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class="sphinxsidebar leftsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <ul> <li class="toctree-l1"><a class="reference internal" href="../../api/python/index.html">Python Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../api/r/index.html">R Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../api/julia/index.html">Julia Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../api/c++/index.html">C++ Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../api/scala/index.html">Scala Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../api/perl/index.html">Perl Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../how_to/index.html">HowTo Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../architecture/index.html">System Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../index.html">Tutorials</a></li> </ul> </div> </div> <div class="content"> <div class="page-tracker"></div> <div class="section" id="callback-function"> <h1>Callback Function<a class="headerlink" href="#callback-function" title="Permalink to this headline">¶</a></h1> This tutorial provides guidelines for using and writing callback functions, which can very useful in model training. <div class="section" id="model-training-example"> <h2>Model Training Example<a class="headerlink" href="#model-training-example" title="Permalink to this headline">¶</a></h2> Let’s begin with a small example. We can build and train a model with the following code: <div class="highlight-r"><div class="highlight"><pre> library(mxnet) data(BostonHousing, package="mlbench") train.ind = seq(1, 506, 3) train.x = data.matrix(BostonHousing[train.ind, -14]) train.y = BostonHousing[train.ind, 14] test.x = data.matrix(BostonHousing[-train.ind, -14]) test.y = BostonHousing[-train.ind, 14] data <- mx.symbol.Variable("data") fc1 <- mx.symbol.FullyConnected(data, num_hidden=1) lro <- mx.symbol.LinearRegressionOutput(fc1) mx.set.seed(0) model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=16.063282524034 ## [1] Validation-rmse=10.1766446093622 ## [2] Train-rmse=12.2792375712573 ## [2] Validation-rmse=12.4331776190813 ## [3] Train-rmse=11.1984634005885 ## [3] Validation-rmse=10.3303041888193 ## [4] Train-rmse=10.2645236892904 ## [4] Validation-rmse=8.42760407903415 ## [5] Train-rmse=9.49711005504284 ## [5] Validation-rmse=8.44557808483234 ## [6] Train-rmse=9.07733734175182 ## [6] Validation-rmse=8.33225500266177 ## [7] Train-rmse=9.07884450847991 ## [7] Validation-rmse=8.38827833418459 ## [8] Train-rmse=9.10463850277417 ## [8] Validation-rmse=8.37394452365264 ## [9] Train-rmse=9.03977049028532 ## [9] Validation-rmse=8.25927979725672 ## [10] Train-rmse=8.96870685004475 ## [10] Validation-rmse=8.19509291481822 </pre></div> </div> We also provide two optional parameters, <code class="docutils literal">batch.end.callback</code> and <code class="docutils literal">epoch.end.callback</code>, which can provide great flexibility in model training. </div> <div class="section" id="how-to-use-callback-functions"> <h2>How to Use Callback Functions<a class="headerlink" href="#how-to-use-callback-functions" title="Permalink to this headline">¶</a></h2> This package provides two callback functions: <ul class="simple"> <li><code class="docutils literal">mx.callback.save.checkpoint</code> saves a checkpoint to files during each period iteration.</li> </ul> <div class="highlight-r"><div class="highlight"><pre> model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.save.checkpoint("boston")) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=19.1621424021617 ## [1] Validation-rmse=20.721515592165 ## Model checkpoint saved to boston-0001.params ## [2] Train-rmse=13.5127391952367 ## [2] Validation-rmse=14.1822123675007 ## Model checkpoint saved to boston-0002.params </pre></div> </div> <ul class="simple"> <li><code class="docutils literal">mx.callback.log.train.metric</code> logs a training metric each period. You can use it either as a <code class="docutils literal">batch.end.callback</code> or an <code class="docutils literal">epoch.end.callback</code>.</li> </ul> <div class="highlight-r"><div class="highlight"><pre> model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, batch.end.callback = mx.callback.log.train.metric(5)) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## Batch [5] Train-rmse=17.6514558545416 ## [1] Train-rmse=15.2879610219001 ## [1] Validation-rmse=12.3332062820921 ## Batch [5] Train-rmse=11.939392828565 ## [2] Train-rmse=11.4382242547217 ## [2] Validation-rmse=9.91176550103181 ............ </pre></div> </div> You also can save the training and evaluation errors for later use by passing a reference class: <div class="highlight-r"><div class="highlight"><pre> logger <- mx.metric.logger$new() model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.log.train.metric(5, logger)) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=19.1083228733256 ## [1] Validation-rmse=12.7150687428974 ## [2] Train-rmse=15.7684378116157 ## [2] Validation-rmse=14.8105319420491 ............ </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre> head(logger$train) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## [1] 19.108323 15.768438 13.531470 11.386050 9.555477 9.351324 </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre> head(logger$eval) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## [1] 12.715069 14.810532 15.840361 10.898733 9.349706 9.363087 </pre></div> </div> </div> <div class="section" id="how-to-write-your-own-callback-functions"> <h2>How to Write Your Own Callback Functions<a class="headerlink" href="#how-to-write-your-own-callback-functions" title="Permalink to this headline">¶</a></h2> You can find the source code for the two callback functions on <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/R-package/R/callback.R">GitHub</a> and use it as a template: Basically, all callback functions follow the following structure: <div class="highlight-r"><div class="highlight"><pre> mx.callback.fun <- function() { function(iteration, nbatch, env) { } } </pre></div> </div> The following <code class="docutils literal">mx.callback.save.checkpoint</code> function is stateless. It gets the model from the environment and saves it:. <div class="highlight-r"><div class="highlight"><pre> mx.callback.save.checkpoint <- function(prefix, period=1) { function(iteration, nbatch, env) { if (iteration %% period == 0) { mx.model.save(env$model, prefix, iteration) cat(sprintf("Model checkpoint saved to %s-%04d.params\n", prefix, iteration)) } return(TRUE) } } </pre></div> </div> The <code class="docutils literal">mx.callback.log.train.metric</code> is a little more complex. It holds a reference class and updates it during the training process: <div class="highlight-r"><div class="highlight"><pre> mx.callback.log.train.metric <- function(period, logger=NULL) { function(iteration, nbatch, env) { if (nbatch %% period == 0 && !is.null(env$metric)) { result <- env$metric$get(env$train.metric) if (nbatch != 0) cat(paste0("Batch [", nbatch, "] Train-", result$name, "=", result$value, "\n")) if (!is.null(logger)) { if (class(logger) != "mx.metric.logger") { stop("Invalid mx.metric.logger.") } logger$train <- c(logger$train, result$value) if (!is.null(env$eval.metric)) { result <- env$metric$get(env$eval.metric) if (nbatch != 0) cat(paste0("Batch [", nbatch, "] Validation-", result$name, "=", result$value, "\n")) logger$eval <- c(logger$eval, result$value) } } } return(TRUE) } } </pre></div> </div> Now you might be curious why both callback functions <code class="docutils literal">return(TRUE)</code>. Can we <code class="docutils literal">return(FALSE)</code>? Yes! You can stop the training early with <code class="docutils literal">return(FALSE)</code>. See the following examples. <div class="highlight-r"><div class="highlight"><pre> mx.callback.early.stop <- function(eval.metric) { function(iteration, nbatch, env) { if (!is.null(env$metric)) { if (!is.null(eval.metric)) { result <- env$metric$get(env$eval.metric) if (result$value < eval.metric) { return(FALSE) } } } return(TRUE) } } model <- mx.model.FeedForward.create( lro, X=train.x, y=train.y, eval.data=list(data=test.x, label=test.y), ctx=mx.cpu(), num.round=10, array.batch.size=20, learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse, epoch.end.callback = mx.callback.early.stop(10)) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre> ## Auto detect layout of input matrix, use row major.. ## Start training with 1 devices ## [1] Train-rmse=18.5897984387033 ## [1] Validation-rmse=13.5555213820571 ## [2] Train-rmse=12.5867564040256 ## [2] Validation-rmse=9.76304967080928 </pre></div> </div> When the validation metric dips below the threshold we set, the training process stops. </div> <div class="section" id="next-steps"> <h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h2> <div class="toctree-wrapper compound"> <ul> <li class="toctree-l1"><a class="reference external" href="/versions/0.11.0/tutorials/r/fiveMinutesNeuralNetwork.html">Neural Networks with MXNet in Five Minutes</a></li> <li class="toctree-l1"><a class="reference external" href="/versions/0.11.0/tutorials/r/classifyRealImageWithPretrainedModel.html">Classify Real-World Images with a Pretrained Model</a></li> <li class="toctree-l1"><a class="reference external" href="/versions/0.11.0/tutorials/r/mnistCompetition.html">Handwritten Digits Classification Competition</a></li> <li class="toctree-l1"><a class="reference external" href="/versions/0.11.0/tutorials/r/charRnnModel.html">Character Language Model Using RNN</a></li> </ul> </div> </div> </div> </div> </div> <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <h3><a href="../../index.html">Table Of Contents</a></h3> <ul> <li><a class="reference internal" href="#">Callback Function</a><ul> <li><a class="reference internal" href="#model-training-example">Model Training Example</a></li> <li><a class="reference internal" href="#how-to-use-callback-functions">How to Use Callback Functions</a></li> <li><a class="reference internal" href="#how-to-write-your-own-callback-functions">How to Write Your Own Callback Functions</a></li> <li><a class="reference internal" href="#next-steps">Next Steps</a></li> </ul> </li> </ul> </div> </div> </div><div class="footer"> <div class="section-disclaimer"> <div class="container"> <div> <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. 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