0163 - acc: 0.9947 - val_loss: 0.7046 - val_acc: 0.8807
Epoch 96/100
132/132 [==============================] - 77s 584ms/step - loss: 0.0147 - acc: 0.9964 - val_loss: 0.6996 - val_acc: 0.8716
Epoch 97/100
132/132 [==============================] - 77s 583ms/step - loss: 0.0135 - acc: 0.9962 - val_loss: 0.6882 - val_acc: 0.8578
Epoch 98/100
132/132 [==============================] - 77s 586ms/step - loss: 0.0185 - acc: 0.9939 - val_loss: 0.7157 - val_acc: 0.8670
Epoch 99/100
132/132 [==============================] - 78s 593ms/step - loss: 0.0226 - acc: 0.9939 - val_loss: 0.5425 - val_acc: 0.8716
Epoch 100/100
132/132 [==============================] - 78s 592ms/step - loss: 0.0117 - acc: 0.9973 - val_loss: 0.5857 - val_acc: 0.8761
40/40 [==============================] - 7s 186ms/step
Accuracy: 0.8902996444895886. F1: 0.8890374104248979
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<div class="prompt input_prompt">In&nbsp;[27]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">make_modelLSTM</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;LSTM_norm&quot;</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 70s 529ms/step - loss: 1.4877 - acc: 0.4409 - val_loss: 1.0417 - val_acc: 0.5963
Epoch 2/500
132/132 [==============================] - 68s 516ms/step - loss: 0.9838 - acc: 0.6325 - val_loss: 0.8893 - val_acc: 0.6239
Epoch 3/500
132/132 [==============================] - 68s 515ms/step - loss: 0.9222 - acc: 0.6501 - val_loss: 0.9605 - val_acc: 0.6376
Epoch 4/500
132/132 [==============================] - 68s 514ms/step - loss: 0.8451 - acc: 0.6859 - val_loss: 0.8228 - val_acc: 0.7018
Epoch 5/500
132/132 [==============================] - 68s 516ms/step - loss: 0.7673 - acc: 0.7232 - val_loss: 0.8217 - val_acc: 0.6835
Epoch 6/500
132/132 [==============================] - 68s 516ms/step - loss: 0.8932 - acc: 0.6740 - val_loss: 0.8068 - val_acc: 0.6835
Epoch 7/500
132/132 [==============================] - 68s 515ms/step - loss: 0.7783 - acc: 0.7223 - val_loss: 0.7522 - val_acc: 0.7156
Epoch 8/500
132/132 [==============================] - 69s 521ms/step - loss: 0.7292 - acc: 0.7457 - val_loss: 0.8725 - val_acc: 0.6789
Epoch 9/500
132/132 [==============================] - 69s 526ms/step - loss: 0.7931 - acc: 0.7276 - val_loss: 0.9985 - val_acc: 0.6193
Epoch 10/500
132/132 [==============================] - 68s 517ms/step - loss: 0.7908 - acc: 0.7200 - val_loss: 0.7298 - val_acc: 0.7202
Epoch 11/500
132/132 [==============================] - 69s 519ms/step - loss: 0.7287 - acc: 0.7540 - val_loss: 0.7604 - val_acc: 0.7156
Epoch 12/500
132/132 [==============================] - 68s 515ms/step - loss: 0.7084 - acc: 0.7575 - val_loss: 0.7620 - val_acc: 0.7156
Epoch 13/500
132/132 [==============================] - 68s 516ms/step - loss: 0.8303 - acc: 0.7065 - val_loss: 0.9063 - val_acc: 0.6835
Epoch 14/500
132/132 [==============================] - 68s 516ms/step - loss: 0.7085 - acc: 0.7523 - val_loss: 0.7390 - val_acc: 0.7477
Epoch 15/500
132/132 [==============================] - 68s 516ms/step - loss: 0.8988 - acc: 0.7030 - val_loss: 0.9524 - val_acc: 0.6239
Epoch 16/500
132/132 [==============================] - 68s 518ms/step - loss: 0.8282 - acc: 0.7088 - val_loss: 0.7657 - val_acc: 0.7202
Epoch 17/500
132/132 [==============================] - 68s 517ms/step - loss: 0.6973 - acc: 0.7607 - val_loss: 0.7638 - val_acc: 0.7248
Epoch 18/500
132/132 [==============================] - 68s 517ms/step - loss: 0.6832 - acc: 0.7576 - val_loss: 0.9498 - val_acc: 0.6697
Epoch 19/500
132/132 [==============================] - 68s 518ms/step - loss: 0.6552 - acc: 0.7748 - val_loss: 0.7554 - val_acc: 0.7339
Epoch 20/500
132/132 [==============================] - 68s 518ms/step - loss: 0.6247 - acc: 0.7838 - val_loss: 0.6874 - val_acc: 0.7339
Epoch 21/500
132/132 [==============================] - 68s 516ms/step - loss: 0.6146 - acc: 0.7817 - val_loss: 0.7337 - val_acc: 0.6972
Epoch 22/500
132/132 [==============================] - 68s 515ms/step - loss: 0.6472 - acc: 0.7756 - val_loss: 0.7721 - val_acc: 0.7202
Epoch 23/500
132/132 [==============================] - 68s 516ms/step - loss: 0.6271 - acc: 0.7790 - val_loss: 0.7251 - val_acc: 0.7385
Epoch 24/500
132/132 [==============================] - 68s 516ms/step - loss: 0.6119 - acc: 0.7790 - val_loss: 0.7027 - val_acc: 0.7523
Epoch 25/500
132/132 [==============================] - 68s 516ms/step - loss: 0.5932 - acc: 0.7968 - val_loss: 0.6826 - val_acc: 0.7569
Epoch 26/500
132/132 [==============================] - 68s 516ms/step - loss: 0.6026 - acc: 0.7891 - val_loss: 0.8069 - val_acc: 0.7202
Epoch 27/500
132/132 [==============================] - 70s 527ms/step - loss: 0.5787 - acc: 0.7981 - val_loss: 0.7249 - val_acc: 0.7385
Epoch 28/500
132/132 [==============================] - 69s 526ms/step - loss: 0.6103 - acc: 0.7877 - val_loss: 0.7283 - val_acc: 0.7615
Epoch 29/500
132/132 [==============================] - 70s 529ms/step - loss: 0.5779 - acc: 0.7972 - val_loss: 0.6651 - val_acc: 0.7706
Epoch 30/500
132/132 [==============================] - 70s 532ms/step - loss: 0.5380 - acc: 0.8126 - val_loss: 0.7005 - val_acc: 0.7752
Epoch 31/500
132/132 [==============================] - 69s 527ms/step - loss: 0.5821 - acc: 0.8011 - val_loss: 0.6916 - val_acc: 0.7569
Epoch 32/500
132/132 [==============================] - 70s 530ms/step - loss: 0.5504 - acc: 0.8068 - val_loss: 0.6620 - val_acc: 0.7752
Epoch 33/500
132/132 [==============================] - 69s 521ms/step - loss: 0.5295 - acc: 0.8164 - val_loss: 0.7690 - val_acc: 0.7339
Epoch 34/500
132/132 [==============================] - 68s 518ms/step - loss: 0.6172 - acc: 0.7635 - val_loss: 0.7196 - val_acc: 0.7202
Epoch 35/500
132/132 [==============================] - 68s 516ms/step - loss: 0.5694 - acc: 0.7874 - val_loss: 0.7051 - val_acc: 0.7156
Epoch 36/500
132/132 [==============================] - 69s 521ms/step - loss: 0.5659 - acc: 0.7902 - val_loss: 0.7012 - val_acc: 0.7294
Epoch 37/500
132/132 [==============================] - 69s 521ms/step - loss: 0.5619 - acc: 0.7968 - val_loss: 0.6722 - val_acc: 0.7385
Epoch 38/500
132/132 [==============================] - 68s 517ms/step - loss: 0.5389 - acc: 0.8081 - val_loss: 0.6816 - val_acc: 0.7431
Epoch 39/500
132/132 [==============================] - 68s 516ms/step - loss: 0.5165 - acc: 0.8231 - val_loss: 0.6051 - val_acc: 0.8073
Epoch 40/500
132/132 [==============================] - 68s 517ms/step - loss: 0.5178 - acc: 0.8249 - val_loss: 0.6336 - val_acc: 0.7936
Epoch 41/500
132/132 [==============================] - 69s 519ms/step - loss: 0.4872 - acc: 0.8293 - val_loss: 0.5941 - val_acc: 0.7844
Epoch 42/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4909 - acc: 0.8290 - val_loss: 0.5461 - val_acc: 0.7890
Epoch 43/500
132/132 [==============================] - 68s 518ms/step - loss: 0.5315 - acc: 0.8185 - val_loss: 0.6411 - val_acc: 0.7844
Epoch 44/500
132/132 [==============================] - 68s 519ms/step - loss: 0.4993 - acc: 0.8290 - val_loss: 0.6280 - val_acc: 0.7890
Epoch 45/500
132/132 [==============================] - 69s 522ms/step - loss: 0.5578 - acc: 0.8137 - val_loss: 0.6668 - val_acc: 0.7844
Epoch 46/500
132/132 [==============================] - 70s 527ms/step - loss: 0.5252 - acc: 0.8199 - val_loss: 0.7361 - val_acc: 0.7569
Epoch 47/500
132/132 [==============================] - 75s 565ms/step - loss: 0.4918 - acc: 0.8305 - val_loss: 0.6588 - val_acc: 0.7844
Epoch 48/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4774 - acc: 0.8369 - val_loss: 0.8217 - val_acc: 0.7431
Epoch 49/500
132/132 [==============================] - 68s 513ms/step - loss: 0.4947 - acc: 0.8306 - val_loss: 0.5946 - val_acc: 0.7844
Epoch 50/500
132/132 [==============================] - 68s 515ms/step - loss: 0.4644 - acc: 0.8428 - val_loss: 0.6764 - val_acc: 0.7569
Epoch 51/500
132/132 [==============================] - 68s 514ms/step - loss: 0.4582 - acc: 0.8446 - val_loss: 0.5887 - val_acc: 0.7752
Epoch 52/500
132/132 [==============================] - 68s 513ms/step - loss: 0.4480 - acc: 0.8448 - val_loss: 0.6343 - val_acc: 0.7752
Epoch 53/500
132/132 [==============================] - 68s 516ms/step - loss: 0.4549 - acc: 0.8401 - val_loss: 0.5694 - val_acc: 0.8028
Epoch 54/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4265 - acc: 0.8520 - val_loss: 0.6285 - val_acc: 0.7844
Epoch 55/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4266 - acc: 0.8542 - val_loss: 0.5906 - val_acc: 0.7982
Epoch 56/500
132/132 [==============================] - 68s 515ms/step - loss: 0.4573 - acc: 0.8401 - val_loss: 0.6070 - val_acc: 0.7523
Epoch 57/500
132/132 [==============================] - 68s 515ms/step - loss: 0.4482 - acc: 0.8469 - val_loss: 0.7028 - val_acc: 0.7431
Epoch 58/500
132/132 [==============================] - 68s 515ms/step - loss: 0.4346 - acc: 0.8523 - val_loss: 0.5513 - val_acc: 0.8119
Epoch 59/500
132/132 [==============================] - 68s 515ms/step - loss: 0.4031 - acc: 0.8590 - val_loss: 0.5259 - val_acc: 0.8303
Epoch 60/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4572 - acc: 0.8429 - val_loss: 0.6416 - val_acc: 0.7982
Epoch 61/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4760 - acc: 0.8359 - val_loss: 0.6226 - val_acc: 0.7752
Epoch 62/500
132/132 [==============================] - 69s 522ms/step - loss: 0.4610 - acc: 0.8434 - val_loss: 0.5040 - val_acc: 0.8211
Epoch 63/500
132/132 [==============================] - 70s 530ms/step - loss: 0.4029 - acc: 0.8592 - val_loss: 0.5011 - val_acc: 0.8028
Epoch 64/500
132/132 [==============================] - 68s 519ms/step - loss: 0.4232 - acc: 0.8565 - val_loss: 0.5381 - val_acc: 0.8165
Epoch 65/500
132/132 [==============================] - 69s 520ms/step - loss: 0.4261 - acc: 0.8551 - val_loss: 0.5612 - val_acc: 0.8165
Epoch 66/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4422 - acc: 0.8494 - val_loss: 0.5171 - val_acc: 0.8165
Epoch 67/500
132/132 [==============================] - 69s 521ms/step - loss: 0.4290 - acc: 0.8598 - val_loss: 0.5535 - val_acc: 0.8073
Epoch 68/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4261 - acc: 0.8560 - val_loss: 0.5493 - val_acc: 0.8119
Epoch 69/500
132/132 [==============================] - 68s 517ms/step - loss: 0.4299 - acc: 0.8532 - val_loss: 0.6492 - val_acc: 0.7798
Epoch 70/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4529 - acc: 0.8466 - val_loss: 0.5992 - val_acc: 0.8028
Epoch 71/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4310 - acc: 0.8496 - val_loss: 0.5539 - val_acc: 0.8165
Epoch 72/500
132/132 [==============================] - 69s 522ms/step - loss: 0.4488 - acc: 0.8473 - val_loss: 0.6259 - val_acc: 0.7890
Epoch 73/500
132/132 [==============================] - 68s 519ms/step - loss: 0.4266 - acc: 0.8510 - val_loss: 0.6011 - val_acc: 0.8028
Epoch 74/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3973 - acc: 0.8642 - val_loss: 0.5526 - val_acc: 0.8349
Epoch 75/500
132/132 [==============================] - 68s 514ms/step - loss: 0.3750 - acc: 0.8679 - val_loss: 0.5596 - val_acc: 0.8303
Epoch 76/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3717 - acc: 0.8720 - val_loss: 0.5196 - val_acc: 0.8349
Epoch 77/500
132/132 [==============================] - 68s 516ms/step - loss: 0.3792 - acc: 0.8708 - val_loss: 0.5977 - val_acc: 0.8119
Epoch 78/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3794 - acc: 0.8673 - val_loss: 0.5721 - val_acc: 0.8394
Epoch 79/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3545 - acc: 0.8799 - val_loss: 0.4861 - val_acc: 0.8349
Epoch 80/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3936 - acc: 0.8670 - val_loss: 0.5117 - val_acc: 0.8211
Epoch 81/500
132/132 [==============================] - 69s 521ms/step - loss: 0.3826 - acc: 0.8681 - val_loss: 0.4941 - val_acc: 0.8486
Epoch 82/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4516 - acc: 0.8483 - val_loss: 0.4740 - val_acc: 0.8394
Epoch 83/500
132/132 [==============================] - 68s 516ms/step - loss: 0.4070 - acc: 0.8611 - val_loss: 0.6432 - val_acc: 0.8119
Epoch 84/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3876 - acc: 0.8666 - val_loss: 0.5155 - val_acc: 0.8349
Epoch 85/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3677 - acc: 0.8732 - val_loss: 0.5209 - val_acc: 0.8303
Epoch 86/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4127 - acc: 0.8625 - val_loss: 0.5236 - val_acc: 0.8119
Epoch 87/500
132/132 [==============================] - 71s 535ms/step - loss: 0.4388 - acc: 0.8521 - val_loss: 0.5613 - val_acc: 0.8349
Epoch 88/500
132/132 [==============================] - 73s 551ms/step - loss: 0.4174 - acc: 0.8555 - val_loss: 0.5471 - val_acc: 0.8073
Epoch 89/500
132/132 [==============================] - 69s 525ms/step - loss: 0.3685 - acc: 0.8706 - val_loss: 0.5315 - val_acc: 0.8394
Epoch 90/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3598 - acc: 0.8770 - val_loss: 0.5221 - val_acc: 0.8211
Epoch 91/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3589 - acc: 0.8751 - val_loss: 0.5524 - val_acc: 0.8257
Epoch 92/500
132/132 [==============================] - 68s 516ms/step - loss: 0.3719 - acc: 0.8717 - val_loss: 0.5018 - val_acc: 0.8257
Epoch 93/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3511 - acc: 0.8789 - val_loss: 0.4650 - val_acc: 0.8532
Epoch 94/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3805 - acc: 0.8690 - val_loss: 0.5016 - val_acc: 0.8486
Epoch 95/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3605 - acc: 0.8764 - val_loss: 0.5222 - val_acc: 0.8303
Epoch 96/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3506 - acc: 0.8788 - val_loss: 0.4935 - val_acc: 0.8624
Epoch 97/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3498 - acc: 0.8817 - val_loss: 0.5108 - val_acc: 0.8394
Epoch 98/500
132/132 [==============================] - 69s 522ms/step - loss: 0.3364 - acc: 0.8853 - val_loss: 0.4910 - val_acc: 0.8532
Epoch 99/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3347 - acc: 0.8838 - val_loss: 0.4889 - val_acc: 0.8624
Epoch 100/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3285 - acc: 0.8854 - val_loss: 0.4422 - val_acc: 0.8486
Epoch 101/500
132/132 [==============================] - 69s 519ms/step - loss: 0.4026 - acc: 0.8693 - val_loss: 0.6550 - val_acc: 0.7982
Epoch 102/500
132/132 [==============================] - 69s 520ms/step - loss: 0.4205 - acc: 0.8541 - val_loss: 0.4987 - val_acc: 0.8532
Epoch 103/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3737 - acc: 0.8697 - val_loss: 0.5745 - val_acc: 0.8211
Epoch 104/500
132/132 [==============================] - 69s 520ms/step - loss: 0.3653 - acc: 0.8751 - val_loss: 0.5370 - val_acc: 0.8486
Epoch 105/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3411 - acc: 0.8794 - val_loss: 0.4977 - val_acc: 0.8394
Epoch 106/500
132/132 [==============================] - 68s 516ms/step - loss: 0.3345 - acc: 0.8855 - val_loss: 0.4030 - val_acc: 0.8761
Epoch 107/500
132/132 [==============================] - 69s 521ms/step - loss: 0.3557 - acc: 0.8795 - val_loss: 0.5181 - val_acc: 0.8349
Epoch 108/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3308 - acc: 0.8847 - val_loss: 0.5085 - val_acc: 0.8440
Epoch 109/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3067 - acc: 0.8919 - val_loss: 0.4455 - val_acc: 0.8578
Epoch 110/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2957 - acc: 0.8962 - val_loss: 0.4685 - val_acc: 0.8624
Epoch 111/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3075 - acc: 0.8961 - val_loss: 0.4599 - val_acc: 0.8807
Epoch 112/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2998 - acc: 0.8938 - val_loss: 0.4586 - val_acc: 0.8807
Epoch 113/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3078 - acc: 0.8939 - val_loss: 0.4751 - val_acc: 0.8624
Epoch 114/500
132/132 [==============================] - 69s 520ms/step - loss: 0.3321 - acc: 0.8875 - val_loss: 0.5124 - val_acc: 0.8486
Epoch 115/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3961 - acc: 0.8633 - val_loss: 0.4728 - val_acc: 0.8394
Epoch 116/500
132/132 [==============================] - 69s 521ms/step - loss: 0.3320 - acc: 0.8876 - val_loss: 0.4532 - val_acc: 0.8761
Epoch 117/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3081 - acc: 0.8931 - val_loss: 0.4398 - val_acc: 0.8486
Epoch 118/500
132/132 [==============================] - 68s 519ms/step - loss: 0.5813 - acc: 0.8066 - val_loss: 0.8248 - val_acc: 0.7248
Epoch 119/500
132/132 [==============================] - 68s 518ms/step - loss: 0.5208 - acc: 0.8279 - val_loss: 0.5673 - val_acc: 0.8165
Epoch 120/500
132/132 [==============================] - 69s 519ms/step - loss: 0.4102 - acc: 0.8615 - val_loss: 0.5296 - val_acc: 0.8211
Epoch 121/500
132/132 [==============================] - 69s 520ms/step - loss: 0.4091 - acc: 0.8611 - val_loss: 0.5347 - val_acc: 0.8165
Epoch 122/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3586 - acc: 0.8740 - val_loss: 0.4458 - val_acc: 0.8532
Epoch 123/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3543 - acc: 0.8813 - val_loss: 0.4962 - val_acc: 0.8440
Epoch 124/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3149 - acc: 0.8925 - val_loss: 0.4749 - val_acc: 0.8440
Epoch 125/500
132/132 [==============================] - 69s 522ms/step - loss: 0.3306 - acc: 0.8840 - val_loss: 0.5136 - val_acc: 0.8486
Epoch 126/500
132/132 [==============================] - 68s 517ms/step - loss: 0.2942 - acc: 0.8981 - val_loss: 0.4751 - val_acc: 0.8486
Epoch 127/500
132/132 [==============================] - 68s 516ms/step - loss: 0.3016 - acc: 0.8973 - val_loss: 0.5224 - val_acc: 0.8578
Epoch 128/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3620 - acc: 0.8749 - val_loss: 0.5606 - val_acc: 0.8073
Epoch 129/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3272 - acc: 0.8826 - val_loss: 0.5363 - val_acc: 0.8486
Epoch 130/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3134 - acc: 0.8914 - val_loss: 0.5047 - val_acc: 0.8394
Epoch 131/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3527 - acc: 0.8802 - val_loss: 0.5644 - val_acc: 0.8394
Epoch 132/500
132/132 [==============================] - 69s 519ms/step - loss: 0.3252 - acc: 0.8863 - val_loss: 0.4875 - val_acc: 0.8670
Epoch 133/500
132/132 [==============================] - 69s 519ms/step - loss: 0.3303 - acc: 0.8878 - val_loss: 0.5171 - val_acc: 0.8486
Epoch 134/500
132/132 [==============================] - 69s 523ms/step - loss: 0.2949 - acc: 0.8970 - val_loss: 0.4680 - val_acc: 0.8624
Epoch 135/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2845 - acc: 0.9019 - val_loss: 0.5713 - val_acc: 0.8257
Epoch 136/500
132/132 [==============================] - 68s 517ms/step - loss: 0.2719 - acc: 0.9055 - val_loss: 0.4481 - val_acc: 0.8761
Epoch 137/500
132/132 [==============================] - 69s 521ms/step - loss: 0.3042 - acc: 0.8934 - val_loss: 0.5554 - val_acc: 0.8394
Epoch 138/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2892 - acc: 0.8987 - val_loss: 0.4455 - val_acc: 0.8899
Epoch 139/500
132/132 [==============================] - 68s 514ms/step - loss: 0.2944 - acc: 0.8957 - val_loss: 0.5819 - val_acc: 0.8349
Epoch 140/500
132/132 [==============================] - 68s 518ms/step - loss: 0.4805 - acc: 0.8376 - val_loss: 0.5353 - val_acc: 0.8119
Epoch 141/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3952 - acc: 0.8678 - val_loss: 0.4881 - val_acc: 0.8394
Epoch 142/500
132/132 [==============================] - 69s 522ms/step - loss: 0.3427 - acc: 0.8843 - val_loss: 0.4871 - val_acc: 0.8349
Epoch 143/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3287 - acc: 0.8843 - val_loss: 0.5005 - val_acc: 0.8303
Epoch 144/500
132/132 [==============================] - 68s 517ms/step - loss: 0.3796 - acc: 0.8755 - val_loss: 0.5843 - val_acc: 0.8028
Epoch 145/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3345 - acc: 0.8825 - val_loss: 0.5164 - val_acc: 0.8578
Epoch 146/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3002 - acc: 0.8945 - val_loss: 0.4542 - val_acc: 0.8578
Epoch 147/500
132/132 [==============================] - 69s 521ms/step - loss: 0.3109 - acc: 0.8900 - val_loss: 0.4655 - val_acc: 0.8761
Epoch 148/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2981 - acc: 0.8963 - val_loss: 0.4801 - val_acc: 0.8486
Epoch 149/500
132/132 [==============================] - 69s 521ms/step - loss: 0.2901 - acc: 0.8997 - val_loss: 0.4485 - val_acc: 0.8670
Epoch 150/500
132/132 [==============================] - 69s 521ms/step - loss: 0.2731 - acc: 0.9026 - val_loss: 0.4507 - val_acc: 0.8486
Epoch 151/500
132/132 [==============================] - 69s 523ms/step - loss: 0.3018 - acc: 0.8965 - val_loss: 0.4871 - val_acc: 0.8624
Epoch 152/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2857 - acc: 0.8995 - val_loss: 0.4715 - val_acc: 0.8624
Epoch 153/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3633 - acc: 0.8776 - val_loss: 0.4737 - val_acc: 0.8257
Epoch 154/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3004 - acc: 0.8968 - val_loss: 0.5322 - val_acc: 0.8165
Epoch 155/500
132/132 [==============================] - 68s 518ms/step - loss: 0.3228 - acc: 0.8851 - val_loss: 0.4026 - val_acc: 0.8578
Epoch 156/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2783 - acc: 0.9030 - val_loss: 0.3921 - val_acc: 0.8761
Epoch 157/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2592 - acc: 0.9083 - val_loss: 0.3625 - val_acc: 0.8899
Epoch 158/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2718 - acc: 0.9063 - val_loss: 0.4592 - val_acc: 0.8349
Epoch 159/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2730 - acc: 0.9054 - val_loss: 0.3830 - val_acc: 0.8853
Epoch 160/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2502 - acc: 0.9136 - val_loss: 0.3935 - val_acc: 0.8716
Epoch 161/500
132/132 [==============================] - 69s 523ms/step - loss: 0.2601 - acc: 0.9083 - val_loss: 0.3973 - val_acc: 0.8624
Epoch 162/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2818 - acc: 0.9010 - val_loss: 0.3865 - val_acc: 0.8716
Epoch 163/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2396 - acc: 0.9149 - val_loss: 0.4042 - val_acc: 0.8807
Epoch 164/500
132/132 [==============================] - 69s 521ms/step - loss: 0.2280 - acc: 0.9182 - val_loss: 0.4008 - val_acc: 0.8807
Epoch 165/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2355 - acc: 0.9176 - val_loss: 0.4605 - val_acc: 0.8670
Epoch 166/500
132/132 [==============================] - 68s 519ms/step - loss: 0.2464 - acc: 0.9150 - val_loss: 0.4043 - val_acc: 0.8807
Epoch 167/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2368 - acc: 0.9164 - val_loss: 0.4598 - val_acc: 0.8670
Epoch 168/500
132/132 [==============================] - 69s 523ms/step - loss: 0.2603 - acc: 0.9073 - val_loss: 0.3981 - val_acc: 0.8716
Epoch 169/500
132/132 [==============================] - 69s 521ms/step - loss: 0.2277 - acc: 0.9185 - val_loss: 0.3989 - val_acc: 0.8853
Epoch 170/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2381 - acc: 0.9175 - val_loss: 0.3739 - val_acc: 0.8761
Epoch 171/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2178 - acc: 0.9227 - val_loss: 0.4656 - val_acc: 0.8716
Epoch 172/500
132/132 [==============================] - 69s 520ms/step - loss: 0.3089 - acc: 0.8994 - val_loss: 0.4980 - val_acc: 0.8486
Epoch 173/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2451 - acc: 0.9133 - val_loss: 0.4528 - val_acc: 0.8578
Epoch 174/500
132/132 [==============================] - 68s 519ms/step - loss: 0.2350 - acc: 0.9161 - val_loss: 0.4948 - val_acc: 0.8670
Epoch 175/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2286 - acc: 0.9206 - val_loss: 0.4855 - val_acc: 0.8670
Epoch 176/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2131 - acc: 0.9236 - val_loss: 0.4164 - val_acc: 0.8899
Epoch 177/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2372 - acc: 0.9169 - val_loss: 0.4967 - val_acc: 0.8303
Epoch 178/500
132/132 [==============================] - 69s 522ms/step - loss: 0.2474 - acc: 0.9141 - val_loss: 0.4467 - val_acc: 0.8624
Epoch 179/500
132/132 [==============================] - 68s 519ms/step - loss: 0.2634 - acc: 0.9094 - val_loss: 0.5041 - val_acc: 0.8394
Epoch 180/500
132/132 [==============================] - 68s 519ms/step - loss: 0.2759 - acc: 0.9041 - val_loss: 0.3754 - val_acc: 0.8853
Epoch 181/500
132/132 [==============================] - 69s 522ms/step - loss: 0.2632 - acc: 0.9073 - val_loss: 0.4349 - val_acc: 0.8624
Epoch 182/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2417 - acc: 0.9136 - val_loss: 0.5491 - val_acc: 0.8578
Epoch 183/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2602 - acc: 0.9082 - val_loss: 0.4700 - val_acc: 0.8761
Epoch 184/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2167 - acc: 0.9227 - val_loss: 0.3934 - val_acc: 0.8945
Epoch 185/500
132/132 [==============================] - 67s 511ms/step - loss: 0.2021 - acc: 0.9242 - val_loss: 0.3818 - val_acc: 0.8853
Epoch 186/500
132/132 [==============================] - 68s 519ms/step - loss: 0.2468 - acc: 0.9125 - val_loss: 0.4971 - val_acc: 0.7982
Epoch 187/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2682 - acc: 0.9045 - val_loss: 0.4535 - val_acc: 0.8578
Epoch 188/500
132/132 [==============================] - 68s 519ms/step - loss: 0.3053 - acc: 0.8997 - val_loss: 0.4248 - val_acc: 0.8670
Epoch 189/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2626 - acc: 0.9097 - val_loss: 0.4048 - val_acc: 0.8716
Epoch 190/500
132/132 [==============================] - 69s 522ms/step - loss: 0.2325 - acc: 0.9182 - val_loss: 0.4587 - val_acc: 0.8670
Epoch 191/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2019 - acc: 0.9247 - val_loss: 0.4226 - val_acc: 0.8624
Epoch 192/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2015 - acc: 0.9312 - val_loss: 0.4248 - val_acc: 0.8716
Epoch 193/500
132/132 [==============================] - 69s 520ms/step - loss: 0.1941 - acc: 0.9301 - val_loss: 0.3855 - val_acc: 0.8807
Epoch 194/500
132/132 [==============================] - 69s 520ms/step - loss: 0.2149 - acc: 0.9253 - val_loss: 0.4564 - val_acc: 0.8761
Epoch 195/500
132/132 [==============================] - 70s 532ms/step - loss: 0.2114 - acc: 0.9280 - val_loss: 0.5301 - val_acc: 0.8578
Epoch 196/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2034 - acc: 0.9247 - val_loss: 0.3759 - val_acc: 0.8761
Epoch 197/500
132/132 [==============================] - 68s 515ms/step - loss: 0.2053 - acc: 0.9286 - val_loss: 0.6034 - val_acc: 0.8440
Epoch 198/500
132/132 [==============================] - 69s 519ms/step - loss: 0.1985 - acc: 0.9303 - val_loss: 0.4686 - val_acc: 0.8807
Epoch 199/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2013 - acc: 0.9294 - val_loss: 0.4586 - val_acc: 0.8807
Epoch 200/500
132/132 [==============================] - 68s 517ms/step - loss: 0.1981 - acc: 0.9275 - val_loss: 0.6364 - val_acc: 0.8394
Epoch 201/500
132/132 [==============================] - 68s 516ms/step - loss: 0.2354 - acc: 0.9198 - val_loss: 0.4933 - val_acc: 0.8578
Epoch 202/500
132/132 [==============================] - 68s 516ms/step - loss: 0.2699 - acc: 0.9089 - val_loss: 0.4577 - val_acc: 0.8624
Epoch 203/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2181 - acc: 0.9221 - val_loss: 0.4899 - val_acc: 0.8486
Epoch 204/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2286 - acc: 0.9198 - val_loss: 0.4880 - val_acc: 0.8532
Epoch 205/500
132/132 [==============================] - 68s 516ms/step - loss: 0.2293 - acc: 0.9179 - val_loss: 0.4827 - val_acc: 0.8716
Epoch 206/500
132/132 [==============================] - 68s 517ms/step - loss: 0.2379 - acc: 0.9167 - val_loss: 0.4676 - val_acc: 0.8532
Epoch 207/500
132/132 [==============================] - 68s 518ms/step - loss: 0.1968 - acc: 0.9329 - val_loss: 0.4269 - val_acc: 0.8853
Epoch 208/500
132/132 [==============================] - 68s 517ms/step - loss: 0.1786 - acc: 0.9330 - val_loss: 0.4674 - val_acc: 0.8716
Epoch 209/500
132/132 [==============================] - 69s 521ms/step - loss: 0.1823 - acc: 0.9341 - val_loss: 0.4737 - val_acc: 0.8624
Epoch 210/500
132/132 [==============================] - 69s 519ms/step - loss: 0.2032 - acc: 0.9268 - val_loss: 0.7722 - val_acc: 0.8165
Epoch 211/500
132/132 [==============================] - 68s 515ms/step - loss: 0.2927 - acc: 0.8992 - val_loss: 0.4734 - val_acc: 0.8532
Epoch 212/500
132/132 [==============================] - 68s 514ms/step - loss: 0.2302 - acc: 0.9165 - val_loss: 0.6355 - val_acc: 0.8211
Epoch 213/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2272 - acc: 0.9207 - val_loss: 0.4929 - val_acc: 0.8761
Epoch 214/500
132/132 [==============================] - 67s 510ms/step - loss: 0.2099 - acc: 0.9258 - val_loss: 0.4612 - val_acc: 0.8761
Epoch 215/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1867 - acc: 0.9347 - val_loss: 0.4755 - val_acc: 0.8716
Epoch 216/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1776 - acc: 0.9383 - val_loss: 0.4094 - val_acc: 0.8807
Epoch 217/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1838 - acc: 0.9345 - val_loss: 0.5179 - val_acc: 0.8670
Epoch 218/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1792 - acc: 0.9341 - val_loss: 0.4451 - val_acc: 0.8624
Epoch 219/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1620 - acc: 0.9414 - val_loss: 0.4999 - val_acc: 0.8670
Epoch 220/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2434 - acc: 0.9151 - val_loss: 0.3839 - val_acc: 0.8807
Epoch 221/500
132/132 [==============================] - 67s 510ms/step - loss: 0.3205 - acc: 0.8969 - val_loss: 0.4876 - val_acc: 0.8624
Epoch 222/500
132/132 [==============================] - 68s 514ms/step - loss: 0.2173 - acc: 0.9241 - val_loss: 0.4689 - val_acc: 0.8624
Epoch 223/500
132/132 [==============================] - 67s 510ms/step - loss: 0.2582 - acc: 0.9070 - val_loss: 0.5123 - val_acc: 0.8532
Epoch 224/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2696 - acc: 0.9070 - val_loss: 0.4293 - val_acc: 0.8716
Epoch 225/500
132/132 [==============================] - 68s 514ms/step - loss: 0.2058 - acc: 0.9280 - val_loss: 0.4715 - val_acc: 0.8624
Epoch 226/500
132/132 [==============================] - 68s 514ms/step - loss: 0.1998 - acc: 0.9283 - val_loss: 0.4609 - val_acc: 0.8761
Epoch 227/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1857 - acc: 0.9348 - val_loss: 0.4362 - val_acc: 0.8945
Epoch 228/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1712 - acc: 0.9403 - val_loss: 0.4657 - val_acc: 0.8578
Epoch 229/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1691 - acc: 0.9380 - val_loss: 0.3909 - val_acc: 0.8761
Epoch 230/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1539 - acc: 0.9448 - val_loss: 0.4011 - val_acc: 0.8853
Epoch 231/500
132/132 [==============================] - 68s 516ms/step - loss: 0.2871 - acc: 0.9188 - val_loss: 0.7621 - val_acc: 0.7752
Epoch 232/500
132/132 [==============================] - 68s 513ms/step - loss: 0.3750 - acc: 0.8734 - val_loss: 0.4213 - val_acc: 0.8716
Epoch 233/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2891 - acc: 0.8983 - val_loss: 0.4899 - val_acc: 0.8670
Epoch 234/500
132/132 [==============================] - 68s 513ms/step - loss: 0.2408 - acc: 0.9148 - val_loss: 0.4691 - val_acc: 0.8807
Epoch 235/500
132/132 [==============================] - 68s 514ms/step - loss: 0.2384 - acc: 0.9155 - val_loss: 0.4092 - val_acc: 0.8807
Epoch 236/500
132/132 [==============================] - 68s 511ms/step - loss: 0.1831 - acc: 0.9356 - val_loss: 0.4104 - val_acc: 0.8899
Epoch 237/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2079 - acc: 0.9273 - val_loss: 0.5952 - val_acc: 0.8670
Epoch 238/500
132/132 [==============================] - 67s 511ms/step - loss: 0.2379 - acc: 0.9197 - val_loss: 0.4847 - val_acc: 0.8716
Epoch 239/500
132/132 [==============================] - 67s 511ms/step - loss: 0.2029 - acc: 0.9301 - val_loss: 0.4465 - val_acc: 0.8807
Epoch 240/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1911 - acc: 0.9333 - val_loss: 0.4353 - val_acc: 0.8945
Epoch 241/500
132/132 [==============================] - 67s 510ms/step - loss: 0.2161 - acc: 0.9260 - val_loss: 0.5946 - val_acc: 0.8211
Epoch 242/500
132/132 [==============================] - 67s 508ms/step - loss: 0.2093 - acc: 0.9261 - val_loss: 0.4640 - val_acc: 0.8761
Epoch 243/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1710 - acc: 0.9397 - val_loss: 0.3934 - val_acc: 0.8761
Epoch 244/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1617 - acc: 0.9435 - val_loss: 0.5318 - val_acc: 0.8624
Epoch 245/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1758 - acc: 0.9397 - val_loss: 0.3966 - val_acc: 0.8761
Epoch 246/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1433 - acc: 0.9464 - val_loss: 0.5042 - val_acc: 0.8670
Epoch 247/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1520 - acc: 0.9432 - val_loss: 0.5171 - val_acc: 0.8761
Epoch 248/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1576 - acc: 0.9442 - val_loss: 0.5430 - val_acc: 0.8716
Epoch 249/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1779 - acc: 0.9358 - val_loss: 0.4432 - val_acc: 0.8670
Epoch 250/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1686 - acc: 0.9378 - val_loss: 0.5270 - val_acc: 0.8853
Epoch 251/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1517 - acc: 0.9429 - val_loss: 0.4412 - val_acc: 0.8899
Epoch 252/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1561 - acc: 0.9462 - val_loss: 0.4529 - val_acc: 0.8899
Epoch 253/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1619 - acc: 0.9415 - val_loss: 0.5505 - val_acc: 0.8716
Epoch 254/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1381 - acc: 0.9483 - val_loss: 0.4810 - val_acc: 0.8853
Epoch 255/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1531 - acc: 0.9453 - val_loss: 0.4352 - val_acc: 0.8853
Epoch 256/500
132/132 [==============================] - 68s 514ms/step - loss: 0.1445 - acc: 0.9471 - val_loss: 0.5280 - val_acc: 0.8624
Epoch 257/500
132/132 [==============================] - 68s 511ms/step - loss: 0.1608 - acc: 0.9415 - val_loss: 0.5165 - val_acc: 0.8807
Epoch 258/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1476 - acc: 0.9461 - val_loss: 0.6056 - val_acc: 0.8670
Epoch 259/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1558 - acc: 0.9439 - val_loss: 0.5126 - val_acc: 0.8716
Epoch 260/500
132/132 [==============================] - 68s 511ms/step - loss: 0.2576 - acc: 0.9171 - val_loss: 0.4178 - val_acc: 0.8899
Epoch 261/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1610 - acc: 0.9424 - val_loss: 0.4065 - val_acc: 0.8991
Epoch 262/500
132/132 [==============================] - 68s 512ms/step - loss: 0.2384 - acc: 0.9214 - val_loss: 0.4738 - val_acc: 0.8807
Epoch 263/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1809 - acc: 0.9347 - val_loss: 0.4627 - val_acc: 0.8807
Epoch 264/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1490 - acc: 0.9480 - val_loss: 0.5462 - val_acc: 0.8624
Epoch 265/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1777 - acc: 0.9359 - val_loss: 0.5646 - val_acc: 0.8532
Epoch 266/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1608 - acc: 0.9438 - val_loss: 0.4595 - val_acc: 0.8807
Epoch 267/500
132/132 [==============================] - 68s 515ms/step - loss: 0.1448 - acc: 0.9501 - val_loss: 0.4624 - val_acc: 0.8761
Epoch 268/500
132/132 [==============================] - 67s 509ms/step - loss: 0.2080 - acc: 0.9297 - val_loss: 0.6528 - val_acc: 0.8440
Epoch 269/500
132/132 [==============================] - 68s 515ms/step - loss: 0.2179 - acc: 0.9267 - val_loss: 0.4977 - val_acc: 0.8807
Epoch 270/500
132/132 [==============================] - 67s 510ms/step - loss: 0.3250 - acc: 0.9006 - val_loss: 0.5128 - val_acc: 0.8578
Epoch 271/500
132/132 [==============================] - 68s 518ms/step - loss: 0.2166 - acc: 0.9279 - val_loss: 0.5429 - val_acc: 0.8578
Epoch 272/500
132/132 [==============================] - 67s 510ms/step - loss: 0.2061 - acc: 0.9289 - val_loss: 0.4805 - val_acc: 0.8670
Epoch 273/500
132/132 [==============================] - 68s 514ms/step - loss: 0.1636 - acc: 0.9412 - val_loss: 0.4954 - val_acc: 0.8486
Epoch 274/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1436 - acc: 0.9514 - val_loss: 0.5052 - val_acc: 0.8578
Epoch 275/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1438 - acc: 0.9509 - val_loss: 0.4381 - val_acc: 0.8899
Epoch 276/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1563 - acc: 0.9425 - val_loss: 0.4854 - val_acc: 0.8761
Epoch 277/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1540 - acc: 0.9476 - val_loss: 0.5752 - val_acc: 0.8624
Epoch 278/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1421 - acc: 0.9498 - val_loss: 0.5209 - val_acc: 0.8486
Epoch 279/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1949 - acc: 0.9371 - val_loss: 0.5554 - val_acc: 0.8394
Epoch 280/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1518 - acc: 0.9455 - val_loss: 0.5304 - val_acc: 0.8578
Epoch 281/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1373 - acc: 0.9524 - val_loss: 0.5183 - val_acc: 0.8899
Epoch 282/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1326 - acc: 0.9515 - val_loss: 0.5888 - val_acc: 0.8578
Epoch 283/500
132/132 [==============================] - 68s 517ms/step - loss: 0.1508 - acc: 0.9464 - val_loss: 0.6534 - val_acc: 0.8394
Epoch 284/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1290 - acc: 0.9553 - val_loss: 0.6307 - val_acc: 0.8440
Epoch 285/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1232 - acc: 0.9523 - val_loss: 0.6294 - val_acc: 0.8578
Epoch 286/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1723 - acc: 0.9438 - val_loss: 0.5287 - val_acc: 0.8624
Epoch 287/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1871 - acc: 0.9361 - val_loss: 0.5480 - val_acc: 0.8670
Epoch 288/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1429 - acc: 0.9479 - val_loss: 0.5592 - val_acc: 0.8578
Epoch 289/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1234 - acc: 0.9568 - val_loss: 0.6306 - val_acc: 0.8394
Epoch 290/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1173 - acc: 0.9595 - val_loss: 0.5926 - val_acc: 0.8624
Epoch 291/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1300 - acc: 0.9516 - val_loss: 0.5633 - val_acc: 0.8716
Epoch 292/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1539 - acc: 0.9450 - val_loss: 0.6616 - val_acc: 0.8486
Epoch 293/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1507 - acc: 0.9478 - val_loss: 0.6367 - val_acc: 0.8257
Epoch 294/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1413 - acc: 0.9521 - val_loss: 0.6778 - val_acc: 0.8394
Epoch 295/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1206 - acc: 0.9561 - val_loss: 0.6064 - val_acc: 0.8440
Epoch 296/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1568 - acc: 0.9476 - val_loss: 0.6351 - val_acc: 0.8486
Epoch 297/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1585 - acc: 0.9455 - val_loss: 0.6150 - val_acc: 0.8578
Epoch 298/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1285 - acc: 0.9545 - val_loss: 0.4617 - val_acc: 0.8807
Epoch 299/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1356 - acc: 0.9524 - val_loss: 0.6146 - val_acc: 0.8486
Epoch 300/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0977 - acc: 0.9648 - val_loss: 0.6657 - val_acc: 0.8624
Epoch 301/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1231 - acc: 0.9573 - val_loss: 0.6698 - val_acc: 0.8394
Epoch 302/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1180 - acc: 0.9570 - val_loss: 0.6192 - val_acc: 0.8440
Epoch 303/500
132/132 [==============================] - 68s 513ms/step - loss: 0.1116 - acc: 0.9603 - val_loss: 0.6135 - val_acc: 0.8578
Epoch 304/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1095 - acc: 0.9617 - val_loss: 0.6666 - val_acc: 0.8394
Epoch 305/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1816 - acc: 0.9360 - val_loss: 0.4928 - val_acc: 0.8853
Epoch 306/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1982 - acc: 0.9330 - val_loss: 0.5743 - val_acc: 0.8716
Epoch 307/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1532 - acc: 0.9458 - val_loss: 0.5782 - val_acc: 0.8670
Epoch 308/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1129 - acc: 0.9605 - val_loss: 0.5303 - val_acc: 0.8807
Epoch 309/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1421 - acc: 0.9480 - val_loss: 0.5966 - val_acc: 0.8440
Epoch 310/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1017 - acc: 0.9638 - val_loss: 0.6250 - val_acc: 0.8394
Epoch 311/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1034 - acc: 0.9627 - val_loss: 0.5932 - val_acc: 0.8578
Epoch 312/500
132/132 [==============================] - 68s 514ms/step - loss: 0.0950 - acc: 0.9661 - val_loss: 0.6402 - val_acc: 0.8532
Epoch 313/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0934 - acc: 0.9648 - val_loss: 0.6901 - val_acc: 0.8670
Epoch 314/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1576 - acc: 0.9480 - val_loss: 0.6433 - val_acc: 0.8394
Epoch 315/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1378 - acc: 0.9498 - val_loss: 0.6957 - val_acc: 0.8532
Epoch 316/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0964 - acc: 0.9630 - val_loss: 0.7067 - val_acc: 0.8349
Epoch 317/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1075 - acc: 0.9615 - val_loss: 0.6249 - val_acc: 0.8532
Epoch 318/500
132/132 [==============================] - 67s 510ms/step - loss: 0.1580 - acc: 0.9481 - val_loss: 0.7222 - val_acc: 0.7982
Epoch 319/500
132/132 [==============================] - 67s 508ms/step - loss: 0.2429 - acc: 0.9175 - val_loss: 0.5788 - val_acc: 0.8486
Epoch 320/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1607 - acc: 0.9408 - val_loss: 0.5455 - val_acc: 0.8716
Epoch 321/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1110 - acc: 0.9606 - val_loss: 0.5495 - val_acc: 0.8624
Epoch 322/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1023 - acc: 0.9630 - val_loss: 0.6516 - val_acc: 0.8394
Epoch 323/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1062 - acc: 0.9644 - val_loss: 0.6112 - val_acc: 0.8670
Epoch 324/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0982 - acc: 0.9635 - val_loss: 0.5594 - val_acc: 0.8440
Epoch 325/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0969 - acc: 0.9670 - val_loss: 0.6127 - val_acc: 0.8578
Epoch 326/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0964 - acc: 0.9667 - val_loss: 0.5827 - val_acc: 0.8807
Epoch 327/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0924 - acc: 0.9685 - val_loss: 0.6710 - val_acc: 0.8670
Epoch 328/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1156 - acc: 0.9618 - val_loss: 0.6712 - val_acc: 0.8578
Epoch 329/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0894 - acc: 0.9668 - val_loss: 0.7018 - val_acc: 0.8532
Epoch 330/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0873 - acc: 0.9688 - val_loss: 0.6185 - val_acc: 0.8486
Epoch 331/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0838 - acc: 0.9712 - val_loss: 0.7148 - val_acc: 0.8349
Epoch 332/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0768 - acc: 0.9742 - val_loss: 0.6597 - val_acc: 0.8394
Epoch 333/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0767 - acc: 0.9714 - val_loss: 0.7115 - val_acc: 0.8394
Epoch 334/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0850 - acc: 0.9703 - val_loss: 0.5945 - val_acc: 0.8624
Epoch 335/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0916 - acc: 0.9667 - val_loss: 0.6270 - val_acc: 0.8670
Epoch 336/500
132/132 [==============================] - 67s 511ms/step - loss: 0.0960 - acc: 0.9671 - val_loss: 0.6916 - val_acc: 0.8624
Epoch 337/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0947 - acc: 0.9677 - val_loss: 0.6551 - val_acc: 0.8761
Epoch 338/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1680 - acc: 0.9508 - val_loss: 0.7232 - val_acc: 0.8440
Epoch 339/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1476 - acc: 0.9468 - val_loss: 0.5886 - val_acc: 0.8486
Epoch 340/500
132/132 [==============================] - 67s 506ms/step - loss: 0.1019 - acc: 0.9673 - val_loss: 0.7192 - val_acc: 0.8532
Epoch 341/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1193 - acc: 0.9598 - val_loss: 0.6396 - val_acc: 0.8532
Epoch 342/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1247 - acc: 0.9570 - val_loss: 0.6091 - val_acc: 0.8624
Epoch 343/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0663 - acc: 0.9770 - val_loss: 0.6259 - val_acc: 0.8853
Epoch 344/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1011 - acc: 0.9676 - val_loss: 0.5577 - val_acc: 0.8624
Epoch 345/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1536 - acc: 0.9483 - val_loss: 0.5993 - val_acc: 0.8624
Epoch 346/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0990 - acc: 0.9670 - val_loss: 0.4696 - val_acc: 0.8807
Epoch 347/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0728 - acc: 0.9771 - val_loss: 0.6707 - val_acc: 0.8394
Epoch 348/500
132/132 [==============================] - 67s 511ms/step - loss: 0.0712 - acc: 0.9739 - val_loss: 0.4488 - val_acc: 0.9037
Epoch 349/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0697 - acc: 0.9747 - val_loss: 0.6144 - val_acc: 0.8532
Epoch 350/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0630 - acc: 0.9777 - val_loss: 0.7588 - val_acc: 0.8578
Epoch 351/500
132/132 [==============================] - 67s 508ms/step - loss: 0.3972 - acc: 0.8921 - val_loss: 0.5587 - val_acc: 0.8349
Epoch 352/500
132/132 [==============================] - 67s 511ms/step - loss: 0.3892 - acc: 0.8717 - val_loss: 0.5738 - val_acc: 0.8394
Epoch 353/500
132/132 [==============================] - 67s 509ms/step - loss: 0.2517 - acc: 0.9111 - val_loss: 0.5328 - val_acc: 0.8578
Epoch 354/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1553 - acc: 0.9450 - val_loss: 0.5431 - val_acc: 0.8670
Epoch 355/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1106 - acc: 0.9629 - val_loss: 0.6392 - val_acc: 0.8440
Epoch 356/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1017 - acc: 0.9665 - val_loss: 0.6689 - val_acc: 0.8578
Epoch 357/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1035 - acc: 0.9655 - val_loss: 0.6480 - val_acc: 0.8624
Epoch 358/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1020 - acc: 0.9665 - val_loss: 0.6352 - val_acc: 0.8670
Epoch 359/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0683 - acc: 0.9767 - val_loss: 0.6905 - val_acc: 0.8578
Epoch 360/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0642 - acc: 0.9791 - val_loss: 0.7074 - val_acc: 0.8716
Epoch 361/500
132/132 [==============================] - 67s 511ms/step - loss: 0.0680 - acc: 0.9753 - val_loss: 0.7571 - val_acc: 0.8578
Epoch 362/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0610 - acc: 0.9811 - val_loss: 0.7410 - val_acc: 0.8578
Epoch 363/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0586 - acc: 0.9800 - val_loss: 0.7607 - val_acc: 0.8624
Epoch 364/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0786 - acc: 0.9742 - val_loss: 0.5611 - val_acc: 0.8807
Epoch 365/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0739 - acc: 0.9744 - val_loss: 0.6306 - val_acc: 0.8716
Epoch 366/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0661 - acc: 0.9771 - val_loss: 0.6048 - val_acc: 0.8670
Epoch 367/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1893 - acc: 0.9405 - val_loss: 0.4573 - val_acc: 0.8899
Epoch 368/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1120 - acc: 0.9617 - val_loss: 0.5539 - val_acc: 0.8761
Epoch 369/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0681 - acc: 0.9783 - val_loss: 0.6221 - val_acc: 0.8440
Epoch 370/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0621 - acc: 0.9795 - val_loss: 0.5606 - val_acc: 0.8761
Epoch 371/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0651 - acc: 0.9764 - val_loss: 0.5543 - val_acc: 0.8761
Epoch 372/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1027 - acc: 0.9664 - val_loss: 0.6453 - val_acc: 0.8578
Epoch 373/500
132/132 [==============================] - 67s 504ms/step - loss: 0.0719 - acc: 0.9777 - val_loss: 0.6160 - val_acc: 0.8716
Epoch 374/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0967 - acc: 0.9667 - val_loss: 0.5695 - val_acc: 0.8761
Epoch 375/500
132/132 [==============================] - 68s 512ms/step - loss: 0.0937 - acc: 0.9662 - val_loss: 0.5473 - val_acc: 0.8716
Epoch 376/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0654 - acc: 0.9765 - val_loss: 0.5782 - val_acc: 0.8853
Epoch 377/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1177 - acc: 0.9640 - val_loss: 0.6789 - val_acc: 0.8486
Epoch 378/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0992 - acc: 0.9683 - val_loss: 0.5628 - val_acc: 0.8716
Epoch 379/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1133 - acc: 0.9631 - val_loss: 0.7003 - val_acc: 0.8073
Epoch 380/500
132/132 [==============================] - 67s 504ms/step - loss: 0.3105 - acc: 0.9018 - val_loss: 0.5031 - val_acc: 0.8440
Epoch 381/500
132/132 [==============================] - 67s 508ms/step - loss: 0.2036 - acc: 0.9320 - val_loss: 0.3735 - val_acc: 0.8716
Epoch 382/500
132/132 [==============================] - 67s 506ms/step - loss: 0.1412 - acc: 0.9473 - val_loss: 0.4550 - val_acc: 0.8761
Epoch 383/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1182 - acc: 0.9592 - val_loss: 0.3494 - val_acc: 0.8991
Epoch 384/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1240 - acc: 0.9604 - val_loss: 0.7033 - val_acc: 0.8761
Epoch 385/500
132/132 [==============================] - 67s 506ms/step - loss: 0.1428 - acc: 0.9545 - val_loss: 0.4410 - val_acc: 0.8716
Epoch 386/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0897 - acc: 0.9688 - val_loss: 0.6209 - val_acc: 0.8578
Epoch 387/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1356 - acc: 0.9555 - val_loss: 0.5158 - val_acc: 0.8532
Epoch 388/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0807 - acc: 0.9720 - val_loss: 0.4869 - val_acc: 0.8761
Epoch 389/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0733 - acc: 0.9755 - val_loss: 0.4833 - val_acc: 0.8899
Epoch 390/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0571 - acc: 0.9798 - val_loss: 0.5161 - val_acc: 0.8899
Epoch 391/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0732 - acc: 0.9767 - val_loss: 0.4615 - val_acc: 0.8945
Epoch 392/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0677 - acc: 0.9759 - val_loss: 0.5878 - val_acc: 0.8945
Epoch 393/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0591 - acc: 0.9792 - val_loss: 0.5250 - val_acc: 0.8716
Epoch 394/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0521 - acc: 0.9809 - val_loss: 0.7375 - val_acc: 0.8486
Epoch 395/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1029 - acc: 0.9676 - val_loss: 0.6319 - val_acc: 0.8394
Epoch 396/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1944 - acc: 0.9376 - val_loss: 0.5608 - val_acc: 0.8578
Epoch 397/500
132/132 [==============================] - 67s 507ms/step - loss: 0.1164 - acc: 0.9618 - val_loss: 0.6148 - val_acc: 0.8532
Epoch 398/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0706 - acc: 0.9786 - val_loss: 0.5968 - val_acc: 0.8807
Epoch 399/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1204 - acc: 0.9632 - val_loss: 0.5360 - val_acc: 0.8532
Epoch 400/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0538 - acc: 0.9833 - val_loss: 0.5756 - val_acc: 0.8899
Epoch 401/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0339 - acc: 0.9900 - val_loss: 0.6035 - val_acc: 0.8945
Epoch 402/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0648 - acc: 0.9792 - val_loss: 0.6125 - val_acc: 0.8532
Epoch 403/500
132/132 [==============================] - 67s 509ms/step - loss: 0.1344 - acc: 0.9541 - val_loss: 0.5853 - val_acc: 0.8670
Epoch 404/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0791 - acc: 0.9718 - val_loss: 0.5459 - val_acc: 0.8761
Epoch 405/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0447 - acc: 0.9848 - val_loss: 0.6489 - val_acc: 0.8761
Epoch 406/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0509 - acc: 0.9815 - val_loss: 0.5831 - val_acc: 0.8716
Epoch 407/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0472 - acc: 0.9830 - val_loss: 0.4754 - val_acc: 0.8945
Epoch 408/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0532 - acc: 0.9824 - val_loss: 0.5353 - val_acc: 0.8761
Epoch 409/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0495 - acc: 0.9844 - val_loss: 0.5658 - val_acc: 0.8716
Epoch 410/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0744 - acc: 0.9770 - val_loss: 0.5518 - val_acc: 0.8716
Epoch 411/500
132/132 [==============================] - 67s 511ms/step - loss: 0.1196 - acc: 0.9629 - val_loss: 0.5998 - val_acc: 0.8349
Epoch 412/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0940 - acc: 0.9659 - val_loss: 0.6356 - val_acc: 0.8578
Epoch 413/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0497 - acc: 0.9833 - val_loss: 0.5912 - val_acc: 0.8624
Epoch 414/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0595 - acc: 0.9792 - val_loss: 0.6196 - val_acc: 0.8670
Epoch 415/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0389 - acc: 0.9882 - val_loss: 0.6367 - val_acc: 0.8991
Epoch 416/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0274 - acc: 0.9917 - val_loss: 0.6392 - val_acc: 0.8761
Epoch 417/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0566 - acc: 0.9800 - val_loss: 0.5896 - val_acc: 0.8945
Epoch 418/500
132/132 [==============================] - 66s 504ms/step - loss: 0.0305 - acc: 0.9906 - val_loss: 0.6935 - val_acc: 0.8991
Epoch 419/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1253 - acc: 0.9670 - val_loss: 0.6264 - val_acc: 0.8670
Epoch 420/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0621 - acc: 0.9786 - val_loss: 0.5776 - val_acc: 0.8578
Epoch 421/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0985 - acc: 0.9703 - val_loss: 0.4829 - val_acc: 0.8761
Epoch 422/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0458 - acc: 0.9853 - val_loss: 0.6459 - val_acc: 0.8761
Epoch 423/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1768 - acc: 0.9515 - val_loss: 0.6643 - val_acc: 0.8349
Epoch 424/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1927 - acc: 0.9380 - val_loss: 0.3521 - val_acc: 0.9174
Epoch 425/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0776 - acc: 0.9736 - val_loss: 0.4792 - val_acc: 0.8670
Epoch 426/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0559 - acc: 0.9848 - val_loss: 0.5944 - val_acc: 0.8624
Epoch 427/500
132/132 [==============================] - 67s 504ms/step - loss: 0.0455 - acc: 0.9847 - val_loss: 0.5983 - val_acc: 0.8807
Epoch 428/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0839 - acc: 0.9771 - val_loss: 0.9861 - val_acc: 0.8119
Epoch 429/500
132/132 [==============================] - 68s 512ms/step - loss: 0.1941 - acc: 0.9422 - val_loss: 0.5229 - val_acc: 0.8624
Epoch 430/500
132/132 [==============================] - 67s 508ms/step - loss: 0.1264 - acc: 0.9600 - val_loss: 0.6222 - val_acc: 0.8532
Epoch 431/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0675 - acc: 0.9817 - val_loss: 0.5962 - val_acc: 0.8624
Epoch 432/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0437 - acc: 0.9862 - val_loss: 0.6632 - val_acc: 0.8578
Epoch 433/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0459 - acc: 0.9861 - val_loss: 0.6575 - val_acc: 0.8440
Epoch 434/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0774 - acc: 0.9774 - val_loss: 0.7489 - val_acc: 0.8578
Epoch 435/500
132/132 [==============================] - 67s 510ms/step - loss: 0.3087 - acc: 0.9183 - val_loss: 0.3580 - val_acc: 0.8761
Epoch 436/500
132/132 [==============================] - 67s 505ms/step - loss: 0.1724 - acc: 0.9420 - val_loss: 0.4055 - val_acc: 0.8807
Epoch 437/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0679 - acc: 0.9798 - val_loss: 0.4247 - val_acc: 0.8807
Epoch 438/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0434 - acc: 0.9870 - val_loss: 0.5208 - val_acc: 0.8853
Epoch 439/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0396 - acc: 0.9879 - val_loss: 0.5800 - val_acc: 0.8807
Epoch 440/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0435 - acc: 0.9864 - val_loss: 0.5120 - val_acc: 0.8807
Epoch 441/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0250 - acc: 0.9932 - val_loss: 0.4867 - val_acc: 0.8945
Epoch 442/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0304 - acc: 0.9911 - val_loss: 0.5623 - val_acc: 0.8807
Epoch 443/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0566 - acc: 0.9827 - val_loss: 0.4762 - val_acc: 0.8807
Epoch 444/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0290 - acc: 0.9908 - val_loss: 0.6075 - val_acc: 0.8761
Epoch 445/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0180 - acc: 0.9948 - val_loss: 0.6302 - val_acc: 0.9037
Epoch 446/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0327 - acc: 0.9909 - val_loss: 0.5205 - val_acc: 0.8899
Epoch 447/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0663 - acc: 0.9783 - val_loss: 0.5821 - val_acc: 0.8716
Epoch 448/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0878 - acc: 0.9738 - val_loss: 0.6375 - val_acc: 0.8624
Epoch 449/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0659 - acc: 0.9797 - val_loss: 0.5897 - val_acc: 0.8578
Epoch 450/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0516 - acc: 0.9838 - val_loss: 0.5681 - val_acc: 0.8899
Epoch 451/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0335 - acc: 0.9891 - val_loss: 0.6164 - val_acc: 0.8716
Epoch 452/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0446 - acc: 0.9864 - val_loss: 0.6678 - val_acc: 0.8807
Epoch 453/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0307 - acc: 0.9917 - val_loss: 0.5479 - val_acc: 0.9083
Epoch 454/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0344 - acc: 0.9880 - val_loss: 0.5567 - val_acc: 0.8899
Epoch 455/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0433 - acc: 0.9855 - val_loss: 0.5459 - val_acc: 0.8899
Epoch 456/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0527 - acc: 0.9823 - val_loss: 0.5377 - val_acc: 0.8807
Epoch 457/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0482 - acc: 0.9845 - val_loss: 0.6158 - val_acc: 0.8761
Epoch 458/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0471 - acc: 0.9873 - val_loss: 0.7190 - val_acc: 0.8532
Epoch 459/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0342 - acc: 0.9897 - val_loss: 0.5602 - val_acc: 0.8807
Epoch 460/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0612 - acc: 0.9832 - val_loss: 0.6586 - val_acc: 0.8624
Epoch 461/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0647 - acc: 0.9814 - val_loss: 0.5801 - val_acc: 0.8807
Epoch 462/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0580 - acc: 0.9808 - val_loss: 0.5118 - val_acc: 0.8853
Epoch 463/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0749 - acc: 0.9767 - val_loss: 0.4627 - val_acc: 0.8899
Epoch 464/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0297 - acc: 0.9912 - val_loss: 0.6158 - val_acc: 0.8716
Epoch 465/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0288 - acc: 0.9917 - val_loss: 0.6872 - val_acc: 0.8761
Epoch 466/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0547 - acc: 0.9835 - val_loss: 0.6386 - val_acc: 0.8670
Epoch 467/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0451 - acc: 0.9859 - val_loss: 0.6357 - val_acc: 0.8578
Epoch 468/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0340 - acc: 0.9909 - val_loss: 0.6344 - val_acc: 0.8945
Epoch 469/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0690 - acc: 0.9786 - val_loss: 0.5350 - val_acc: 0.8716
Epoch 470/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0224 - acc: 0.9942 - val_loss: 0.5556 - val_acc: 0.8991
Epoch 471/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0156 - acc: 0.9965 - val_loss: 0.6378 - val_acc: 0.8899
Epoch 472/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0657 - acc: 0.9806 - val_loss: 0.4963 - val_acc: 0.8991
Epoch 473/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0517 - acc: 0.9852 - val_loss: 0.6223 - val_acc: 0.8578
Epoch 474/500
132/132 [==============================] - 68s 512ms/step - loss: 0.0741 - acc: 0.9789 - val_loss: 0.7855 - val_acc: 0.8761
Epoch 475/500
132/132 [==============================] - 67s 509ms/step - loss: 0.0728 - acc: 0.9789 - val_loss: 0.5745 - val_acc: 0.8945
Epoch 476/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0332 - acc: 0.9898 - val_loss: 0.5438 - val_acc: 0.9037
Epoch 477/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0344 - acc: 0.9876 - val_loss: 0.5808 - val_acc: 0.8945
Epoch 478/500
132/132 [==============================] - 67s 506ms/step - loss: 0.0337 - acc: 0.9895 - val_loss: 0.5876 - val_acc: 0.9037
Epoch 479/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0369 - acc: 0.9883 - val_loss: 0.5842 - val_acc: 0.8945
Epoch 480/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0814 - acc: 0.9776 - val_loss: 0.5729 - val_acc: 0.8761
Epoch 481/500
132/132 [==============================] - 67s 505ms/step - loss: 0.0865 - acc: 0.9770 - val_loss: 0.6622 - val_acc: 0.8578
Epoch 482/500
132/132 [==============================] - 67s 506ms/step - loss: 0.1073 - acc: 0.9645 - val_loss: 0.5356 - val_acc: 0.8578
Epoch 483/500
132/132 [==============================] - 68s 514ms/step - loss: 0.0358 - acc: 0.9898 - val_loss: 0.6729 - val_acc: 0.8440
Epoch 484/500
132/132 [==============================] - 69s 521ms/step - loss: 0.0254 - acc: 0.9924 - val_loss: 0.5878 - val_acc: 0.8761
Epoch 485/500
132/132 [==============================] - 70s 529ms/step - loss: 0.0367 - acc: 0.9900 - val_loss: 0.6533 - val_acc: 0.8670
Epoch 486/500
132/132 [==============================] - 75s 567ms/step - loss: 0.0893 - acc: 0.9761 - val_loss: 0.5674 - val_acc: 0.8670
Epoch 487/500
132/132 [==============================] - 69s 525ms/step - loss: 0.0587 - acc: 0.9842 - val_loss: 0.5797 - val_acc: 0.8624
Epoch 488/500
132/132 [==============================] - 71s 536ms/step - loss: 0.0307 - acc: 0.9926 - val_loss: 0.5698 - val_acc: 0.8807
Epoch 489/500
132/132 [==============================] - 72s 546ms/step - loss: 0.0413 - acc: 0.9885 - val_loss: 0.6265 - val_acc: 0.8670
Epoch 490/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0690 - acc: 0.9783 - val_loss: 0.4475 - val_acc: 0.8807
Epoch 491/500
132/132 [==============================] - 68s 519ms/step - loss: 0.0284 - acc: 0.9918 - val_loss: 0.5024 - val_acc: 0.8761
Epoch 492/500
132/132 [==============================] - 68s 514ms/step - loss: 0.0336 - acc: 0.9911 - val_loss: 0.5363 - val_acc: 0.8945
Epoch 493/500
132/132 [==============================] - 67s 511ms/step - loss: 0.0952 - acc: 0.9732 - val_loss: 0.6371 - val_acc: 0.8670
Epoch 494/500
132/132 [==============================] - 68s 518ms/step - loss: 0.0892 - acc: 0.9709 - val_loss: 0.4709 - val_acc: 0.8761
Epoch 495/500
132/132 [==============================] - 67s 510ms/step - loss: 0.0312 - acc: 0.9902 - val_loss: 0.4795 - val_acc: 0.8945
Epoch 496/500
132/132 [==============================] - 67s 507ms/step - loss: 0.0134 - acc: 0.9964 - val_loss: 0.5932 - val_acc: 0.8761
Epoch 497/500
132/132 [==============================] - 69s 520ms/step - loss: 0.0086 - acc: 0.9985 - val_loss: 0.6569 - val_acc: 0.8853
Epoch 498/500
132/132 [==============================] - 69s 526ms/step - loss: 0.0062 - acc: 0.9991 - val_loss: 0.6731 - val_acc: 0.8807
Epoch 499/500
132/132 [==============================] - 67s 508ms/step - loss: 0.0780 - acc: 0.9756 - val_loss: 0.6632 - val_acc: 0.8853
Epoch 500/500
132/132 [==============================] - 70s 532ms/step - loss: 0.0938 - acc: 0.9703 - val_loss: 0.6344 - val_acc: 0.8578
40/40 [==============================] - 6s 144ms/step
Accuracy: 0.8806500761808025. F1: 0.8790933479912493
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<div class="prompt input_prompt">In&nbsp;[28]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cnn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">make_modelCom</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span><span class="p">,</span> <span class="n">save_model</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Com_norm&quot;</span><span class="p">,</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
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<pre>Epoch 1/500
132/132 [==============================] - 40s 306ms/step - loss: 1.6890 - acc: 0.3368 - val_loss: 1.4291 - val_acc: 0.4037
Epoch 2/500
132/132 [==============================] - 38s 289ms/step - loss: 1.3679 - acc: 0.4458 - val_loss: 1.3360 - val_acc: 0.4037
Epoch 3/500
132/132 [==============================] - 45s 342ms/step - loss: 1.4274 - acc: 0.4179 - val_loss: 1.1894 - val_acc: 0.4725
Epoch 4/500
132/132 [==============================] - 45s 340ms/step - loss: 1.3364 - acc: 0.4626 - val_loss: 1.2203 - val_acc: 0.4541
Epoch 5/500
132/132 [==============================] - 45s 338ms/step - loss: 1.3266 - acc: 0.4432 - val_loss: 1.1925 - val_acc: 0.4587
Epoch 6/500
132/132 [==============================] - 45s 340ms/step - loss: 1.2993 - acc: 0.4669 - val_loss: 1.2508 - val_acc: 0.4495
Epoch 7/500
132/132 [==============================] - 45s 341ms/step - loss: 1.2630 - acc: 0.4908 - val_loss: 1.4301 - val_acc: 0.3853
Epoch 8/500
132/132 [==============================] - 45s 340ms/step - loss: 1.3976 - acc: 0.4494 - val_loss: 1.2087 - val_acc: 0.4679
Epoch 9/500
132/132 [==============================] - 45s 338ms/step - loss: 1.1921 - acc: 0.5157 - val_loss: 1.1401 - val_acc: 0.5138
Epoch 10/500
132/132 [==============================] - 45s 342ms/step - loss: 1.2446 - acc: 0.5141 - val_loss: 1.4212 - val_acc: 0.4037
Epoch 11/500
132/132 [==============================] - 45s 344ms/step - loss: 1.1915 - acc: 0.5256 - val_loss: 1.1230 - val_acc: 0.4954
Epoch 12/500
132/132 [==============================] - 46s 346ms/step - loss: 1.1638 - acc: 0.5418 - val_loss: 1.1132 - val_acc: 0.5092
Epoch 13/500
132/132 [==============================] - 46s 350ms/step - loss: 1.0827 - acc: 0.5735 - val_loss: 1.0360 - val_acc: 0.5321
Epoch 14/500
132/132 [==============================] - 46s 351ms/step - loss: 1.0130 - acc: 0.5994 - val_loss: 1.0286 - val_acc: 0.5505
Epoch 15/500
132/132 [==============================] - 46s 350ms/step - loss: 1.0078 - acc: 0.6004 - val_loss: 1.0545 - val_acc: 0.5229
Epoch 16/500
132/132 [==============================] - 46s 350ms/step - loss: 1.0422 - acc: 0.5888 - val_loss: 0.9681 - val_acc: 0.5459
Epoch 17/500
132/132 [==============================] - 46s 350ms/step - loss: 0.9660 - acc: 0.6180 - val_loss: 1.0530 - val_acc: 0.5229
Epoch 18/500
132/132 [==============================] - 46s 349ms/step - loss: 1.0802 - acc: 0.5735 - val_loss: 1.1057 - val_acc: 0.5183
Epoch 19/500
132/132 [==============================] - 46s 350ms/step - loss: 0.9488 - acc: 0.6275 - val_loss: 0.9378 - val_acc: 0.6009
Epoch 20/500
132/132 [==============================] - 45s 342ms/step - loss: 0.9636 - acc: 0.6250 - val_loss: 1.1366 - val_acc: 0.5459
Epoch 21/500
132/132 [==============================] - 46s 347ms/step - loss: 0.9854 - acc: 0.6100 - val_loss: 0.9666 - val_acc: 0.6101
Epoch 22/500
132/132 [==============================] - 52s 394ms/step - loss: 1.0061 - acc: 0.6090 - val_loss: 1.0071 - val_acc: 0.5459
Epoch 23/500
132/132 [==============================] - 47s 356ms/step - loss: 0.9425 - acc: 0.6294 - val_loss: 0.9093 - val_acc: 0.5780
Epoch 24/500
132/132 [==============================] - 47s 355ms/step - loss: 0.9703 - acc: 0.6268 - val_loss: 0.9615 - val_acc: 0.5688
Epoch 25/500
132/132 [==============================] - 49s 372ms/step - loss: 0.9816 - acc: 0.6250 - val_loss: 1.0056 - val_acc: 0.5550
Epoch 26/500
132/132 [==============================] - 47s 358ms/step - loss: 0.9360 - acc: 0.6297 - val_loss: 1.0102 - val_acc: 0.5550
Epoch 27/500
132/132 [==============================] - 54s 413ms/step - loss: 0.8907 - acc: 0.6465 - val_loss: 0.9029 - val_acc: 0.6101
Epoch 28/500
132/132 [==============================] - 53s 400ms/step - loss: 0.9167 - acc: 0.6472 - val_loss: 0.9631 - val_acc: 0.5872
Epoch 29/500
132/132 [==============================] - 55s 415ms/step - loss: 0.8861 - acc: 0.6517 - val_loss: 0.9492 - val_acc: 0.5963
Epoch 30/500
132/132 [==============================] - 57s 433ms/step - loss: 0.8546 - acc: 0.6600 - val_loss: 0.9152 - val_acc: 0.6055
Epoch 31/500
132/132 [==============================] - 59s 450ms/step - loss: 0.8694 - acc: 0.6679 - val_loss: 0.9575 - val_acc: 0.5963
Epoch 32/500
132/132 [==============================] - 62s 469ms/step - loss: 0.8293 - acc: 0.6865 - val_loss: 0.8925 - val_acc: 0.6239
Epoch 33/500
132/132 [==============================] - 60s 458ms/step - loss: 0.8012 - acc: 0.6969 - val_loss: 0.8260 - val_acc: 0.6514
Epoch 34/500
132/132 [==============================] - 58s 439ms/step - loss: 0.8737 - acc: 0.6621 - val_loss: 0.9118 - val_acc: 0.5642
Epoch 35/500
132/132 [==============================] - 61s 462ms/step - loss: 0.8369 - acc: 0.6738 - val_loss: 0.8906 - val_acc: 0.6101
Epoch 36/500
132/132 [==============================] - 63s 479ms/step - loss: 0.7961 - acc: 0.6926 - val_loss: 0.8980 - val_acc: 0.6147
Epoch 37/500
132/132 [==============================] - 59s 446ms/step - loss: 1.0565 - acc: 0.6039 - val_loss: 1.1557 - val_acc: 0.5183
Epoch 38/500
132/132 [==============================] - 59s 446ms/step - loss: 0.9814 - acc: 0.6103 - val_loss: 1.0050 - val_acc: 0.5642
Epoch 39/500
132/132 [==============================] - 60s 457ms/step - loss: 1.2674 - acc: 0.5192 - val_loss: 1.3249 - val_acc: 0.4771
Epoch 40/500
132/132 [==============================] - 57s 434ms/step - loss: 1.2678 - acc: 0.5062 - val_loss: 1.2206 - val_acc: 0.4633
Epoch 41/500
132/132 [==============================] - 57s 434ms/step - loss: 1.0384 - acc: 0.5850 - val_loss: 1.0403 - val_acc: 0.5550
Epoch 42/500
132/132 [==============================] - 62s 473ms/step - loss: 1.0380 - acc: 0.5913 - val_loss: 1.0242 - val_acc: 0.5642
Epoch 43/500
132/132 [==============================] - 61s 460ms/step - loss: 1.0319 - acc: 0.5976 - val_loss: 1.0550 - val_acc: 0.5413
Epoch 44/500
132/132 [==============================] - 60s 451ms/step - loss: 1.0777 - acc: 0.5813 - val_loss: 1.1054 - val_acc: 0.5138
Epoch 45/500
132/132 [==============================] - 59s 450ms/step - loss: 0.9569 - acc: 0.6254 - val_loss: 0.9899 - val_acc: 0.6101
Epoch 46/500
132/132 [==============================] - 59s 444ms/step - loss: 1.1457 - acc: 0.5381 - val_loss: 1.1402 - val_acc: 0.5000
Epoch 47/500
132/132 [==============================] - 60s 455ms/step - loss: 1.0581 - acc: 0.5794 - val_loss: 1.0129 - val_acc: 0.5826
Epoch 48/500
132/132 [==============================] - 57s 432ms/step - loss: 0.9432 - acc: 0.6352 - val_loss: 0.9826 - val_acc: 0.5872
Epoch 49/500
132/132 [==============================] - 60s 452ms/step - loss: 0.9000 - acc: 0.6407 - val_loss: 0.9866 - val_acc: 0.5642
Epoch 50/500
132/132 [==============================] - 58s 441ms/step - loss: 0.9749 - acc: 0.6146 - val_loss: 0.9945 - val_acc: 0.5917
Epoch 51/500
132/132 [==============================] - 60s 452ms/step - loss: 0.8889 - acc: 0.6507 - val_loss: 0.9578 - val_acc: 0.5688
Epoch 52/500
132/132 [==============================] - 60s 454ms/step - loss: 0.9180 - acc: 0.6275 - val_loss: 0.9214 - val_acc: 0.6009
Epoch 53/500
132/132 [==============================] - 58s 438ms/step - loss: 0.8439 - acc: 0.6686 - val_loss: 0.8923 - val_acc: 0.6193
Epoch 54/500
132/132 [==============================] - 54s 410ms/step - loss: 0.8128 - acc: 0.6746 - val_loss: 0.9119 - val_acc: 0.6009
Epoch 55/500
132/132 [==============================] - 57s 428ms/step - loss: 0.8279 - acc: 0.6810 - val_loss: 0.9161 - val_acc: 0.6147
Epoch 56/500
132/132 [==============================] - 58s 437ms/step - loss: 0.8419 - acc: 0.6749 - val_loss: 0.9187 - val_acc: 0.5963
Epoch 57/500
132/132 [==============================] - 59s 445ms/step - loss: 0.9696 - acc: 0.6305 - val_loss: 1.0481 - val_acc: 0.5734
Epoch 58/500
132/132 [==============================] - 58s 436ms/step - loss: 0.8810 - acc: 0.6587 - val_loss: 0.9416 - val_acc: 0.5917
Epoch 59/500
132/132 [==============================] - 57s 429ms/step - loss: 0.8559 - acc: 0.6649 - val_loss: 0.9616 - val_acc: 0.6284
Epoch 60/500
132/132 [==============================] - 59s 445ms/step - loss: 0.8246 - acc: 0.6804 - val_loss: 1.0232 - val_acc: 0.5826
Epoch 61/500
132/132 [==============================] - 56s 427ms/step - loss: 0.8375 - acc: 0.6758 - val_loss: 0.9195 - val_acc: 0.5963
Epoch 62/500
132/132 [==============================] - 58s 441ms/step - loss: 0.7694 - acc: 0.7097 - val_loss: 0.9009 - val_acc: 0.6193
Epoch 63/500
132/132 [==============================] - 60s 451ms/step - loss: 0.8416 - acc: 0.6769 - val_loss: 0.9258 - val_acc: 0.5963
Epoch 64/500
132/132 [==============================] - 58s 440ms/step - loss: 0.8206 - acc: 0.6791 - val_loss: 0.8955 - val_acc: 0.6193
Epoch 65/500
132/132 [==============================] - 57s 430ms/step - loss: 0.7847 - acc: 0.6961 - val_loss: 0.9065 - val_acc: 0.6193
Epoch 66/500
132/132 [==============================] - 58s 436ms/step - loss: 0.8397 - acc: 0.6748 - val_loss: 1.1287 - val_acc: 0.5596
Epoch 67/500
132/132 [==============================] - 58s 439ms/step - loss: 0.8644 - acc: 0.6656 - val_loss: 0.9116 - val_acc: 0.6101
Epoch 68/500
132/132 [==============================] - 56s 422ms/step - loss: 0.8910 - acc: 0.6547 - val_loss: 0.9408 - val_acc: 0.5917
Epoch 69/500
132/132 [==============================] - 46s 351ms/step - loss: 0.8363 - acc: 0.6673 - val_loss: 1.0255 - val_acc: 0.5872
Epoch 70/500
132/132 [==============================] - 46s 350ms/step - loss: 0.8468 - acc: 0.6633 - val_loss: 0.8990 - val_acc: 0.6193
Epoch 71/500
132/132 [==============================] - 46s 350ms/step - loss: 0.8059 - acc: 0.6939 - val_loss: 0.8546 - val_acc: 0.6560
Epoch 72/500
132/132 [==============================] - 46s 350ms/step - loss: 0.7680 - acc: 0.7166 - val_loss: 0.8891 - val_acc: 0.6422
Epoch 73/500
132/132 [==============================] - 45s 341ms/step - loss: 0.7721 - acc: 0.7052 - val_loss: 0.8781 - val_acc: 0.6193
Epoch 74/500
132/132 [==============================] - 44s 337ms/step - loss: 0.7613 - acc: 0.7047 - val_loss: 0.8386 - val_acc: 0.6468
Epoch 75/500
132/132 [==============================] - 44s 335ms/step - loss: 0.7337 - acc: 0.7246 - val_loss: 0.8566 - val_acc: 0.6468
Epoch 76/500
132/132 [==============================] - 44s 335ms/step - loss: 0.7250 - acc: 0.7283 - val_loss: 0.8014 - val_acc: 0.6697
Epoch 77/500
132/132 [==============================] - 50s 377ms/step - loss: 0.7236 - acc: 0.7330 - val_loss: 0.8599 - val_acc: 0.6468
Epoch 78/500
132/132 [==============================] - 48s 360ms/step - loss: 0.7374 - acc: 0.7210 - val_loss: 0.8665 - val_acc: 0.6651
Epoch 79/500
132/132 [==============================] - 46s 351ms/step - loss: 0.7316 - acc: 0.7172 - val_loss: 0.8375 - val_acc: 0.6422
Epoch 80/500
132/132 [==============================] - 46s 350ms/step - loss: 0.7215 - acc: 0.7379 - val_loss: 0.8761 - val_acc: 0.6514
Epoch 81/500
132/132 [==============================] - 46s 349ms/step - loss: 0.7311 - acc: 0.7232 - val_loss: 0.8637 - val_acc: 0.6789
Epoch 82/500
132/132 [==============================] - 46s 349ms/step - loss: 0.6989 - acc: 0.7431 - val_loss: 0.8424 - val_acc: 0.6468
Epoch 83/500
132/132 [==============================] - 46s 351ms/step - loss: 0.7402 - acc: 0.7078 - val_loss: 0.8266 - val_acc: 0.6606
Epoch 84/500
132/132 [==============================] - 47s 354ms/step - loss: 0.6998 - acc: 0.7414 - val_loss: 0.8526 - val_acc: 0.6881
Epoch 85/500
132/132 [==============================] - 47s 353ms/step - loss: 0.6926 - acc: 0.7435 - val_loss: 0.8060 - val_acc: 0.6743
Epoch 86/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6998 - acc: 0.7407 - val_loss: 0.8283 - val_acc: 0.6606
Epoch 87/500
132/132 [==============================] - 47s 356ms/step - loss: 0.6885 - acc: 0.7529 - val_loss: 0.8010 - val_acc: 0.6835
Epoch 88/500
132/132 [==============================] - 46s 350ms/step - loss: 0.7623 - acc: 0.7162 - val_loss: 0.8532 - val_acc: 0.6651
Epoch 89/500
132/132 [==============================] - 47s 357ms/step - loss: 0.7208 - acc: 0.7349 - val_loss: 0.8232 - val_acc: 0.6835
Epoch 90/500
132/132 [==============================] - 47s 354ms/step - loss: 0.7631 - acc: 0.7206 - val_loss: 0.7851 - val_acc: 0.6743
Epoch 91/500
132/132 [==============================] - 46s 350ms/step - loss: 0.6665 - acc: 0.7557 - val_loss: 0.8022 - val_acc: 0.6789
Epoch 92/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6680 - acc: 0.7573 - val_loss: 0.7526 - val_acc: 0.7110
Epoch 93/500
132/132 [==============================] - 47s 355ms/step - loss: 0.6731 - acc: 0.7563 - val_loss: 0.7493 - val_acc: 0.7064
Epoch 94/500
132/132 [==============================] - 46s 350ms/step - loss: 0.6680 - acc: 0.7589 - val_loss: 0.7430 - val_acc: 0.6972
Epoch 95/500
132/132 [==============================] - 47s 353ms/step - loss: 0.6961 - acc: 0.7432 - val_loss: 1.0366 - val_acc: 0.5917
Epoch 96/500
132/132 [==============================] - 46s 352ms/step - loss: 0.8166 - acc: 0.6879 - val_loss: 0.8976 - val_acc: 0.6284
Epoch 97/500
132/132 [==============================] - 47s 358ms/step - loss: 0.6975 - acc: 0.7506 - val_loss: 0.8269 - val_acc: 0.7064
Epoch 98/500
132/132 [==============================] - 48s 361ms/step - loss: 0.7486 - acc: 0.7263 - val_loss: 0.8273 - val_acc: 0.6697
Epoch 99/500
132/132 [==============================] - 48s 360ms/step - loss: 0.6707 - acc: 0.7632 - val_loss: 0.7715 - val_acc: 0.7248
Epoch 100/500
132/132 [==============================] - 47s 357ms/step - loss: 0.6502 - acc: 0.7673 - val_loss: 0.8033 - val_acc: 0.7064
Epoch 101/500
132/132 [==============================] - 47s 359ms/step - loss: 0.6695 - acc: 0.7582 - val_loss: 0.8290 - val_acc: 0.6514
Epoch 102/500
132/132 [==============================] - 47s 359ms/step - loss: 0.6609 - acc: 0.7564 - val_loss: 0.7747 - val_acc: 0.6927
Epoch 103/500
132/132 [==============================] - 48s 365ms/step - loss: 0.6549 - acc: 0.7576 - val_loss: 0.7565 - val_acc: 0.6835
Epoch 104/500
132/132 [==============================] - 48s 361ms/step - loss: 0.6314 - acc: 0.7726 - val_loss: 0.7981 - val_acc: 0.7018
Epoch 105/500
132/132 [==============================] - 48s 360ms/step - loss: 0.6236 - acc: 0.7738 - val_loss: 0.7958 - val_acc: 0.7064
Epoch 106/500
132/132 [==============================] - 48s 365ms/step - loss: 0.6074 - acc: 0.7833 - val_loss: 0.7638 - val_acc: 0.6927
Epoch 107/500
132/132 [==============================] - 48s 360ms/step - loss: 0.6248 - acc: 0.7767 - val_loss: 0.7690 - val_acc: 0.7156
Epoch 108/500
132/132 [==============================] - 47s 358ms/step - loss: 0.6687 - acc: 0.7575 - val_loss: 0.7893 - val_acc: 0.7018
Epoch 109/500
132/132 [==============================] - 48s 361ms/step - loss: 0.7579 - acc: 0.7215 - val_loss: 0.9135 - val_acc: 0.6239
Epoch 110/500
132/132 [==============================] - 49s 372ms/step - loss: 0.6828 - acc: 0.7510 - val_loss: 0.8233 - val_acc: 0.6697
Epoch 111/500
132/132 [==============================] - 50s 375ms/step - loss: 0.6475 - acc: 0.7663 - val_loss: 0.7602 - val_acc: 0.7018
Epoch 112/500
132/132 [==============================] - 48s 362ms/step - loss: 0.6698 - acc: 0.7567 - val_loss: 0.8057 - val_acc: 0.7018
Epoch 113/500
132/132 [==============================] - 50s 375ms/step - loss: 0.6483 - acc: 0.7670 - val_loss: 0.7675 - val_acc: 0.7248
Epoch 114/500
132/132 [==============================] - 49s 369ms/step - loss: 0.6106 - acc: 0.7857 - val_loss: 0.7612 - val_acc: 0.7064
Epoch 115/500
132/132 [==============================] - 51s 386ms/step - loss: 0.6919 - acc: 0.7456 - val_loss: 0.7316 - val_acc: 0.6972
Epoch 116/500
132/132 [==============================] - 48s 363ms/step - loss: 0.6323 - acc: 0.7684 - val_loss: 0.7469 - val_acc: 0.6972
Epoch 117/500
132/132 [==============================] - 46s 350ms/step - loss: 0.6023 - acc: 0.7827 - val_loss: 0.7363 - val_acc: 0.7248
Epoch 118/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6186 - acc: 0.7795 - val_loss: 0.8048 - val_acc: 0.7110
Epoch 119/500
132/132 [==============================] - 47s 354ms/step - loss: 0.6169 - acc: 0.7757 - val_loss: 0.7376 - val_acc: 0.7385
Epoch 120/500
132/132 [==============================] - 46s 350ms/step - loss: 0.6273 - acc: 0.7692 - val_loss: 0.7785 - val_acc: 0.6927
Epoch 121/500
132/132 [==============================] - 47s 355ms/step - loss: 0.6746 - acc: 0.7539 - val_loss: 0.7871 - val_acc: 0.6789
Epoch 122/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6471 - acc: 0.7707 - val_loss: 0.7496 - val_acc: 0.7248
Epoch 123/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6125 - acc: 0.7818 - val_loss: 0.7448 - val_acc: 0.7294
Epoch 124/500
132/132 [==============================] - 47s 353ms/step - loss: 0.6059 - acc: 0.7842 - val_loss: 0.7526 - val_acc: 0.7431
Epoch 125/500
132/132 [==============================] - 46s 352ms/step - loss: 0.6004 - acc: 0.7902 - val_loss: 0.7449 - val_acc: 0.7156
Epoch 126/500
132/132 [==============================] - 46s 351ms/step - loss: 0.6097 - acc: 0.7799 - val_loss: 0.7469 - val_acc: 0.7385
Epoch 127/500
132/132 [==============================] - 47s 358ms/step - loss: 0.5832 - acc: 0.7900 - val_loss: 0.7742 - val_acc: 0.7156
Epoch 128/500
132/132 [==============================] - 46s 351ms/step - loss: 0.5975 - acc: 0.7895 - val_loss: 0.7328 - val_acc: 0.7064
Epoch 129/500
132/132 [==============================] - 47s 353ms/step - loss: 0.6346 - acc: 0.7804 - val_loss: 0.7585 - val_acc: 0.7018
Epoch 130/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5887 - acc: 0.7860 - val_loss: 0.7345 - val_acc: 0.7248
Epoch 131/500
132/132 [==============================] - 47s 353ms/step - loss: 0.5699 - acc: 0.7961 - val_loss: 0.7267 - val_acc: 0.7248
Epoch 132/500
132/132 [==============================] - 49s 372ms/step - loss: 0.5850 - acc: 0.7895 - val_loss: 0.7139 - val_acc: 0.7339
Epoch 133/500
132/132 [==============================] - 49s 368ms/step - loss: 0.5729 - acc: 0.7966 - val_loss: 0.6986 - val_acc: 0.7615
Epoch 134/500
132/132 [==============================] - 45s 339ms/step - loss: 0.5807 - acc: 0.7916 - val_loss: 0.7131 - val_acc: 0.7202
Epoch 135/500
132/132 [==============================] - 44s 333ms/step - loss: 0.6138 - acc: 0.7863 - val_loss: 0.9487 - val_acc: 0.6514
Epoch 136/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6074 - acc: 0.7901 - val_loss: 0.7294 - val_acc: 0.7431
Epoch 137/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5565 - acc: 0.8061 - val_loss: 0.6638 - val_acc: 0.7615
Epoch 138/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5575 - acc: 0.8051 - val_loss: 0.7402 - val_acc: 0.7248
Epoch 139/500
132/132 [==============================] - 44s 334ms/step - loss: 0.5643 - acc: 0.8037 - val_loss: 0.6504 - val_acc: 0.7661
Epoch 140/500
132/132 [==============================] - 44s 333ms/step - loss: 0.6037 - acc: 0.7885 - val_loss: 0.6980 - val_acc: 0.7385
Epoch 141/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5756 - acc: 0.7969 - val_loss: 0.6959 - val_acc: 0.7752
Epoch 142/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5773 - acc: 0.7965 - val_loss: 0.6523 - val_acc: 0.7569
Epoch 143/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5458 - acc: 0.8096 - val_loss: 0.6598 - val_acc: 0.7661
Epoch 144/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5549 - acc: 0.8052 - val_loss: 0.7100 - val_acc: 0.7523
Epoch 145/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5644 - acc: 0.8051 - val_loss: 0.6559 - val_acc: 0.7339
Epoch 146/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6029 - acc: 0.7864 - val_loss: 0.7332 - val_acc: 0.7110
Epoch 147/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5751 - acc: 0.7998 - val_loss: 0.6879 - val_acc: 0.7477
Epoch 148/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5456 - acc: 0.8101 - val_loss: 0.7053 - val_acc: 0.7477
Epoch 149/500
132/132 [==============================] - 44s 331ms/step - loss: 0.8309 - acc: 0.7013 - val_loss: 0.9836 - val_acc: 0.6284
Epoch 150/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7248 - acc: 0.7346 - val_loss: 0.7522 - val_acc: 0.6835
Epoch 151/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6872 - acc: 0.7528 - val_loss: 0.7450 - val_acc: 0.6972
Epoch 152/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7948 - acc: 0.7083 - val_loss: 1.0157 - val_acc: 0.6101
Epoch 153/500
132/132 [==============================] - 44s 330ms/step - loss: 0.8632 - acc: 0.6764 - val_loss: 0.8884 - val_acc: 0.6651
Epoch 154/500
132/132 [==============================] - 44s 331ms/step - loss: 0.7294 - acc: 0.7403 - val_loss: 0.7586 - val_acc: 0.7018
Epoch 155/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6301 - acc: 0.7773 - val_loss: 0.7110 - val_acc: 0.7477
Epoch 156/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5980 - acc: 0.7891 - val_loss: 0.7443 - val_acc: 0.7431
Epoch 157/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5945 - acc: 0.7923 - val_loss: 0.7214 - val_acc: 0.7569
Epoch 158/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5927 - acc: 0.7910 - val_loss: 0.6712 - val_acc: 0.7431
Epoch 159/500
132/132 [==============================] - 45s 343ms/step - loss: 0.5751 - acc: 0.7982 - val_loss: 0.7027 - val_acc: 0.7477
Epoch 160/500
132/132 [==============================] - 48s 364ms/step - loss: 0.5626 - acc: 0.8026 - val_loss: 0.7012 - val_acc: 0.7477
Epoch 161/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5456 - acc: 0.8119 - val_loss: 0.7212 - val_acc: 0.7569
Epoch 162/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5309 - acc: 0.8152 - val_loss: 0.6639 - val_acc: 0.7431
Epoch 163/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5350 - acc: 0.8103 - val_loss: 0.6486 - val_acc: 0.7385
Epoch 164/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5610 - acc: 0.8032 - val_loss: 0.7189 - val_acc: 0.7385
Epoch 165/500
132/132 [==============================] - 46s 348ms/step - loss: 0.6005 - acc: 0.7849 - val_loss: 0.8322 - val_acc: 0.6881
Epoch 166/500
132/132 [==============================] - 46s 348ms/step - loss: 0.6502 - acc: 0.7674 - val_loss: 0.7056 - val_acc: 0.7385
Epoch 167/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5663 - acc: 0.8004 - val_loss: 0.6235 - val_acc: 0.7477
Epoch 168/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5475 - acc: 0.8124 - val_loss: 0.6160 - val_acc: 0.7385
Epoch 169/500
132/132 [==============================] - 46s 347ms/step - loss: 0.5339 - acc: 0.8142 - val_loss: 0.6519 - val_acc: 0.7752
Epoch 170/500
132/132 [==============================] - 46s 351ms/step - loss: 0.5342 - acc: 0.8139 - val_loss: 0.6183 - val_acc: 0.7523
Epoch 171/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5231 - acc: 0.8151 - val_loss: 0.6908 - val_acc: 0.7569
Epoch 172/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5659 - acc: 0.8060 - val_loss: 0.6998 - val_acc: 0.7110
Epoch 173/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5486 - acc: 0.8105 - val_loss: 0.6154 - val_acc: 0.7569
Epoch 174/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5295 - acc: 0.8149 - val_loss: 0.6191 - val_acc: 0.7523
Epoch 175/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5351 - acc: 0.8175 - val_loss: 0.6670 - val_acc: 0.7615
Epoch 176/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5240 - acc: 0.8163 - val_loss: 0.6699 - val_acc: 0.7569
Epoch 177/500
132/132 [==============================] - 47s 353ms/step - loss: 0.5160 - acc: 0.8243 - val_loss: 0.7101 - val_acc: 0.7431
Epoch 178/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5055 - acc: 0.8242 - val_loss: 0.6697 - val_acc: 0.7615
Epoch 179/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5281 - acc: 0.8151 - val_loss: 0.6524 - val_acc: 0.7477
Epoch 180/500
132/132 [==============================] - 46s 348ms/step - loss: 0.6074 - acc: 0.7892 - val_loss: 0.6840 - val_acc: 0.7202
Epoch 181/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5317 - acc: 0.8089 - val_loss: 0.6546 - val_acc: 0.7339
Epoch 182/500
132/132 [==============================] - 46s 347ms/step - loss: 0.5505 - acc: 0.8050 - val_loss: 0.6917 - val_acc: 0.7431
Epoch 183/500
132/132 [==============================] - 46s 352ms/step - loss: 0.5924 - acc: 0.7869 - val_loss: 0.6994 - val_acc: 0.7477
Epoch 184/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5305 - acc: 0.8139 - val_loss: 0.6785 - val_acc: 0.7477
Epoch 185/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5468 - acc: 0.8087 - val_loss: 0.6820 - val_acc: 0.7431
Epoch 186/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5478 - acc: 0.8069 - val_loss: 0.6574 - val_acc: 0.7615
Epoch 187/500
132/132 [==============================] - 46s 347ms/step - loss: 0.5813 - acc: 0.7916 - val_loss: 0.7292 - val_acc: 0.7385
Epoch 188/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5830 - acc: 0.7885 - val_loss: 0.6489 - val_acc: 0.7294
Epoch 189/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5315 - acc: 0.8193 - val_loss: 0.7176 - val_acc: 0.7615
Epoch 190/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5918 - acc: 0.7921 - val_loss: 0.7220 - val_acc: 0.7294
Epoch 191/500
132/132 [==============================] - 46s 350ms/step - loss: 0.6037 - acc: 0.7794 - val_loss: 0.6882 - val_acc: 0.7339
Epoch 192/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5746 - acc: 0.7984 - val_loss: 0.7137 - val_acc: 0.7569
Epoch 193/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5175 - acc: 0.8176 - val_loss: 0.6285 - val_acc: 0.7615
Epoch 194/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5321 - acc: 0.8149 - val_loss: 0.6604 - val_acc: 0.7523
Epoch 195/500
132/132 [==============================] - 46s 348ms/step - loss: 0.5582 - acc: 0.8021 - val_loss: 0.7764 - val_acc: 0.7064
Epoch 196/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5466 - acc: 0.8070 - val_loss: 0.6768 - val_acc: 0.7339
Epoch 197/500
132/132 [==============================] - 48s 365ms/step - loss: 0.5023 - acc: 0.8281 - val_loss: 0.6862 - val_acc: 0.7615
Epoch 198/500
132/132 [==============================] - 46s 348ms/step - loss: 0.4891 - acc: 0.8267 - val_loss: 0.6776 - val_acc: 0.7569
Epoch 199/500
132/132 [==============================] - 48s 365ms/step - loss: 0.5493 - acc: 0.8046 - val_loss: 0.6950 - val_acc: 0.7248
Epoch 200/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5045 - acc: 0.8185 - val_loss: 0.7646 - val_acc: 0.7385
Epoch 201/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5060 - acc: 0.8264 - val_loss: 0.7383 - val_acc: 0.7385
Epoch 202/500
132/132 [==============================] - 47s 357ms/step - loss: 0.5196 - acc: 0.8237 - val_loss: 0.6807 - val_acc: 0.7523
Epoch 203/500
132/132 [==============================] - 45s 342ms/step - loss: 0.5184 - acc: 0.8187 - val_loss: 0.6182 - val_acc: 0.7431
Epoch 204/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5071 - acc: 0.8263 - val_loss: 0.6643 - val_acc: 0.7477
Epoch 205/500
132/132 [==============================] - 45s 344ms/step - loss: 0.5438 - acc: 0.8100 - val_loss: 0.6954 - val_acc: 0.7431
Epoch 206/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5004 - acc: 0.8249 - val_loss: 0.6740 - val_acc: 0.7615
Epoch 207/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5023 - acc: 0.8240 - val_loss: 0.6957 - val_acc: 0.7431
Epoch 208/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4925 - acc: 0.8264 - val_loss: 0.7407 - val_acc: 0.7385
Epoch 209/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5068 - acc: 0.8196 - val_loss: 0.6515 - val_acc: 0.7523
Epoch 210/500
132/132 [==============================] - 44s 335ms/step - loss: 0.4960 - acc: 0.8267 - val_loss: 0.7190 - val_acc: 0.7294
Epoch 211/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4888 - acc: 0.8270 - val_loss: 0.6977 - val_acc: 0.7385
Epoch 212/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5102 - acc: 0.8201 - val_loss: 0.7200 - val_acc: 0.7294
Epoch 213/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5044 - acc: 0.8279 - val_loss: 0.7202 - val_acc: 0.7339
Epoch 214/500
132/132 [==============================] - 45s 343ms/step - loss: 0.4948 - acc: 0.8304 - val_loss: 0.6674 - val_acc: 0.7523
Epoch 215/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4850 - acc: 0.8346 - val_loss: 0.6963 - val_acc: 0.7523
Epoch 216/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4999 - acc: 0.8231 - val_loss: 0.6582 - val_acc: 0.7523
Epoch 217/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4826 - acc: 0.8339 - val_loss: 0.6511 - val_acc: 0.7569
Epoch 218/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4933 - acc: 0.8308 - val_loss: 0.7945 - val_acc: 0.7294
Epoch 219/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5537 - acc: 0.8029 - val_loss: 0.7720 - val_acc: 0.7202
Epoch 220/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5287 - acc: 0.8170 - val_loss: 0.6453 - val_acc: 0.7523
Epoch 221/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4543 - acc: 0.8428 - val_loss: 0.6410 - val_acc: 0.7661
Epoch 222/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4576 - acc: 0.8395 - val_loss: 0.6804 - val_acc: 0.7706
Epoch 223/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4945 - acc: 0.8308 - val_loss: 0.6598 - val_acc: 0.7706
Epoch 224/500
132/132 [==============================] - 44s 336ms/step - loss: 0.4726 - acc: 0.8349 - val_loss: 0.6936 - val_acc: 0.7523
Epoch 225/500
132/132 [==============================] - 44s 332ms/step - loss: 0.4754 - acc: 0.8352 - val_loss: 0.6544 - val_acc: 0.7523
Epoch 226/500
132/132 [==============================] - 44s 332ms/step - loss: 0.4716 - acc: 0.8319 - val_loss: 0.6559 - val_acc: 0.7569
Epoch 227/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5404 - acc: 0.8172 - val_loss: 0.5897 - val_acc: 0.7706
Epoch 228/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5202 - acc: 0.8190 - val_loss: 0.6595 - val_acc: 0.7523
Epoch 229/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5006 - acc: 0.8312 - val_loss: 0.7236 - val_acc: 0.7385
Epoch 230/500
132/132 [==============================] - 44s 332ms/step - loss: 0.4759 - acc: 0.8373 - val_loss: 0.6385 - val_acc: 0.7752
Epoch 231/500
132/132 [==============================] - 44s 336ms/step - loss: 0.4611 - acc: 0.8404 - val_loss: 0.6909 - val_acc: 0.7477
Epoch 232/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4760 - acc: 0.8354 - val_loss: 0.6486 - val_acc: 0.7706
Epoch 233/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4454 - acc: 0.8469 - val_loss: 0.6548 - val_acc: 0.7523
Epoch 234/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4756 - acc: 0.8318 - val_loss: 0.6938 - val_acc: 0.7706
Epoch 235/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5020 - acc: 0.8261 - val_loss: 0.6581 - val_acc: 0.7844
Epoch 236/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7113 - acc: 0.7728 - val_loss: 0.6837 - val_acc: 0.7431
Epoch 237/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4936 - acc: 0.8278 - val_loss: 0.6943 - val_acc: 0.7523
Epoch 238/500
132/132 [==============================] - 45s 343ms/step - loss: 0.4713 - acc: 0.8355 - val_loss: 0.6971 - val_acc: 0.7615
Epoch 239/500
132/132 [==============================] - 46s 346ms/step - loss: 0.4714 - acc: 0.8342 - val_loss: 0.6741 - val_acc: 0.7569
Epoch 240/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4716 - acc: 0.8364 - val_loss: 0.6924 - val_acc: 0.7661
Epoch 241/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4605 - acc: 0.8385 - val_loss: 0.6582 - val_acc: 0.7523
Epoch 242/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4670 - acc: 0.8358 - val_loss: 0.6566 - val_acc: 0.7752
Epoch 243/500
132/132 [==============================] - 45s 339ms/step - loss: 0.5073 - acc: 0.8217 - val_loss: 0.7327 - val_acc: 0.7248
Epoch 244/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4574 - acc: 0.8408 - val_loss: 0.6747 - val_acc: 0.7661
Epoch 245/500
132/132 [==============================] - 45s 341ms/step - loss: 0.4758 - acc: 0.8318 - val_loss: 0.6634 - val_acc: 0.7661
Epoch 246/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4416 - acc: 0.8448 - val_loss: 0.7293 - val_acc: 0.7477
Epoch 247/500
132/132 [==============================] - 45s 341ms/step - loss: 0.4930 - acc: 0.8304 - val_loss: 0.7187 - val_acc: 0.7294
Epoch 248/500
132/132 [==============================] - 45s 340ms/step - loss: 0.6124 - acc: 0.7850 - val_loss: 0.8049 - val_acc: 0.6972
Epoch 249/500
132/132 [==============================] - 45s 340ms/step - loss: 0.5063 - acc: 0.8201 - val_loss: 0.6955 - val_acc: 0.7431
Epoch 250/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4914 - acc: 0.8251 - val_loss: 0.6569 - val_acc: 0.7798
Epoch 251/500
132/132 [==============================] - 46s 347ms/step - loss: 0.5079 - acc: 0.8217 - val_loss: 0.7000 - val_acc: 0.7523
Epoch 252/500
132/132 [==============================] - 45s 342ms/step - loss: 0.4576 - acc: 0.8382 - val_loss: 0.6080 - val_acc: 0.7936
Epoch 253/500
132/132 [==============================] - 45s 338ms/step - loss: 0.5115 - acc: 0.8233 - val_loss: 0.6550 - val_acc: 0.7431
Epoch 254/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4772 - acc: 0.8320 - val_loss: 0.6665 - val_acc: 0.7385
Epoch 255/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4642 - acc: 0.8385 - val_loss: 0.7507 - val_acc: 0.7569
Epoch 256/500
132/132 [==============================] - 45s 340ms/step - loss: 0.5618 - acc: 0.8051 - val_loss: 0.7521 - val_acc: 0.7385
Epoch 257/500
132/132 [==============================] - 45s 339ms/step - loss: 0.5366 - acc: 0.8125 - val_loss: 0.7761 - val_acc: 0.7569
Epoch 258/500
132/132 [==============================] - 45s 339ms/step - loss: 0.8010 - acc: 0.7051 - val_loss: 0.9133 - val_acc: 0.6560
Epoch 259/500
132/132 [==============================] - 45s 342ms/step - loss: 0.7274 - acc: 0.7422 - val_loss: 0.7645 - val_acc: 0.7294
Epoch 260/500
132/132 [==============================] - 45s 339ms/step - loss: 0.6653 - acc: 0.7606 - val_loss: 0.7630 - val_acc: 0.7156
Epoch 261/500
132/132 [==============================] - 45s 340ms/step - loss: 0.6479 - acc: 0.7619 - val_loss: 0.7734 - val_acc: 0.6972
Epoch 262/500
132/132 [==============================] - 45s 343ms/step - loss: 0.6086 - acc: 0.7749 - val_loss: 0.7274 - val_acc: 0.7202
Epoch 263/500
132/132 [==============================] - 45s 344ms/step - loss: 0.6070 - acc: 0.7849 - val_loss: 0.7256 - val_acc: 0.7339
Epoch 264/500
132/132 [==============================] - 46s 346ms/step - loss: 0.6524 - acc: 0.7573 - val_loss: 0.9079 - val_acc: 0.6330
Epoch 265/500
132/132 [==============================] - 46s 345ms/step - loss: 0.7175 - acc: 0.7361 - val_loss: 0.7785 - val_acc: 0.7110
Epoch 266/500
132/132 [==============================] - 45s 340ms/step - loss: 0.6050 - acc: 0.7815 - val_loss: 0.7710 - val_acc: 0.7064
Epoch 267/500
132/132 [==============================] - 45s 343ms/step - loss: 0.5794 - acc: 0.7886 - val_loss: 0.7152 - val_acc: 0.7248
Epoch 268/500
132/132 [==============================] - 45s 341ms/step - loss: 0.5838 - acc: 0.7820 - val_loss: 0.6895 - val_acc: 0.7477
Epoch 269/500
132/132 [==============================] - 45s 340ms/step - loss: 0.5382 - acc: 0.8053 - val_loss: 0.7201 - val_acc: 0.7248
Epoch 270/500
132/132 [==============================] - 45s 340ms/step - loss: 0.5434 - acc: 0.7988 - val_loss: 0.7175 - val_acc: 0.7294
Epoch 271/500
132/132 [==============================] - 45s 345ms/step - loss: 0.5200 - acc: 0.8085 - val_loss: 0.6951 - val_acc: 0.7431
Epoch 272/500
132/132 [==============================] - 46s 352ms/step - loss: 0.5379 - acc: 0.8064 - val_loss: 0.7269 - val_acc: 0.7523
Epoch 273/500
132/132 [==============================] - 46s 351ms/step - loss: 0.5269 - acc: 0.8100 - val_loss: 0.6742 - val_acc: 0.7615
Epoch 274/500
132/132 [==============================] - 46s 347ms/step - loss: 0.5250 - acc: 0.8122 - val_loss: 0.8036 - val_acc: 0.7202
Epoch 275/500
132/132 [==============================] - 47s 353ms/step - loss: 0.5142 - acc: 0.8182 - val_loss: 0.6996 - val_acc: 0.7385
Epoch 276/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5511 - acc: 0.8032 - val_loss: 0.7479 - val_acc: 0.7339
Epoch 277/500
132/132 [==============================] - 46s 352ms/step - loss: 0.5174 - acc: 0.8158 - val_loss: 0.6816 - val_acc: 0.7431
Epoch 278/500
132/132 [==============================] - 46s 350ms/step - loss: 0.5232 - acc: 0.8164 - val_loss: 0.6742 - val_acc: 0.7477
Epoch 279/500
132/132 [==============================] - 46s 349ms/step - loss: 0.5172 - acc: 0.8229 - val_loss: 0.6921 - val_acc: 0.7431
Epoch 280/500
132/132 [==============================] - 46s 348ms/step - loss: 0.4916 - acc: 0.8254 - val_loss: 0.6772 - val_acc: 0.7615
Epoch 281/500
132/132 [==============================] - 47s 353ms/step - loss: 0.4822 - acc: 0.8257 - val_loss: 0.6790 - val_acc: 0.7477
Epoch 282/500
132/132 [==============================] - 48s 360ms/step - loss: 0.5010 - acc: 0.8200 - val_loss: 0.7221 - val_acc: 0.7248
Epoch 283/500
132/132 [==============================] - 47s 358ms/step - loss: 0.5234 - acc: 0.8123 - val_loss: 0.6672 - val_acc: 0.7798
Epoch 284/500
132/132 [==============================] - 46s 352ms/step - loss: 0.4717 - acc: 0.8266 - val_loss: 0.7202 - val_acc: 0.7661
Epoch 285/500
132/132 [==============================] - 47s 355ms/step - loss: 0.5104 - acc: 0.8208 - val_loss: 0.6358 - val_acc: 0.7798
Epoch 286/500
132/132 [==============================] - 46s 350ms/step - loss: 0.4719 - acc: 0.8295 - val_loss: 0.5878 - val_acc: 0.7890
Epoch 287/500
132/132 [==============================] - 51s 387ms/step - loss: 0.4379 - acc: 0.8473 - val_loss: 0.5851 - val_acc: 0.7706
Epoch 288/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4342 - acc: 0.8454 - val_loss: 0.6647 - val_acc: 0.7569
Epoch 289/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4544 - acc: 0.8386 - val_loss: 0.6493 - val_acc: 0.7706
Epoch 290/500
132/132 [==============================] - 45s 342ms/step - loss: 0.4787 - acc: 0.8280 - val_loss: 0.6459 - val_acc: 0.7752
Epoch 291/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4781 - acc: 0.8302 - val_loss: 0.5966 - val_acc: 0.7844
Epoch 292/500
132/132 [==============================] - 46s 345ms/step - loss: 0.5775 - acc: 0.7985 - val_loss: 0.6514 - val_acc: 0.7294
Epoch 293/500
132/132 [==============================] - 45s 341ms/step - loss: 0.5063 - acc: 0.8164 - val_loss: 0.6934 - val_acc: 0.7569
Epoch 294/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4620 - acc: 0.8337 - val_loss: 0.8233 - val_acc: 0.7431
Epoch 295/500
132/132 [==============================] - 45s 338ms/step - loss: 0.4645 - acc: 0.8367 - val_loss: 0.6083 - val_acc: 0.7844
Epoch 296/500
132/132 [==============================] - 45s 338ms/step - loss: 0.4595 - acc: 0.8372 - val_loss: 0.7627 - val_acc: 0.7248
Epoch 297/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4754 - acc: 0.8356 - val_loss: 0.6961 - val_acc: 0.7477
Epoch 298/500
132/132 [==============================] - 45s 338ms/step - loss: 0.4563 - acc: 0.8309 - val_loss: 0.6992 - val_acc: 0.7477
Epoch 299/500
132/132 [==============================] - 45s 338ms/step - loss: 0.4541 - acc: 0.8365 - val_loss: 0.6895 - val_acc: 0.7523
Epoch 300/500
132/132 [==============================] - 45s 338ms/step - loss: 0.4554 - acc: 0.8392 - val_loss: 0.6819 - val_acc: 0.7385
Epoch 301/500
132/132 [==============================] - 45s 339ms/step - loss: 0.4813 - acc: 0.8305 - val_loss: 0.6697 - val_acc: 0.7385
Epoch 302/500
132/132 [==============================] - 45s 338ms/step - loss: 0.6450 - acc: 0.7768 - val_loss: 0.7840 - val_acc: 0.7248
Epoch 303/500
132/132 [==============================] - 45s 338ms/step - loss: 0.5898 - acc: 0.7907 - val_loss: 0.7193 - val_acc: 0.7248
Epoch 304/500
132/132 [==============================] - 45s 339ms/step - loss: 0.5956 - acc: 0.7885 - val_loss: 0.7329 - val_acc: 0.7385
Epoch 305/500
132/132 [==============================] - 45s 341ms/step - loss: 0.5114 - acc: 0.8179 - val_loss: 0.6634 - val_acc: 0.7798
Epoch 306/500
132/132 [==============================] - 45s 341ms/step - loss: 0.4885 - acc: 0.8306 - val_loss: 0.6555 - val_acc: 0.7569
Epoch 307/500
132/132 [==============================] - 45s 342ms/step - loss: 0.5335 - acc: 0.8145 - val_loss: 0.6106 - val_acc: 0.7798
Epoch 308/500
132/132 [==============================] - 45s 342ms/step - loss: 0.4709 - acc: 0.8333 - val_loss: 0.6937 - val_acc: 0.7661
Epoch 309/500
132/132 [==============================] - 45s 342ms/step - loss: 0.4824 - acc: 0.8320 - val_loss: 0.6342 - val_acc: 0.7936
Epoch 310/500
132/132 [==============================] - 45s 340ms/step - loss: 0.4947 - acc: 0.8239 - val_loss: 0.6298 - val_acc: 0.7844
Epoch 311/500
132/132 [==============================] - 45s 339ms/step - loss: 0.5108 - acc: 0.8176 - val_loss: 0.6764 - val_acc: 0.7661
Epoch 312/500
132/132 [==============================] - 45s 341ms/step - loss: 0.4726 - acc: 0.8322 - val_loss: 0.7300 - val_acc: 0.7248
Epoch 313/500
132/132 [==============================] - 45s 340ms/step - loss: 0.7668 - acc: 0.7621 - val_loss: 0.7718 - val_acc: 0.7110
Epoch 314/500
132/132 [==============================] - 45s 340ms/step - loss: 0.6324 - acc: 0.7782 - val_loss: 0.7257 - val_acc: 0.7294
Epoch 315/500
132/132 [==============================] - 45s 340ms/step - loss: 0.6014 - acc: 0.7822 - val_loss: 0.6741 - val_acc: 0.7569
Epoch 316/500
132/132 [==============================] - 45s 338ms/step - loss: 0.5408 - acc: 0.8050 - val_loss: 0.6386 - val_acc: 0.7752
Epoch 317/500
132/132 [==============================] - 46s 351ms/step - loss: 0.4943 - acc: 0.8225 - val_loss: 0.6600 - val_acc: 0.7385
Epoch 318/500
132/132 [==============================] - 46s 346ms/step - loss: 0.4851 - acc: 0.8298 - val_loss: 0.6014 - val_acc: 0.7706
Epoch 319/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5340 - acc: 0.8094 - val_loss: 0.9906 - val_acc: 0.6560
Epoch 320/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7187 - acc: 0.7307 - val_loss: 0.8002 - val_acc: 0.7064
Epoch 321/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5815 - acc: 0.7883 - val_loss: 0.7898 - val_acc: 0.7064
Epoch 322/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5477 - acc: 0.8046 - val_loss: 0.6679 - val_acc: 0.7569
Epoch 323/500
132/132 [==============================] - 44s 331ms/step - loss: 0.4867 - acc: 0.8237 - val_loss: 0.6723 - val_acc: 0.7615
Epoch 324/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5065 - acc: 0.8192 - val_loss: 0.6960 - val_acc: 0.7523
Epoch 325/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5057 - acc: 0.8187 - val_loss: 0.5922 - val_acc: 0.7752
Epoch 326/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4855 - acc: 0.8276 - val_loss: 0.7090 - val_acc: 0.7523
Epoch 327/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5829 - acc: 0.7977 - val_loss: 0.8202 - val_acc: 0.6972
Epoch 328/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6297 - acc: 0.7751 - val_loss: 0.9538 - val_acc: 0.6330
Epoch 329/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6661 - acc: 0.7566 - val_loss: 0.7496 - val_acc: 0.7248
Epoch 330/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6334 - acc: 0.7696 - val_loss: 0.6289 - val_acc: 0.7890
Epoch 331/500
132/132 [==============================] - 44s 334ms/step - loss: 0.5149 - acc: 0.8198 - val_loss: 0.5552 - val_acc: 0.7890
Epoch 332/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5283 - acc: 0.8085 - val_loss: 0.6200 - val_acc: 0.7798
Epoch 333/500
132/132 [==============================] - 44s 336ms/step - loss: 0.5683 - acc: 0.7970 - val_loss: 0.6942 - val_acc: 0.7523
Epoch 334/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5614 - acc: 0.7989 - val_loss: 0.6571 - val_acc: 0.7523
Epoch 335/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5949 - acc: 0.7947 - val_loss: 0.6273 - val_acc: 0.7431
Epoch 336/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5214 - acc: 0.8119 - val_loss: 0.6318 - val_acc: 0.7569
Epoch 337/500
132/132 [==============================] - 44s 335ms/step - loss: 0.4977 - acc: 0.8204 - val_loss: 0.6797 - val_acc: 0.7385
Epoch 338/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5187 - acc: 0.8132 - val_loss: 0.7694 - val_acc: 0.7294
Epoch 339/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5700 - acc: 0.7983 - val_loss: 0.7504 - val_acc: 0.7569
Epoch 340/500
132/132 [==============================] - 44s 334ms/step - loss: 0.5003 - acc: 0.8239 - val_loss: 0.7221 - val_acc: 0.7523
Epoch 341/500
132/132 [==============================] - 44s 332ms/step - loss: 0.4965 - acc: 0.8285 - val_loss: 0.6871 - val_acc: 0.7752
Epoch 342/500
132/132 [==============================] - 44s 330ms/step - loss: 0.4884 - acc: 0.8227 - val_loss: 0.7127 - val_acc: 0.7523
Epoch 343/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5035 - acc: 0.8176 - val_loss: 0.6660 - val_acc: 0.7615
Epoch 344/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5348 - acc: 0.8075 - val_loss: 0.6796 - val_acc: 0.7661
Epoch 345/500
132/132 [==============================] - 43s 329ms/step - loss: 0.7774 - acc: 0.7422 - val_loss: 0.9460 - val_acc: 0.6789
Epoch 346/500
132/132 [==============================] - 43s 329ms/step - loss: 0.7501 - acc: 0.7268 - val_loss: 0.8654 - val_acc: 0.7018
Epoch 347/500
132/132 [==============================] - 44s 334ms/step - loss: 0.7628 - acc: 0.7148 - val_loss: 0.9274 - val_acc: 0.6284
Epoch 348/500
132/132 [==============================] - 43s 329ms/step - loss: 0.8779 - acc: 0.6687 - val_loss: 0.9314 - val_acc: 0.6514
Epoch 349/500
132/132 [==============================] - 43s 329ms/step - loss: 0.7324 - acc: 0.7257 - val_loss: 0.7607 - val_acc: 0.7385
Epoch 350/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6794 - acc: 0.7435 - val_loss: 0.8013 - val_acc: 0.7202
Epoch 351/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6330 - acc: 0.7659 - val_loss: 0.9112 - val_acc: 0.6789
Epoch 352/500
132/132 [==============================] - 43s 329ms/step - loss: 0.7313 - acc: 0.7277 - val_loss: 0.8744 - val_acc: 0.6835
Epoch 353/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7068 - acc: 0.7332 - val_loss: 0.8492 - val_acc: 0.6743
Epoch 354/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6472 - acc: 0.7519 - val_loss: 0.7950 - val_acc: 0.7294
Epoch 355/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6368 - acc: 0.7634 - val_loss: 0.7755 - val_acc: 0.7523
Epoch 356/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6540 - acc: 0.7576 - val_loss: 0.7593 - val_acc: 0.7431
Epoch 357/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6048 - acc: 0.7784 - val_loss: 0.7623 - val_acc: 0.7385
Epoch 358/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5957 - acc: 0.7828 - val_loss: 0.8326 - val_acc: 0.7156
Epoch 359/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6169 - acc: 0.7788 - val_loss: 0.7282 - val_acc: 0.7385
Epoch 360/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5833 - acc: 0.7859 - val_loss: 0.7653 - val_acc: 0.7294
Epoch 361/500
132/132 [==============================] - 45s 338ms/step - loss: 0.5850 - acc: 0.7812 - val_loss: 0.7147 - val_acc: 0.7431
Epoch 362/500
132/132 [==============================] - 43s 330ms/step - loss: 0.5476 - acc: 0.8029 - val_loss: 0.6991 - val_acc: 0.7523
Epoch 363/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6514 - acc: 0.7566 - val_loss: 0.8398 - val_acc: 0.6743
Epoch 364/500
132/132 [==============================] - 43s 329ms/step - loss: 0.9522 - acc: 0.6460 - val_loss: 1.1188 - val_acc: 0.5872
Epoch 365/500
132/132 [==============================] - 44s 334ms/step - loss: 0.8934 - acc: 0.6693 - val_loss: 0.9306 - val_acc: 0.6376
Epoch 366/500
132/132 [==============================] - 43s 329ms/step - loss: 0.7374 - acc: 0.7355 - val_loss: 0.7629 - val_acc: 0.7064
Epoch 367/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6568 - acc: 0.7626 - val_loss: 0.7527 - val_acc: 0.7294
Epoch 368/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6212 - acc: 0.7754 - val_loss: 0.6733 - val_acc: 0.7615
Epoch 369/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6885 - acc: 0.7577 - val_loss: 0.7604 - val_acc: 0.7202
Epoch 370/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6541 - acc: 0.7544 - val_loss: 0.7215 - val_acc: 0.7064
Epoch 371/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6392 - acc: 0.7678 - val_loss: 0.6934 - val_acc: 0.7615
Epoch 372/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6346 - acc: 0.7668 - val_loss: 0.6436 - val_acc: 0.7661
Epoch 373/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6276 - acc: 0.7707 - val_loss: 0.7944 - val_acc: 0.6927
Epoch 374/500
132/132 [==============================] - 44s 333ms/step - loss: 0.6000 - acc: 0.7663 - val_loss: 0.6232 - val_acc: 0.7752
Epoch 375/500
132/132 [==============================] - 45s 341ms/step - loss: 0.6548 - acc: 0.7590 - val_loss: 0.8466 - val_acc: 0.6743
Epoch 376/500
132/132 [==============================] - 45s 342ms/step - loss: 0.6554 - acc: 0.7540 - val_loss: 0.7179 - val_acc: 0.7431
Epoch 377/500
132/132 [==============================] - 45s 342ms/step - loss: 0.5675 - acc: 0.7904 - val_loss: 0.7973 - val_acc: 0.7064
Epoch 378/500
132/132 [==============================] - 45s 338ms/step - loss: 0.6287 - acc: 0.7716 - val_loss: 0.7249 - val_acc: 0.7385
Epoch 379/500
132/132 [==============================] - 44s 335ms/step - loss: 0.6603 - acc: 0.7477 - val_loss: 0.9155 - val_acc: 0.6514
Epoch 380/500
132/132 [==============================] - 44s 334ms/step - loss: 0.7618 - acc: 0.7047 - val_loss: 0.8770 - val_acc: 0.6606
Epoch 381/500
132/132 [==============================] - 44s 333ms/step - loss: 0.7179 - acc: 0.7221 - val_loss: 0.8842 - val_acc: 0.6514
Epoch 382/500
132/132 [==============================] - 44s 332ms/step - loss: 0.7243 - acc: 0.7229 - val_loss: 0.8097 - val_acc: 0.7018
Epoch 383/500
132/132 [==============================] - 44s 336ms/step - loss: 0.6948 - acc: 0.7443 - val_loss: 0.7569 - val_acc: 0.7110
Epoch 384/500
132/132 [==============================] - 44s 330ms/step - loss: 0.7103 - acc: 0.7354 - val_loss: 0.8843 - val_acc: 0.6835
Epoch 385/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6930 - acc: 0.7420 - val_loss: 0.7657 - val_acc: 0.7202
Epoch 386/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6777 - acc: 0.7407 - val_loss: 0.8095 - val_acc: 0.7064
Epoch 387/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6391 - acc: 0.7674 - val_loss: 0.7226 - val_acc: 0.7248
Epoch 388/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6079 - acc: 0.7710 - val_loss: 0.7659 - val_acc: 0.7202
Epoch 389/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6353 - acc: 0.7602 - val_loss: 0.7775 - val_acc: 0.7064
Epoch 390/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5923 - acc: 0.7853 - val_loss: 0.6173 - val_acc: 0.7706
Epoch 391/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5719 - acc: 0.7865 - val_loss: 0.6888 - val_acc: 0.7431
Epoch 392/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5530 - acc: 0.7972 - val_loss: 0.7176 - val_acc: 0.7294
Epoch 393/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5834 - acc: 0.7892 - val_loss: 0.6700 - val_acc: 0.7569
Epoch 394/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5719 - acc: 0.7882 - val_loss: 0.6602 - val_acc: 0.7431
Epoch 395/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5938 - acc: 0.7784 - val_loss: 0.7621 - val_acc: 0.7202
Epoch 396/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6187 - acc: 0.7773 - val_loss: 0.7755 - val_acc: 0.6835
Epoch 397/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5941 - acc: 0.7818 - val_loss: 0.6684 - val_acc: 0.7523
Epoch 398/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5784 - acc: 0.7876 - val_loss: 0.6934 - val_acc: 0.7477
Epoch 399/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5728 - acc: 0.7870 - val_loss: 0.7314 - val_acc: 0.7248
Epoch 400/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5823 - acc: 0.7885 - val_loss: 0.7386 - val_acc: 0.7339
Epoch 401/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6030 - acc: 0.7740 - val_loss: 0.8329 - val_acc: 0.7339
Epoch 402/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5633 - acc: 0.7939 - val_loss: 0.7203 - val_acc: 0.7477
Epoch 403/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6256 - acc: 0.7696 - val_loss: 0.8296 - val_acc: 0.6835
Epoch 404/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6012 - acc: 0.7801 - val_loss: 0.7526 - val_acc: 0.7248
Epoch 405/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6793 - acc: 0.7490 - val_loss: 0.7683 - val_acc: 0.7064
Epoch 406/500
132/132 [==============================] - 44s 335ms/step - loss: 0.6825 - acc: 0.7451 - val_loss: 0.7993 - val_acc: 0.6972
Epoch 407/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6293 - acc: 0.7676 - val_loss: 0.6917 - val_acc: 0.7477
Epoch 408/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6086 - acc: 0.7714 - val_loss: 0.6744 - val_acc: 0.7523
Epoch 409/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6338 - acc: 0.7635 - val_loss: 0.7736 - val_acc: 0.6972
Epoch 410/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6675 - acc: 0.7617 - val_loss: 0.7432 - val_acc: 0.7202
Epoch 411/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6130 - acc: 0.7769 - val_loss: 0.7125 - val_acc: 0.6927
Epoch 412/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6263 - acc: 0.7773 - val_loss: 0.6587 - val_acc: 0.7569
Epoch 413/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5557 - acc: 0.7965 - val_loss: 0.6914 - val_acc: 0.7523
Epoch 414/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5593 - acc: 0.7951 - val_loss: 0.7099 - val_acc: 0.7706
Epoch 415/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5380 - acc: 0.8039 - val_loss: 0.6668 - val_acc: 0.7752
Epoch 416/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5497 - acc: 0.8008 - val_loss: 0.6681 - val_acc: 0.7523
Epoch 417/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5513 - acc: 0.7935 - val_loss: 0.6763 - val_acc: 0.7523
Epoch 418/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6049 - acc: 0.7739 - val_loss: 0.7467 - val_acc: 0.7339
Epoch 419/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5940 - acc: 0.7824 - val_loss: 0.8109 - val_acc: 0.7018
Epoch 420/500
132/132 [==============================] - 44s 335ms/step - loss: 0.6068 - acc: 0.7731 - val_loss: 0.6997 - val_acc: 0.7385
Epoch 421/500
132/132 [==============================] - 44s 334ms/step - loss: 0.6169 - acc: 0.7724 - val_loss: 0.7508 - val_acc: 0.7202
Epoch 422/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6242 - acc: 0.7611 - val_loss: 0.7066 - val_acc: 0.7339
Epoch 423/500
132/132 [==============================] - 44s 332ms/step - loss: 0.5995 - acc: 0.7795 - val_loss: 0.7261 - val_acc: 0.7294
Epoch 424/500
132/132 [==============================] - 44s 330ms/step - loss: 0.5882 - acc: 0.7807 - val_loss: 0.7644 - val_acc: 0.7110
Epoch 425/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6175 - acc: 0.7685 - val_loss: 0.6769 - val_acc: 0.7294
Epoch 426/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5670 - acc: 0.7954 - val_loss: 0.6329 - val_acc: 0.7661
Epoch 427/500
132/132 [==============================] - 44s 334ms/step - loss: 0.6296 - acc: 0.7610 - val_loss: 0.7653 - val_acc: 0.7018
Epoch 428/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6435 - acc: 0.7604 - val_loss: 0.7240 - val_acc: 0.7248
Epoch 429/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6451 - acc: 0.7617 - val_loss: 0.7146 - val_acc: 0.7339
Epoch 430/500
132/132 [==============================] - 44s 332ms/step - loss: 0.6064 - acc: 0.7707 - val_loss: 0.6980 - val_acc: 0.7248
Epoch 431/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6099 - acc: 0.7682 - val_loss: 0.7212 - val_acc: 0.7294
Epoch 432/500
132/132 [==============================] - 44s 333ms/step - loss: 0.5531 - acc: 0.7982 - val_loss: 0.6261 - val_acc: 0.7615
Epoch 433/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5606 - acc: 0.7958 - val_loss: 0.6753 - val_acc: 0.7523
Epoch 434/500
132/132 [==============================] - 44s 335ms/step - loss: 0.5548 - acc: 0.7935 - val_loss: 0.6418 - val_acc: 0.7706
Epoch 435/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5389 - acc: 0.8020 - val_loss: 0.7433 - val_acc: 0.7339
Epoch 436/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5562 - acc: 0.7942 - val_loss: 0.7026 - val_acc: 0.7661
Epoch 437/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5405 - acc: 0.8023 - val_loss: 0.7782 - val_acc: 0.7156
Epoch 438/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5583 - acc: 0.7882 - val_loss: 0.7133 - val_acc: 0.7615
Epoch 439/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6092 - acc: 0.7772 - val_loss: 0.7699 - val_acc: 0.7431
Epoch 440/500
132/132 [==============================] - 43s 327ms/step - loss: 0.6073 - acc: 0.7809 - val_loss: 0.8975 - val_acc: 0.6927
Epoch 441/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6035 - acc: 0.7754 - val_loss: 0.6718 - val_acc: 0.7385
Epoch 442/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5526 - acc: 0.8001 - val_loss: 0.7230 - val_acc: 0.7385
Epoch 443/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5625 - acc: 0.7945 - val_loss: 0.8586 - val_acc: 0.7156
Epoch 444/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6238 - acc: 0.7675 - val_loss: 0.7429 - val_acc: 0.7339
Epoch 445/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5976 - acc: 0.7754 - val_loss: 0.7710 - val_acc: 0.7248
Epoch 446/500
132/132 [==============================] - 44s 331ms/step - loss: 0.5830 - acc: 0.7836 - val_loss: 0.6846 - val_acc: 0.7385
Epoch 447/500
132/132 [==============================] - 43s 329ms/step - loss: 0.6111 - acc: 0.7748 - val_loss: 0.6865 - val_acc: 0.7477
Epoch 448/500
132/132 [==============================] - 44s 335ms/step - loss: 0.6421 - acc: 0.7635 - val_loss: 0.7339 - val_acc: 0.7248
Epoch 449/500
132/132 [==============================] - 44s 330ms/step - loss: 0.6110 - acc: 0.7801 - val_loss: 0.7211 - val_acc: 0.7202
Epoch 450/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5807 - acc: 0.7862 - val_loss: 0.6949 - val_acc: 0.7477
Epoch 451/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6044 - acc: 0.7760 - val_loss: 0.7101 - val_acc: 0.7202
Epoch 452/500
132/132 [==============================] - 44s 331ms/step - loss: 0.6093 - acc: 0.7782 - val_loss: 0.7396 - val_acc: 0.7385
Epoch 453/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5743 - acc: 0.7903 - val_loss: 0.7426 - val_acc: 0.7569
Epoch 454/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5917 - acc: 0.7803 - val_loss: 0.7869 - val_acc: 0.7339
Epoch 455/500
132/132 [==============================] - 43s 329ms/step - loss: 0.5853 - acc: 0.7883 - val_loss: 0.7838 - val_acc: 0.7431
Epoch 456/500
132/132 [==============================] - 43s 328ms/step - loss: 0.6230 - acc: 0.7738 - val_loss: 0.7464 - val_acc: 0.7202
Epoch 457/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5781 - acc: 0.7849 - val_loss: 0.6273 - val_acc: 0.7569
Epoch 458/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5596 - acc: 0.7925 - val_loss: 0.6067 - val_acc: 0.7798
Epoch 459/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5546 - acc: 0.7967 - val_loss: 0.6808 - val_acc: 0.7661
Epoch 460/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5791 - acc: 0.7779 - val_loss: 0.6970 - val_acc: 0.7294
Epoch 461/500
132/132 [==============================] - 43s 328ms/step - loss: 0.5857 - acc: 0.7836 - val_loss: 0.7297 - val_acc: 0.7248
Epoch 462/500
132/132 [==============================] - 44s 336ms/step - loss: 0.5674 - acc: 0.7908 - val_loss: 0.7160 - val_acc: 0.7385
Epoch 463/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5416 - acc: 0.7966 - val_loss: 0.6955 - val_acc: 0.7523
Epoch 464/500
132/132 [==============================] - 43s 327ms/step - loss: 0.5758 - acc: 0.7854 - val_loss: 0.6905 - val_acc: 0.7339
Epoch 465/500
132/132 [==============================] - 43s 327ms/step - loss: 1.2264 - acc: 0.5328 - val_loss: 1.1161 - val_acc: 0.5321
Epoch 466/500
132/132 [==============================] - 43s 327ms/step - loss: 1.1298 - acc: 0.5512 - val_loss: 1.1306 - val_acc: 0.5734
Epoch 467/500
132/132 [==============================] - 43s 327ms/step - loss: 1.1191 - acc: 0.5482 - val_loss: 1.1462 - val_acc: 0.5092
Epoch 468/500
132/132 [==============================] - 43s 329ms/step - loss: 1.0753 - acc: 0.5816 - val_loss: 1.0545 - val_acc: 0.5688
Epoch 469/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9828 - acc: 0.6301 - val_loss: 0.9804 - val_acc: 0.6239
Epoch 470/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9555 - acc: 0.6288 - val_loss: 0.9911 - val_acc: 0.6147
Epoch 471/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9168 - acc: 0.6393 - val_loss: 0.9136 - val_acc: 0.6560
Epoch 472/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9481 - acc: 0.6296 - val_loss: 1.1124 - val_acc: 0.5413
Epoch 473/500
132/132 [==============================] - 43s 327ms/step - loss: 1.0992 - acc: 0.5646 - val_loss: 1.1174 - val_acc: 0.5642
Epoch 474/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9936 - acc: 0.6241 - val_loss: 1.0212 - val_acc: 0.5826
Epoch 475/500
132/132 [==============================] - 43s 327ms/step - loss: 0.9524 - acc: 0.6311 - val_loss: 1.2036 - val_acc: 0.5963
Epoch 476/500
132/132 [==============================] - 44s 332ms/step - loss: 0.9502 - acc: 0.6397 - val_loss: 0.9984 - val_acc: 0.6147
Epoch 477/500
132/132 [==============================] - 44s 331ms/step - loss: 0.8701 - acc: 0.6673 - val_loss: 0.9643 - val_acc: 0.6376
Epoch 478/500
132/132 [==============================] - 44s 330ms/step - loss: 0.8278 - acc: 0.6733 - val_loss: 0.8738 - val_acc: 0.6284
Epoch 479/500
132/132 [==============================] - 43s 329ms/step - loss: 0.8177 - acc: 0.6800 - val_loss: 0.8950 - val_acc: 0.6376
Epoch 480/500
132/132 [==============================] - 44s 330ms/step - loss: 0.9256 - acc: 0.6494 - val_loss: 1.1360 - val_acc: 0.5183
Epoch 481/500
132/132 [==============================] - 44s 331ms/step - loss: 1.0523 - acc: 0.5892 - val_loss: 1.1415 - val_acc: 0.5826
Epoch 482/500
132/132 [==============================] - 44s 330ms/step - loss: 1.0149 - acc: 0.6117 - val_loss: 1.0320 - val_acc: 0.5596
Epoch 483/500
132/132 [==============================] - 44s 336ms/step - loss: 0.8769 - acc: 0.6544 - val_loss: 0.9545 - val_acc: 0.6101
Epoch 484/500
132/132 [==============================] - 46s 347ms/step - loss: 0.8978 - acc: 0.6541 - val_loss: 1.0379 - val_acc: 0.5596
Epoch 485/500
132/132 [==============================] - 46s 346ms/step - loss: 0.8819 - acc: 0.6585 - val_loss: 0.9615 - val_acc: 0.6193
Epoch 486/500
132/132 [==============================] - 45s 338ms/step - loss: 0.8318 - acc: 0.6802 - val_loss: 0.8219 - val_acc: 0.6651
Epoch 487/500
132/132 [==============================] - 44s 336ms/step - loss: 0.7765 - acc: 0.6960 - val_loss: 0.8912 - val_acc: 0.6835
Epoch 488/500
132/132 [==============================] - 44s 336ms/step - loss: 0.7568 - acc: 0.7023 - val_loss: 0.8427 - val_acc: 0.6651
Epoch 489/500
132/132 [==============================] - 44s 337ms/step - loss: 0.7544 - acc: 0.7079 - val_loss: 0.7962 - val_acc: 0.6881
Epoch 490/500
132/132 [==============================] - 45s 340ms/step - loss: 0.7508 - acc: 0.7032 - val_loss: 0.8604 - val_acc: 0.6514
Epoch 491/500
132/132 [==============================] - 44s 335ms/step - loss: 0.7237 - acc: 0.7124 - val_loss: 0.7528 - val_acc: 0.7064
Epoch 492/500
132/132 [==============================] - 44s 335ms/step - loss: 0.7540 - acc: 0.7021 - val_loss: 0.8571 - val_acc: 0.6743
Epoch 493/500
132/132 [==============================] - 44s 335ms/step - loss: 0.7508 - acc: 0.7118 - val_loss: 0.8592 - val_acc: 0.6330
Epoch 494/500
132/132 [==============================] - 45s 337ms/step - loss: 0.7409 - acc: 0.7135 - val_loss: 0.8600 - val_acc: 0.6697
Epoch 495/500
132/132 [==============================] - 44s 337ms/step - loss: 0.7426 - acc: 0.7128 - val_loss: 0.8541 - val_acc: 0.6697
Epoch 496/500
132/132 [==============================] - 45s 339ms/step - loss: 0.7542 - acc: 0.7122 - val_loss: 0.8150 - val_acc: 0.6560
Epoch 497/500
132/132 [==============================] - 45s 340ms/step - loss: 0.7240 - acc: 0.7155 - val_loss: 0.7585 - val_acc: 0.6835
Epoch 498/500
132/132 [==============================] - 45s 338ms/step - loss: 0.7421 - acc: 0.7162 - val_loss: 0.7433 - val_acc: 0.7110
Epoch 499/500
132/132 [==============================] - 45s 338ms/step - loss: 0.7011 - acc: 0.7310 - val_loss: 0.7754 - val_acc: 0.6651
Epoch 500/500
132/132 [==============================] - 45s 339ms/step - loss: 0.6899 - acc: 0.7232 - val_loss: 0.7346 - val_acc: 0.6972
40/40 [==============================] - 5s 113ms/step
Accuracy: 0.7277805992889792. F1: 0.7229566172510475
</pre>
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<div class="prompt input_prompt">In&nbsp;[18]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_2D_small_0.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_2D_small_1.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_2D_small_2.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_2D_small_3.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_1D_norm.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_LSTM_norm.hdf5&#39;</span><span class="p">),</span> 
          <span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;models/model_Com_norm.hdf5&#39;</span><span class="p">)]</span>
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<pre>WARNING:tensorflow:From c:\users\jerem_000\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\util\deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
</pre>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_model</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">test_model</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">models</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">file_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
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<pre>40/40 [==============================] - 3s 71ms/step
Accuracy: 0.5642458100558659. F1: 0.5559214378924485
40/40 [==============================] - 3s 70ms/step
Accuracy: 0.7953275774504824. F1: 0.7944290220418879
40/40 [==============================] - 21s 517ms/step
Accuracy: 0.8110716099542915. F1: 0.8099749109721136
40/40 [==============================] - 22s 548ms/step
Accuracy: 0.8689690198070086. F1: 0.865455499361151
40/40 [==============================] - 7s 167ms/step
Accuracy: 0.8969019807008634. F1: 0.8958705030987514
40/40 [==============================] - 5s 128ms/step
Accuracy: 0.8806500761808025. F1: 0.8800810128115094
40/40 [==============================] - 4s 108ms/step
Accuracy: 0.8364652107668867. F1: 0.8318890576416703
</pre>
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<div class="prompt input_prompt">In&nbsp;[34]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="n">avg_models</span> <span class="o">=</span> <span class="n">models</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">file_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">test_model_avg</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">avg_models</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">file_types</span><span class="p">)</span>
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<pre>40/40 [==============================] - 7s 178ms/step
40/40 [==============================] - 4s 111ms/step
Accuracy: 0.9116302691721686. F1: 0.9107363152807513
</pre>
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</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
<span class="n">avg_models</span> <span class="o">=</span> <span class="n">models</span>
<span class="n">weights</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="o">.</span><span class="mi">4</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">file_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">test_model_avg</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">avg_models</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">file_types</span><span class="p">)</span>
</pre></div>

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<pre>40/40 [==============================] - 3s 81ms/step
40/40 [==============================] - 6s 162ms/step
40/40 [==============================] - 4s 98ms/step
Accuracy: 0.9233113255459624. F1: 0.9219258532903954
</pre>
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