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    <li>Optimizer Design</li>
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  <div class="section" id="optimizer-design">
<span id="optimizer-design"></span><h1>Optimizer Design<a class="headerlink" href="#optimizer-design" title="永久链接至标题"></a></h1>
<div class="section" id="the-problem">
<span id="the-problem"></span><h2>The Problem<a class="headerlink" href="#the-problem" title="永久链接至标题"></a></h2>
<p>A PaddlePaddle program, or a block, is a sequence of operators operating variables.  A training program needs to do three kinds of works:</p>
<ol class="simple">
<li>the forward pass, which computes intermediate results and the cost(s),</li>
<li>the backward pass, which derives gradients from intermediate results and costs, and</li>
<li>the optimization pass, which update model parameters to optimize the cost(s).</li>
</ol>
<p>These works rely on three kinds of operators:</p>
<ol class="simple">
<li>forward operators,</li>
<li>gradient operators, and</li>
<li>optimization operators.</li>
</ol>
<p>It&#8217;s true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.</p>
<p>In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.</p>
</div>
<div class="section" id="high-level-python-api-to-describe-the-training-process">
<span id="high-level-python-api-to-describe-the-training-process"></span><h2>High-level Python API to describe the training process<a class="headerlink" href="#high-level-python-api-to-describe-the-training-process" title="永久链接至标题"></a></h2>
<ol>
<li><p class="first">User write code to describe the network:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">images</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;images&quot;</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;labels&quot;</span><span class="p">)</span>
<span class="n">w1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;w1&quot;</span><span class="p">)</span>
<span class="n">b1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;b1&quot;</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">w1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b1</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
</pre></div>
</div>
<p>The above code snippet will create forward operators in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md">Block</a>.</p>
</li>
</ol>
<ol>
<li><p class="first">Users create a certain kind of Optimizer with some argument.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">AdagradOptimizer</span><span class="p">(</span><span class="n">learing_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p class="first">Users use the optimizer to <code class="docutils literal"><span class="pre">minimize</span></code> a certain <code class="docutils literal"><span class="pre">cost</span></code> through updating parameters in parameter_list.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">opt_op_list</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">parameter_list</span><span class="o">=</span><span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">b1</span><span class="p">])</span>
</pre></div>
</div>
<p>The above code snippet will create gradient and optimization operators in Block. The return value of <code class="docutils literal"><span class="pre">minimize()</span></code> is list of optimization operators that will be run by session.</p>
</li>
<li><p class="first">Users use Session/Executor to run this opt_op_list as target to do training.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span> <span class="n">opt_op_list</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ol>
<div class="section" id="optimizer-python-interface">
<span id="optimizer-python-interface"></span><h3>Optimizer Python interface:<a class="headerlink" href="#optimizer-python-interface" title="永久链接至标题"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Optimizer Base class.</span>

<span class="sd">    &quot;&quot;&quot;</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="k">pass</span>

    <span class="k">def</span> <span class="nf">create_optimization_pass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameters_and_grads</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Add optimization operators to update gradients to variables.</span>

<span class="sd">        Args:</span>
<span class="sd">          parameters_and_grads: a list of (variable, gradient) pair to update.</span>

<span class="sd">        Returns:</span>
<span class="sd">          optmization_op_list: a list of optimization operator that will update parameter using gradient.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">None</span>

    <span class="k">def</span> <span class="nf">minimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Add operations to minimize `loss` by updating `parameter_list`.</span>
T
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307 
<span class="sd">        This method combines interface `append_backward()` and</span>
T
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308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 
<span class="sd">        `create_optimization_pass()` into one.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">params_grads</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_backward_pass</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">)</span>
        <span class="n">update_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_optimization_pass</span><span class="p">(</span><span class="n">params_grads</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">update_ops</span>

</pre></div>
</div>
<p>Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.</p>
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