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class='section' id='blog'><div class='widget FeaturedPost' data-version='1' id='FeaturedPost1'> </div><div class='widget Blog' data-version='1' id='Blog1'> <div class='tensorsite-container--narrow'> <div class='tensorsite-detail'> <a aria-hidden='true' class='tensorsite-detail__next-url' hidden='true' href='https://blog.tensorflow.org/2019/09/disaster-watch-crisis-mapping-platform.html'></a> <div aria-hidden='true' class='tensorsite-detail__current-url' hidden='true'>https://blog.tensorflow.org/2019/09/introducing-neural-structured-learning.html</div> <div aria-hidden='true' class='tensorsite-detail__tags' hidden='true'> <span>TensorFlow Core</span> </div> <div aria-hidden='true' class='tensorsite-detail__main-image' hidden='true'> https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHKQiYlvQVzuD7fIYQI1pGRePEA-KR8m6ZktQEAl6bJdGnBqYWo5LBgyPt5Dm_y1gnxYfXghGFa0inFaWx2EJ83PEQ6ew9lSsouUCu3NRewSshpymK74ed3xSpyYdUka0STtctdhglVfPa/s1600/1.png </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> September 03, 2019 — </span> <em class="gx" style="box-sizing: inherit;">Posted by Da-Cheng Juan (Senior Software Engineer) and </em><em class="gx" style="box-sizing: inherit;"><a class="bg cn gy gz ha hb" href="https://twitter.com/ravisujith" rel="noopener" target="_blank">Sujith Ravi</em></a><em class="gx" style="box-sizing: inherit;"> (Senior Staff Research Scientist)</em><br><br>We are excited to introduce <a class="bg cn gy gz ha hb" href="https://www.tensorflow.org/neural_structured_learning" rel="noopener" target="_blank">Neural Structured Learning in TensorFlow</a>, an easy-to-use framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, l… </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20'> <span>TensorFlow Core</span> </a> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> Introducing Neural Structured Learning in TensorFlow </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>September 03, 2019</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%22Introducing Neural Structured Learning in TensorFlow%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2019/09/introducing-neural-structured-learning.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="0b0f" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;"><em class="gx" style="box-sizing: inherit;">Posted by Da-Cheng Juan (Senior Software Engineer) and </em><a class="bg cn gy gz ha hb" href="https://twitter.com/ravisujith" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank"><em class="gx" style="box-sizing: inherit;">Sujith Ravi</em></a><em class="gx" style="box-sizing: inherit;"> (Senior Staff Research Scientist)</em></span><br /> <span style="font-family: inherit; letter-spacing: -0.004em;"><br /></span> <span style="font-family: inherit; letter-spacing: -0.004em;">We are excited to introduce </span><a class="bg cn gy gz ha hb" href="https://www.tensorflow.org/neural_structured_learning" rel="noopener" style="font-family: inherit; letter-spacing: -0.004em;" target="_blank">Neural Structured Learning in TensorFlow</a><span style="font-family: inherit; letter-spacing: -0.004em;">, an easy-to-use framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.</span></div> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="de62" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;"></span><br /> <a name='more'></a></div> <div class="separator" style="clear: both; text-align: center;"> <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHKQiYlvQVzuD7fIYQI1pGRePEA-KR8m6ZktQEAl6bJdGnBqYWo5LBgyPt5Dm_y1gnxYfXghGFa0inFaWx2EJ83PEQ6ew9lSsouUCu3NRewSshpymK74ed3xSpyYdUka0STtctdhglVfPa/s1600/1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img alt="Diagram of structured signals in addition to regular images being used as training data for a neural network" border="0" data-original-height="788" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHKQiYlvQVzuD7fIYQI1pGRePEA-KR8m6ZktQEAl6bJdGnBqYWo5LBgyPt5Dm_y1gnxYfXghGFa0inFaWx2EJ83PEQ6ew9lSsouUCu3NRewSshpymK74ed3xSpyYdUka0STtctdhglVfPa/s1600/1.png" title="" /></a></div> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="34cc" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">Many machine learning tasks benefit from using structured data which contains rich relational information among the samples. For example, modeling citation networks, <a class="bg cn gy gz ha hb" href="https://en.wikipedia.org/wiki/Knowledge_Graph" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">Knowledge Graph</a> inference and reasoning on linguistic structure of sentences, and learning molecular fingerprints all require a model to learn from structured inputs, as opposed to just individual samples. These structures can be explicitly given (e.g., as a graph), or implicitly inferred (e.g., as an adversarial example). Leveraging structured signals during training allows developers to achieve <a class="bg cn gy gz ha hb" href="https://ai.google/research/pubs/pub46568.pdf" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">higher model accuracy</a>, particularly when the amount of labeled data is relatively small. Training with structured signals also leads to <a class="bg cn gy gz ha hb" href="https://arxiv.org/pdf/1412.6572.pdf" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">more robust models</a>. These techniques have been widely used in Google for improving model performance, such as <a class="bg cn gy gz ha hb" href="https://arxiv.org/abs/1902.10814" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">learning image semantic embedding</a>.</span></div> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="b5b2" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">Neural Structured Learning (NSL) is an open source framework for training deep neural networks with structured signals. It implements <a class="bg cn gy gz ha hb" href="https://ai.google/research/pubs/pub46568.pdf" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">Neural Graph Learning</a>, which enables developers to train neural networks using graphs. The graphs can come from multiple sources such as Knowledge graphs, medical records, genomic data or multimodal relations (e.g., image-text pairs). NSL also generalizes to <a class="bg cn gy gz ha hb" href="https://arxiv.org/pdf/1412.6572.pdf" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">Adversarial Learning</a> where the structure between input examples is dynamically constructed using adversarial perturbation.</span></div> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="56df" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">NSL allows TensorFlow users to easily incorporate various structured signals for training neural networks, and works for different learning scenarios: supervised, semi-supervised and unsupervised (representation) settings.</span></div> <h2 style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-weight: 600; letter-spacing: -0.022em;"><span style="font-family: inherit;">How Neural Structured Learning (NSL) Works</span></span></h2> <div class="separator" style="clear: both; text-align: center;"> <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhO6dXwz1x6KzinvrRs8bQ_aZ_yo58j50CVVenzaDgHZ6lcC0iHHAUahyphenhyphenfueITSX-MWgmOCRhTuc7Waz4pjLi8YUVXfmPCM-NrYMA7Pduc6o3DwSMnR6xi7mp96RO2i-W186F1PL-g7U7sm/s1600/0_sbDVG_o-N4BLzxUL.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img alt="Diagram of structured signals and examples" border="0" data-original-height="590" data-original-width="1600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhO6dXwz1x6KzinvrRs8bQ_aZ_yo58j50CVVenzaDgHZ6lcC0iHHAUahyphenhyphenfueITSX-MWgmOCRhTuc7Waz4pjLi8YUVXfmPCM-NrYMA7Pduc6o3DwSMnR6xi7mp96RO2i-W186F1PL-g7U7sm/s1600/0_sbDVG_o-N4BLzxUL.png" title="" /></a></div> <span style="color: rgba(0 , 0 , 0 , 0.83921568627451);"><span style="letter-spacing: -0.084px;">In Neural Structured Learning (NSL), the structured signals─whether explicitly defined as a graph or implicitly learned as adversarial examples─are used to regularize the training of a neural network, forcing the model to learn accurate predictions (by minimizing supervised loss), while at the same time maintaining the similarity among inputs from the same structure (by minimizing the neighbor loss, see the figure above). </span></span><span style="font-family: inherit;"><span style="background-color: white; color: rgba(0 , 0 , 0 , 0.84); letter-spacing: -0.084px;">This technique is generic and can be applied on arbitrary neural architectures, such as Feed-forward NNs, Convolutional NNs and Recurrent NNs. </span></span><br /> <h2 style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.022em; line-height: 1.12; margin: 1.95em 0px -0.28em;"> <span style="font-family: inherit;">Create a Model with Neural Structured Learning (NSL)</span></h2> <div class="separator" style="clear: both; text-align: center;"> </div> <div> <div class="gj gk bx as gl b gm in go io gq ip gs iq gu ir gw" data-selectable-paragraph="" id="5d60" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 0.86em;"> <span style="font-family: inherit;">With NSL, building a model to leverage structured signals becomes easy and straightforward. Given a graph (as explicit structure) and training samples, NSL provides a tool to process and combine these examples into <a class="bg cn gy gz ha hb" href="https://www.tensorflow.org/tutorials/load_data/tf_records" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">TFRecords</a> for downstream training:</span></div> <pre style="background-color: white;"><code class="”language-javascript”"><span style="letter-spacing: -0.064px;">python pack_nbrs.py --max_nbrs=5 \ labeled_data.tfr \ unlabeled_data.tfr \ graph.tsv \ merged_examples.tfr </span></code></pre> <div class="gj gk bx as gl b gm in go io gq ip gs iq gu ir gw" data-selectable-paragraph="" id="5d60" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 0.86em;"> <span style="letter-spacing: -0.084px;"><span style="font-family: inherit;">Next, NSL provides APIs to “wrap around” the custom model to consume the processed examples and enable graph regularization. Let’s directly take a look at the code example.</span></span><br /> <pre><code class="”language-python”"> import neural_structured_learning as nsl # Create a custom model — sequential, functional, or subclass. base_model = tf.keras.Sequential(…) # Wrap the custom model with graph regularization. graph_config = nsl.configs.GraphRegConfig(neighbor_config=nsl.configs.GraphNeighborConfig(max_neighbors=1)) graph_model = nsl.keras.GraphRegularization(base_model, graph_config) # Compile, train, and evaluate. graph_model.compile(optimizer=’adam’, loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=[‘accuracy’]) graph_model.fit(train_dataset, epochs=5) graph_model.evaluate(test_dataset) </code> </pre> <span style="font-family: inherit; letter-spacing: -0.084px;">With less than 5 additional lines (yes, including the comment!), we obtain a neural model that leverages graph signals during training. Empirically, using a graph structure allows models to be able to train with less labeled data without losing much accuracy (for example, 10% or even 1% of the original supervision).</span></div> <h2 style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.022em; line-height: 1.12; margin: 1.95em 0px -0.28em;"> <span style="font-family: inherit;">What if No Explicit Structure is Given?</span></h2> <div class="gj gk bx as gl b gm in go io gq ip gs iq gu ir gw" data-selectable-paragraph="" id="3fe8" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 0.86em;"> <span style="font-family: inherit;">What if the explicit structure (such as graphs) is not available or not given as inputs? NSL provides tools for developers to construct graphs from raw data; alternatively, NSL also provides APIs to “induce” adversarial examples as implicit structured signals. Adversarial examples are constructed to intentionally confuse the model一training with such examples usually results in models that are robust against small input perturbations. Let’s take a look at the code example below to see how NSL enables training with adversarial examples.</span></div> <br /> <pre><code class="”language-python”"> import neural_structured_learning as nsl # Create a base model — sequential, functional, or subclass. model = tf.keras.Sequential(…) # Wrap the model with adversarial regularization. adv_config = nsl.configs.make_adv_reg_config(multiplier=0.2, adv_step_size=0.05) adv_model = nsl.keras.AdversarialRegularization(model, adv_config=adv_config) # Compile, train, and evaluate. adv_model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’]) adv_model.fit({‘feature’: x_train, ‘label’: y_train}, epochs=5) adv_model.evaluate({‘feature’: x_test, ‘label’: y_test}) </code> </pre> <br /> <span style="background-color: white; color: rgba(0 , 0 , 0 , 0.84); font-family: inherit; letter-spacing: -0.004em;">With less than 5 additional lines (again, including the comment), we obtain a neural model that trains with adversarial examples providing an implicit structure. Empirically, models trained without adversarial examples suffer from significant accuracy loss (e.g., 30% lower) when malicious yet not human-detectable perturbations are added to inputs.</span><br /> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="4d0c" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">Ready to get started?</span></div> <div class="gj gk bx as gl b gm gn go gp gq gr gs gt gu gv gw" data-selectable-paragraph="" id="ddcf" style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">Please visit <a class="bg cn gy gz ha hb" href="https://www.tensorflow.org/neural_structured_learning/" rel="noopener" style="-webkit-tap-highlight-color: transparent; background-image: url("data:image/svg+xml; background-position: 0px calc(1em + 1px); background-repeat: repeat-x; background-size: 1px 1px; box-sizing: inherit; http: //www.w3.org/2000/svg\"><line x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\" stroke=\"rgba(0, 0, 0, 0.84)\" /></svg>"); text-decoration-line: none;" target="_blank">https://www.tensorflow.org/neural_structured_learning/</a>, and try out NSL today!</span></div> <h2 style="background-color: white; box-sizing: inherit; color: rgba(0, 0, 0, 0.84); letter-spacing: -0.004em; line-height: 1.58; margin-bottom: -0.46em; margin-top: 2em;"> <span style="font-family: inherit;">Acknowledgements</span></h2> <div> <span style="background-color: white; color: rgba(0 , 0 , 0 , 0.84); letter-spacing: -0.064px;"><br /> </span></div> <div> <span style="color: rgba(0 , 0 , 0 , 0.83921568627451);"><span style="letter-spacing: -0.064px;"><i>We would like to acknowledge core contributions from Chun-Sung Ferng, Arjun Gopalan, Allan Heydon, Yicheng Fan, Chun-Ta Lu, Philip Pham and Andrew Tomkins. We also want to thank Daniel ‘Wolff’ Dobson and Karmel Allison for their technical suggestions, Mark Daoust, Billy Lamberta and Yash Katariya for their help in creating the tutorials, and Google Expander team for their feedback.</i></span></span></div> <div class="hm ep jd s ak je jf e" data-test-id="post-sidebar" style="box-sizing: inherit; opacity: 0; pointer-events: none; position: fixed; top: calc(159px); transition: opacity 200ms ease 0s; width: 1439px; will-change: opacity;"> <div class="n p" style="box-sizing: inherit; display: flex; justify-content: center;"> <div class="ac ae af ag ah ai aj ak" style="box-sizing: inherit; margin: 0px 24px; max-width: 1032px; min-width: 0px; width: 1032px;"> <div class="jg n eb" style="box-sizing: inherit; display: flex; flex-direction: column; width: 131px;"> <div class="ep" style="box-sizing: inherit; pointer-events: none;"> <div class="jh ji r" style="border-bottom: 1px solid rgba(0, 0, 0, 0.1); box-sizing: inherit; padding-bottom: 28px;"> <a class="bg bh bi bj bk bl bm bn bo bp bq br bs bt bu bv" href="https://medium.com/tensorflow?source=post_sidebar--------------------------post_sidebar-" rel="noopener" style="-webkit-tap-highlight-color: transparent; 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Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, l… </span> </p> </div> </div> </div> </div>  </div></div> </div>  <div class='tensorsite-full-footer'> <div class='section' id='footer'><div class='widget HTML' data-version='1' id='HTML2'> <section class='tensorsite-footer'> <div class='tensorsite-container tensorsite-footer__container'> <div class='tensorsite-footer__side tensorsite-footer__side--left'></div> <div class='tensorsite-footer__side tensorsite-footer__side--right'></div> <div class='tensorsite-footer__content'> <div class='tensorsite-content'> <div class='tensorsite-content__title tensorsite-content__title--grow'> Build, deploy, and experiment easily with TensorFlow </div> <div class='tensorsite-content__cta-wrapper'> <a class='tensorsite-content__button tensorsite-button tensorsite-button--white' href='https://www.tensorflow.org/'> Get started </a> </div> </div> </div> </div> <div class='tensorsite-footer__lines'></div> 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