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<!--- under the License. --><div class="section" id="about-gluon">
<span id="about-gluon"></span><h1>About Gluon<a class="headerlink" href="#about-gluon" title="Permalink to this headline"></a></h1>
<p><img alt="gluon logo" src="https://github.com/dmlc/web-data/blob/master/mxnet/image/image-gluon-logo.png?raw=true"/></p>
<p>Based on the <a class="reference external" href="https://github.com/gluon-api/gluon-api">the Gluon API specification</a>, the new Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed. <a class="reference external" href="/versions/1.4.1/install">Install the latest version of MXNet</a> to get access to Gluon.</p>
<p>To get started with Gluon, checkout the following resources and tutorials:</p>
<ul class="simple">
<li><a class="reference external" href="https://gluon-crash-course.mxnet.io/">60-minute Gluon Crash Course</a> - six 10-minute lessons on using Gluon</li>
<li><a class="reference external" href="https://gluon-cv.mxnet.io/">GluonCV Toolkit</a> - implementations of state of the art deep learning algorithms in <strong>Computer Vision (CV)</strong></li>
<li><a class="reference external" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a> - implementations of state of the art deep learning algorithms in <strong>Natural Language Processing (NLP)</strong></li>
<li><a class="reference external" href="https://gluon.mxnet.io/">Gluon: The Straight Dope</a> - notebooks designed to teach deep learning from the ground up, all using the Gluon API</li>
</ul>
<p><br/></p>
<div class="boxed">
Advantages
</div><ol class="simple">
<li>Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.</li>
<li>Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.</li>
<li>Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.</li>
<li>High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.</li>
</ol>
<p><br/></p>
<div class="boxed">
The Straight Dope
</div><p>The community is also working on parallel effort to create a foundational resource for learning about machine learning. The Straight Dope is a book composed of introductory as well as advanced tutorials – all based on the Gluon interface. For example,</p>
<ul class="simple">
<li><a class="reference external" href="http://gluon.mxnet.io/chapter01_crashcourse/introduction.html">Learn about machine learning basics</a>.</li>
<li><a class="reference external" href="http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html">Develop and train a simple neural network model</a>.</li>
<li><a class="reference external" href="http://gluon.mxnet.io/chapter05_recurrent-neural-networks/simple-rnn.html">Implement a Recurrent Neural Network (RNN) model for Language Modeling</a>.</li>
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<p><br/></p>
<div class="boxed">
Code Examples
</div><p><strong><strong>Simple, Easy-to-Understand Code</strong></strong></p>
<p>Use plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="c1"># When instantiated, Sequential stores a chain of neural network layers.</span>
<span class="c1"># Once presented with data, Sequential executes each layer in turn, using</span>
<span class="c1"># the output of one layer as the input for the next</span>
<span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span> <span class="c1"># 1st layer (256 nodes)</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span> <span class="c1"># 2nd hidden layer</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">num_outputs</span><span class="p">))</span>
</pre></div>
</div>
<p><br/></p>
<p><strong><strong>Flexible, Imperative Structure</strong></strong></p>
<p>Prototype, build, and train neural networks in fully imperative manner using the MXNet autograd package and the Gluon trainer method:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_data</span><span class="p">):</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="c1"># the forward iteration</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
<p><br/>
<strong><strong>Dynamic Graphs</strong></strong></p>
<p>Build neural networks on the fly for use cases where neural networks must change in size and shape during model training:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>def forward(self, F, inputs, tree):
children_outputs = [self.forward(F, inputs, child)
for child in tree.children]
#Recursively builds the neural network based on each input sentence’s
#syntactic structure during the model definition and training process
…
</pre></div>
</div>
<p><br/>
<strong><strong>High Performance</strong></strong></p>
<p>Easily cache the neural network to achieve high performance by defining your neural network with <code class="docutils literal"><span class="pre">HybridSequential</span></code> and calling the <code class="docutils literal"><span class="pre">hybridize</span></code> method:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span>
<span class="k">with</span> <span class="n">net</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">))</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span>
</pre></div>
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<span id="learn-more"></span><h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this headline"></a></h2>
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