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class="row"> <div aria-label="main navigation" 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="customized-loss-function"> <h1>Customized loss function<a class="headerlink" href="#customized-loss-function" title="Permalink to this headline">¶</a></h1> This tutorial provides guidelines for using customized loss function in network construction. <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 regression example. We can build and train a regression model with the following code: <div class="highlight-r"><div class="highlight"><pre>data(BostonHousing, package = "mlbench") BostonHousing[, sapply(BostonHousing, is.factor)] <- as.numeric(as.character(BostonHousing[, sapply(BostonHousing, is.factor)])) BostonHousing <- data.frame(scale(BostonHousing)) test.ind = seq(1, 506, 5) # 1 pt in 5 used for testing train.x = data.matrix(BostonHousing[-test.ind,-14]) train.y = BostonHousing[-test.ind, 14] test.x = data.matrix(BostonHousing[--test.ind,-14]) test.y = BostonHousing[--test.ind, 14] require(mxnet) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Loading required package: mxnet </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>data <- mx.symbol.Variable("data") label <- mx.symbol.Variable("label") fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1") tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1") fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2") lro <- mx.symbol.LinearRegressionOutput(fc2, name = "lro") mx.set.seed(0) model <- mx.model.FeedForward.create(lro, X = train.x, y = train.y, ctx = mx.cpu(), num.round = 5, array.batch.size = 60, optimizer = "rmsprop", verbose = TRUE, array.layout = "rowmajor", batch.end.callback = NULL, epoch.end.callback = NULL) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Start training with 1 devices </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>pred <- predict(model, test.x) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>sum((test.y - pred[1,])^2) / length(test.y) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## [1] 0.2485236 </pre></div> </div> Besides the <code class="docutils literal">LinearRegressionOutput</code>, we also provide <code class="docutils literal">LogisticRegressionOutput</code> and <code class="docutils literal">MAERegressionOutput</code>. However, this might not be enough for real-world models. You can provide your own loss function by using <code class="docutils literal">mx.symbol.MakeLoss</code> when constructing the network. </div> <div class="section" id="how-to-use-your-own-loss-function"> <h2>How to Use Your Own Loss Function<a class="headerlink" href="#how-to-use-your-own-loss-function" title="Permalink to this headline">¶</a></h2> We still use our previous example, but this time we use <code class="docutils literal">mx.symbol.MakeLoss</code> to minimize the <code class="docutils literal">(pred-label)^2</code> <div class="highlight-r"><div class="highlight"><pre>data <- mx.symbol.Variable("data") label <- mx.symbol.Variable("label") fc1 <- mx.symbol.FullyConnected(data, num_hidden = 14, name = "fc1") tanh1 <- mx.symbol.Activation(fc1, act_type = "tanh", name = "tanh1") fc2 <- mx.symbol.FullyConnected(tanh1, num_hidden = 1, name = "fc2") lro2 <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc2, shape = 0) - label), name="lro2") </pre></div> </div> Then we can train the network just as usual. <div class="highlight-r"><div class="highlight"><pre>mx.set.seed(0) model2 <- mx.model.FeedForward.create(lro2, X = train.x, y = train.y, ctx = mx.cpu(), num.round = 5, array.batch.size = 60, optimizer = "rmsprop", verbose = TRUE, array.layout = "rowmajor", batch.end.callback = NULL, epoch.end.callback = NULL) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Start training with 1 devices </pre></div> </div> We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different. <div class="highlight-r"><div class="highlight"><pre>pred2 <- predict(model2, test.x) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>sum((test.y - pred2)^2) / length(test.y) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## [1] 1.234584 </pre></div> </div> This is because output of <code class="docutils literal">mx.symbol.MakeLoss</code> is the gradient of loss with respect to the input data. We can get the real prediction as below. <div class="highlight-r"><div class="highlight"><pre>internals = internals(model2$symbol) fc_symbol = internals[[match("fc2_output", outputs(internals))]] model3 <- list(symbol = fc_symbol, arg.params = model2$arg.params, aux.params = model2$aux.params) class(model3) <- "MXFeedForwardModel" pred3 <- predict(model3, test.x) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>sum((test.y - pred3[1,])^2) / length(test.y) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## [1] 0.248294 </pre></div> </div> We have provided many operations on the symbols. An example of <code class="docutils literal">|pred-label|</code> can be found below. <div class="highlight-r"><div class="highlight"><pre>lro_abs <- mx.symbol.MakeLoss(mx.symbol.abs(mx.symbol.Reshape(fc2, shape = 0) - label)) mx.set.seed(0) model4 <- mx.model.FeedForward.create(lro_abs, X = train.x, y = train.y, ctx = mx.cpu(), num.round = 20, array.batch.size = 60, optimizer = "sgd", learning.rate = 0.001, verbose = TRUE, array.layout = "rowmajor", batch.end.callback = NULL, epoch.end.callback = NULL) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Start training with 1 devices </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>internals = internals(model4$symbol) fc_symbol = internals[[match("fc2_output", outputs(internals))]] model5 <- list(symbol = fc_symbol, arg.params = model4$arg.params, aux.params = model4$aux.params) class(model5) <- "MXFeedForwardModel" pred5 <- predict(model5, test.x) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>sum(abs(test.y - pred5[1,])) / length(test.y) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## [1] 0.7056902 </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>lro_mae <- mx.symbol.MAERegressionOutput(fc2, name = "lro") mx.set.seed(0) model6 <- mx.model.FeedForward.create(lro_mae, X = train.x, y = train.y, ctx = mx.cpu(), num.round = 20, array.batch.size = 60, optimizer = "sgd", learning.rate = 0.001, verbose = TRUE, array.layout = "rowmajor", batch.end.callback = NULL, epoch.end.callback = NULL) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Start training with 1 devices </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>pred6 <- predict(model6, test.x) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## Warning in mx.model.select.layout.predict(X, model): Auto detect layout of input matrix, use rowmajor.. </pre></div> </div> <div class="highlight-r"><div class="highlight"><pre>sum(abs(test.y - pred6[1,])) / length(test.y) </pre></div> </div> <div class="highlight-default"><div class="highlight"><pre>## [1] 0.7056902 </pre></div> </div> </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="#">Customized loss 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-your-own-loss-function">How to Use Your Own Loss Function</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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