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<div class="section" id="hybrid-faster-training-and-easy-deployment">
<span id="hybrid-faster-training-and-easy-deployment"></span><h1>Hybrid - Faster training and easy deployment<a class="headerlink" href="#hybrid-faster-training-and-easy-deployment" title="Permalink to this headline"></a></h1>
<p>Deep learning frameworks can be roughly divided into two categories: declarative
and imperative. With declarative frameworks (including Tensorflow, Theano, etc)
users first declare a fixed computation graph and then execute it end-to-end.
The benefit of fixed computation graph is it’s portable and runs more
efficiently. However, it’s less flexible because any logic must be encoded
into the graph as special operators like <code class="docutils literal"><span class="pre">scan</span></code>, <code class="docutils literal"><span class="pre">while_loop</span></code> and <code class="docutils literal"><span class="pre">cond</span></code>.
It’s also hard to debug.</p>
<p>Imperative frameworks (including PyTorch, Chainer, etc) are just the opposite:
they execute commands one-by-one just like old fashioned Matlab and Numpy.
This style is more flexible, easier to debug, but less efficient.</p>
<p><code class="docutils literal"><span class="pre">HybridBlock</span></code> seamlessly combines declarative programming and imperative programming
to offer the benefit of both. Users can quickly develop and debug models with
imperative programming and switch to efficient declarative execution by simply
calling: <code class="docutils literal"><span class="pre">HybridBlock.hybridize()</span></code>.</p>
<div class="section" id="hybridblock">
<span id="hybridblock"></span><h2>HybridBlock<a class="headerlink" href="#hybridblock" title="Permalink to this headline"></a></h2>
<p><code class="docutils literal"><span class="pre">HybridBlock</span></code> is very similar to <code class="docutils literal"><span class="pre">Block</span></code> but has a few restrictions:</p>
<ul class="simple">
<li>All children layers of <code class="docutils literal"><span class="pre">HybridBlock</span></code> must also be <code class="docutils literal"><span class="pre">HybridBlock</span></code>.</li>
<li>Only methods that are implemented for both <code class="docutils literal"><span class="pre">NDArray</span></code> and <code class="docutils literal"><span class="pre">Symbol</span></code> can be used.
For example you cannot use <code class="docutils literal"><span class="pre">.asnumpy()</span></code>, <code class="docutils literal"><span class="pre">.shape</span></code>, etc.</li>
<li>Operations cannot change from run to run. For example, you cannot do <code class="docutils literal"><span class="pre">if</span> <span class="pre">x:</span></code>
if <code class="docutils literal"><span class="pre">x</span></code> is different for each iteration.</li>
</ul>
<p>To use hybrid support, we subclass the <code class="docutils literal"><span class="pre">HybridBlock</span></code>:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
<span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">gluon</span><span class="o">.</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">:</span>
<span class="c1"># layers created in name_scope will inherit name space</span>
<span class="c1"># from parent layer.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Pool2D</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Pool2D</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc1</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">120</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc2</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">84</span><span class="p">)</span>
<span class="c1"># You can use a Dense layer for fc3 but we do dot product manually</span>
<span class="c1"># here for illustration purposes.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc3_weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fc3_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">84</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">fc3_weight</span><span class="p">):</span>
<span class="c1"># Here `F` can be either mx.nd or mx.sym, x is the input data,</span>
<span class="c1"># and fc3_weight is either self.fc3_weight.data() or</span>
<span class="c1"># self.fc3_weight.var() depending on whether x is Symbol or NDArray</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="c1"># 0 means copy over size from corresponding dimension.</span>
<span class="c1"># -1 means infer size from the rest of dimensions.</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fc3_weight</span><span class="p">,</span> <span class="n">transpose_b</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
</div>
<div class="section" id="hybridize">
<span id="hybridize"></span><h2>Hybridize<a class="headerlink" href="#hybridize" title="Permalink to this headline"></a></h2>
<p>By default, <code class="docutils literal"><span class="pre">HybridBlock</span></code> runs just like a standard <code class="docutils literal"><span class="pre">Block</span></code>. Each time a layer
is called, its <code class="docutils literal"><span class="pre">hybrid_forward</span></code> will be run:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Hybrid execution can be activated by simply calling <code class="docutils literal"><span class="pre">.hybridize()</span></code> on the top
level layer. The first forward call after activation will try to build a
computation graph from <code class="docutils literal"><span class="pre">hybrid_forward</span></code> and cache it. On subsequent forward
calls the cached graph instead of <code class="docutils literal"><span class="pre">hybrid_forward</span></code> will be invoked:</p>
<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>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that before hybridize, <code class="docutils literal"><span class="pre">print(x)</span></code> printed out one NDArray for forward,
but after hybridize, only the first forward printed out a Symbol. On subsequent
forward <code class="docutils literal"><span class="pre">hybrid_forward</span></code> is not called so nothing was printed.</p>
<p>Hybridize will speed up execution and save memory. If the top level layer is
not a <code class="docutils literal"><span class="pre">HybridBlock</span></code>, you can still call <code class="docutils literal"><span class="pre">.hybridize()</span></code> on it and Gluon will try
to hybridize its children layers instead.</p>
</div>
<div class="section" id="serializing-trained-model-for-deployment">
<span id="serializing-trained-model-for-deployment"></span><h2>Serializing trained model for deployment<a class="headerlink" href="#serializing-trained-model-for-deployment" title="Permalink to this headline"></a></h2>
<p>Models implemented as <code class="docutils literal"><span class="pre">HybridBlock</span></code> can be easily serialized for deployment
using other language front-ends like C, C++ and Scala. To this end, we simply
forward the model with symbolic variables instead of NDArrays and save the
output Symbol(s):</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">y</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'model.json'</span><span class="p">)</span>
<span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'model.params'</span><span class="p">)</span>
</pre></div>
</div>
<p>If your network outputs more than one value, you can use <code class="docutils literal"><span class="pre">mx.sym.Group</span></code> to
combine them into a grouped Symbol and then save. The saved json and params
files can then be loaded with C, C++ and Scala interface for prediction.</p>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Hybrid - Faster training and easy deployment</a><ul>
<li><a class="reference internal" href="#hybridblock">HybridBlock</a></li>
<li><a class="reference internal" href="#hybridize">Hybridize</a></li>
<li><a class="reference internal" href="#serializing-trained-model-for-deployment">Serializing trained model for deployment</a></li>
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</ul>
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