Neural Structured Learning

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</li> </ul> </div> <devsite-feature-tooltip ack-key="AckCollectionsBookmarkTooltipDismiss" analytics-category="Site-Wide Custom Events" analytics-action-show="Callout Profile displayed" analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> <span slot="popout-heading"> Stay organized with collections </span> <span slot="popout-contents"> Save and categorize content based on your preferences. </span> </devsite-feature-tooltip> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded disabled> </devsite-toc> <div class="devsite-article-body clearfix devsite-no-page-title"> <section class="devsite-landing-row devsite-landing-row-2-up" header-position="top" > <div class="devsite-landing-row-inner"> <header class="devsite-landing-row-header" > <div class="devsite-landing-row-header-text"> <h2 id="neural-structured-learning-training-with-structured-signals" data-text="Neural Structured Learning: Training with Structured Signals" tabindex="0"> Neural Structured Learning: Training with Structured Signals </h2> </div> </header> <div class="devsite-landing-row-group"> <div class="devsite-landing-row-item devsite-landing-row-item-no-media" description-position="bottom" > <div class="devsite-landing-row-item-description" > <div class="devsite-landing-row-item-body"> <div class="devsite-landing-row-item-description-content">  <figure> <img src="/static/neural_structured_learning/images/nsl_overview.png"> </figure> <p><b>Neural Structured Learning (NSL)</b> is a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation.</p> <p>Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when <b>the amount of labeled data is relatively small</b>. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be <b>robust against malicious attacks</b>, which are designed to mislead a model's prediction or classification.</p> <p>NSL generalizes to <a href="https://research.google/pubs/pub46568.pdf">Neural Graph Learning</a> as well as <a href="https://arxiv.org/pdf/1412.6572.pdf">Adversarial Learning</a>. The NSL framework in TensorFlow provides the following easy-to-use APIs and tools for developers to train models with structured signals:</p> <ul style="padding-left: 20px;"> <li><b>Keras APIs</b> to enable training with graphs (explicit structure) and adversarial perturbations (implicit structure).</li> <li><b>TF ops and functions</b> to enable training with structure when using lower-level TensorFlow APIs</li> <li><b>Tools</b> to build graphs and construct graph inputs for training</li> </ul> <p>Incorporating structured signals is done only during training. So, the performance of the serving/inference workflow remains unchanged. More information on neural structured learning can be found in our <a href="/neural_structured_learning/framework">framework description</a>. To get started, please see our <a href="/neural_structured_learning/install"> install guide</a>, and for a practical introduction to NSL, check out our <a href="/neural_structured_learning/framework#step-by-step_tutorials">tutorials.</a></p> </div> </div> </div> </div> <div class="devsite-landing-row-item devsite-landing-row-item-no-description" description-position="bottom" > <div class="devsite-landing-row-item-media "> <div class="devsite-landing-row-item-code"> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><span class="devsite-syntax-kn">import</span> <span class="devsite-syntax-nn">tensorflow</span> <span class="devsite-syntax-k">as</span> <span class="devsite-syntax-nn">tf</span> <span class="devsite-syntax-kn">import</span> <span class="devsite-syntax-nn">neural_structured_learning</span> <span class="devsite-syntax-k">as</span> <span class="devsite-syntax-nn">nsl</span> <span class="devsite-syntax-c1"># Prepare data.</span> <span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_train</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_train</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">datasets</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">mnist</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">load_data</span><span class="devsite-syntax-p">()</span> <span class="devsite-syntax-n">x_train</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">x_test</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">x_train</span> <span class="devsite-syntax-o">/</span> <span class="devsite-syntax-mf">255.0</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">x_test</span> <span class="devsite-syntax-o">/</span> <span class="devsite-syntax-mf">255.0</span> <span class="devsite-syntax-c1"># Create a base model -- sequential, functional, or subclass.</span> <span class="devsite-syntax-n">model</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Sequential</span><span class="devsite-syntax-p">([</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Input</span><span class="devsite-syntax-p">((</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">name</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'feature'</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Flatten</span><span class="devsite-syntax-p">(),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">128</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">nn</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">relu</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">nn</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">softmax</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-p">])</span> <span class="devsite-syntax-c1"># Wrap the model with adversarial regularization.</span> <span class="devsite-syntax-n">adv_config</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">nsl</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">configs</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">make_adv_reg_config</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">multiplier</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mf">0.2</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">adv_step_size</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mf">0.05</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-n">adv_model</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">nsl</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">AdversarialRegularization</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">model</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">adv_config</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-n">adv_config</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-c1"># Compile, train, and evaluate.</span> <span class="devsite-syntax-n">adv_model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">compile</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">optimizer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'adam'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">loss</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'sparse_categorical_crossentropy'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">metrics</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">[</span><span class="devsite-syntax-s1">'accuracy'</span><span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">adv_model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">fit</span><span class="devsite-syntax-p">({</span><span class="devsite-syntax-s1">'feature'</span><span class="devsite-syntax-p">:</span> <span class="devsite-syntax-n">x_train</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-s1">'label'</span><span class="devsite-syntax-p">:</span> <span class="devsite-syntax-n">y_train</span><span class="devsite-syntax-p">},</span> <span class="devsite-syntax-n">batch_size</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">32</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">epochs</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">5</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-n">adv_model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">evaluate</span><span class="devsite-syntax-p">({</span><span class="devsite-syntax-s1">'feature'</span><span class="devsite-syntax-p">:</span> <span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-s1">'label'</span><span class="devsite-syntax-p">:</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">})</span></pre></devsite-code> </div> </div> </div> </div> </div> </section> <section class="devsite-landing-row devsite-landing-row-3-up devsite-landing-row-cards" header-position="top" > <div class="devsite-landing-row-inner"> <div class="devsite-landing-row-group"> <div class="devsite-landing-row-item" description-position="bottom" > <div class="devsite-landing-row-item-media "> <figure class="devsite-landing-row-item-image"> <a href="https://blog.tensorflow.org/2019/09/introducing-neural-structured-learning.html"> <picture> <img alt="" src="https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9.png" srcset="https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_36.png 36w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_48.png 48w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_72.png 72w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_96.png 96w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_480.png 480w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_720.png 720w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_856.png 856w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_960.png 960w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_1440.png 1440w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_1920.png 1920w,https://www.tensorflow.org/static/resources/images/tf-logo-card-16x9_2880.png 2880w" sizes="(max-width: 840px) 50vw, 464px" loading="lazy" > </picture> </a> </figure> </div> <div class="devsite-landing-row-item-description" > <div class="devsite-landing-row-item-body"> <h3 id="introducing-neural-structured-learning-in-tensorflow" data-text="Introducing Neural Structured Learning in TensorFlow" class="hide-from-toc no-link" tabindex="0"> <a href="https://blog.tensorflow.org/2019/09/introducing-neural-structured-learning.html"> Introducing Neural Structured Learning in TensorFlow </a> </h3> <div class="devsite-landing-row-item-buttons"> <a href="https://blog.tensorflow.org/2019/09/introducing-neural-structured-learning.html" class="button button-white " >Read on TensorFlow blog</a> </div> </div> </div> </div> <div class="devsite-landing-row-item" description-position="bottom" > <div class="devsite-landing-row-item-media "> <figure class="devsite-landing-row-item-image"> <a href="https://github.com/tensorflow/neural-structured-learning"> <picture> <img alt="" src="https://www.tensorflow.org/static/resources/images/github-card-16x9.png" srcset="https://www.tensorflow.org/static/resources/images/github-card-16x9_36.png 36w,https://www.tensorflow.org/static/resources/images/github-card-16x9_48.png 48w,https://www.tensorflow.org/static/resources/images/github-card-16x9_72.png 72w,https://www.tensorflow.org/static/resources/images/github-card-16x9_96.png 96w,https://www.tensorflow.org/static/resources/images/github-card-16x9_480.png 480w,https://www.tensorflow.org/static/resources/images/github-card-16x9_720.png 720w,https://www.tensorflow.org/static/resources/images/github-card-16x9_856.png 856w,https://www.tensorflow.org/static/resources/images/github-card-16x9_960.png 960w,https://www.tensorflow.org/static/resources/images/github-card-16x9_1440.png 1440w,https://www.tensorflow.org/static/resources/images/github-card-16x9_1920.png 1920w,https://www.tensorflow.org/static/resources/images/github-card-16x9_2880.png 2880w" sizes="(max-width: 840px) 50vw, 464px" loading="lazy" > </picture> </a> </figure> </div> <div class="devsite-landing-row-item-description" > <div class="devsite-landing-row-item-body"> <h3 id="tf-nsl-on-github" data-text="TF-NSL on GitHub" class="hide-from-toc no-link" tabindex="0"> <a href="https://github.com/tensorflow/neural-structured-learning"> TF-NSL on GitHub </a> </h3> <div class="devsite-landing-row-item-buttons"> <a href="https://github.com/tensorflow/neural-structured-learning" class="button button-white " >View on GitHub</a> </div> </div> </div> </div> <div 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