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<p><img alt="" 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. Install the latest version of MXNet to get access to Gluon by either following these easy steps or using this simple command:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span> <span class="n">pip</span> <span class="n">install</span> <span class="n">mxnet</span> <span class="o">--</span><span class="n">pre</span> <span class="o">--</span><span class="n">user</span>
</pre></div>
</div>
<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.
<br/></li>
</ol>
<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>
</ul>
<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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