<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/plotting-deep-learning-model-performance-trajectories/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://www.win-vector.com/blog/2017/12/plotting-deep-learning-model-performance-trajectories/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I am excited to share a new deep learning model performance trajectory graph.</p>
<p>Here is an example produced based on Keras in R using ggplot2:</p>
<p><img border="0" src="https://i1.wp.com/www.win-vector.com/blog/wp-content/uploads/2017/12/Unknown.png?resize=599%2C428" alt="Unknown" width="450" title="Unknown.png" data-recalc-dims="1"><span></span></p>
<p>
<p>The ideas include:</p>
<ul>
<li>We plot model performance as a function of training epoch, data set (training and validation), and metric.</li>
<li>For legibility we facet on metric, and facets are adjusted so all facets have the same visual interpretation (“up is better”).</li>
<li>The only solid horizontal curve is validation performance, and training performance is only indicated as the top-region of a shared region that depicts degree of over-fit.</li>
</ul>
<p>Obviously is going to take some training and practice to read these graphs quickly: but that is petty much true for all visualizations.</p>
<p>The methods work with just about any staged machine learning algorithm (neural nets, deep learning, boosting, random forests, and more) and can also be adapted to non-staged bug regularized methods (lasso, elastic net, and so on). </p>
<p>The graph is now part of the <a rel="nofollow" href="https://winvector.github.io/WVPlots/reference/plot_Keras_fit_trajectory.html" target="_blank">development version of WVPlots</a>. And we have complete worked examples for <a rel="nofollow" href="http://winvector.github.io/FluidData/PlotExample/KerasPerfPlot.html" target="_blank">Keras</a> and <a rel="nofollow" href="http://winvector.github.io/FluidData/PlotExample/xgboostPerfPlot.html" target="_blank">xgboost</a>.</p>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.win-vector.com/blog/2017/12/plotting-deep-learning-model-performance-trajectories/"> R – Win-Vector Blog</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/7ozn9OOe_D4" height="1"></a></div>