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<li><a href="../index.html">Docs</a> »</li> + + <li><a href="index.html">Documentation</a> »</li> + + <li>Optimizer</li> + + + <li class="wy-breadcrumbs-aside"> + + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <div class="section" id="module-singa.optimizer"> +<span id="optimizer"></span><h1>Optimizer<a class="headerlink" href="#module-singa.optimizer" title="Permalink to this headline">¶</a></h1> +<p>This module includes a set of optimizers for updating model parameters.</p> +<p>Example usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">optimizer</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span> + +<span class="n">sgd</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span> +<span class="n">p</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> +<span class="n">p</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">g</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> +<span class="n">g</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span> + +<span class="n">sgd</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use the global lr=0.1 for epoch 1</span> +<span class="n">sgd</span><span class="o">.</span><span class="n">apply_with_lr</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use lr=0.03 for epoch 2</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.optimizer.Optimizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>The base python optimizer class.</p> +<p>Typically, an optimizer is used as follows:</p> +<ol class="arabic simple"> +<li><p>construct the optimizer</p></li> +<li><p>(optional) register each parameter with its specs.</p></li> +<li><p>use the optimizer to update parameter values given parameter gradients +and other optional info</p></li> +</ol> +<p>The subclasses should override the apply_with_lr function to do the real +parameter udpate.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>lr</strong> (<em>float</em>) â a constant value for the learning rate</p></li> +<li><p><strong>momentum</strong> (<em>float</em>) â a constant value for the momentum value</p></li> +<li><p><strong>weight_decay</strong> (<em>float</em>) â the coefficent for L2 regularizer, which is +mutually exclusive with âregularizerâ.</p></li> +<li><p><strong>regularizer</strong> â an instance of Regularizer or RegularizerConf; If set, +regularization would be applied in apply_with_lr(). +Users can also do regularization outside.</p></li> +<li><p><strong>constraint</strong> â an instance of Constraint or ConstraintConf; If set, +constraint would be applied inside apply_with_lr(). Users can +also apply constraint outside.</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.Optimizer.register"> +<code class="descname">register</code><span class="sig-paren">(</span><em>name</em>, <em>specs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.register" title="Permalink to this definition">¶</a></dt> +<dd><p>Register the param specs, including creating regularizer and +constraint per param object. Param specific regularizer and constraint +have higher priority than the global ones. If all parameters share the +same setting for learning rate, regularizer and constraint, then there +is no need to call this function.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>name</strong> (<em>str</em>) â parameter name</p></li> +<li><p><strong>specs</strong> (<em>ParamSpec</em>) â protobuf obj, including regularizer and +constraint, multipliers for learning rate and weight decay.</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply_regularizer_constraint"> +<code class="descname">apply_regularizer_constraint</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_regularizer_constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Apply regularization and constraint if available.</p> +<p>If there are both global regularizer (constraint) and param specific +regularizer (constraint), it would use the param specific one.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>value</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter value Tensor</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient Tensor</p></li> +<li><p><strong>name</strong> (<em>string</em>) â to get parameter specific regularizer or constraint</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>the updated gradient Tensor</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update assuming the learning rate generator is set.</p> +<p>The subclass optimizer does not need to override this function.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to retrieval parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â training iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.SGD"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>The vallina Stochasitc Gradient Descent algorithm with momentum.</p> +<p>See the base Optimizer for all arguments.</p> +<dl class="method"> +<dt id="singa.optimizer.SGD.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Nesterov"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Nesterov</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=0.9</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>The SGD with Nesterov momentum.</p> +<p>See the base Optimizer for all arguments.</p> +<dl class="method"> +<dt id="singa.optimizer.Nesterov.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.RMSProp"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">RMSProp</code><span class="sig-paren">(</span><em>rho=0.9</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>RMSProp optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>rho</strong> (<em>float</em>) â float within [0, 1]</p></li> +<li><p><strong>epsilon</strong> (<em>float</em>) â small value for preventing numeric error</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.RMSProp.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.AdaGrad"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">AdaGrad</code><span class="sig-paren">(</span><em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>lr_gen=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>AdaGrad optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>epsilon</strong> (<em>float</em>) â small number for preventing numeric error.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.AdaGrad.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Adam"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>beta_1=0.9</em>, <em>beta_2=0.999</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>Adam optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>beta_1</strong> (<em>float</em>) â coefficient of momentum</p></li> +<li><p><strong>beta_2</strong> (<em>float</em>) â coefficient of aggregated squared gradient</p></li> +<li><p><strong>epsilon</strong> (<em>float</em>) â small value for preventing numeric error</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.Adam.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Update one parameter object.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>step</strong> (<em>int</em>) â the accumulated training iterations, not the iteration ID</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Regularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Regularizer</code><a class="headerlink" href="#singa.optimizer.Regularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base Python regularizer for parameter gradients.</p> +<dl class="method"> +<dt id="singa.optimizer.Regularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Regularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.CppRegularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppRegularizer</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>Wrapper for regularizer implemented using C++.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>conf</strong> (<em>RegularizerConf</em>) â protobuf message for the configuration.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.CppRegularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.L2Regularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Regularizer</code><span class="sig-paren">(</span><em>coefficient</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>L2 regularization</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>coefficient</strong> (<em>float</em>) â regularization coefficient.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.L2Regularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Constraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Constraint</code><a class="headerlink" href="#singa.optimizer.Constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base Python constraint class for paramter gradients</p> +<dl class="method"> +<dt id="singa.optimizer.Constraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Constraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.CppConstraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppConstraint</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Constraint</span></code></a></p> +<p>Wrapper for constraints implemented using C++.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>conf</strong> (<em>ConstraintConf</em>) â protobuf message for the configuration.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.CppConstraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.L2Constraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Constraint</code><span class="sig-paren">(</span><em>threshold=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Constraint</span></code></a></p> +<p>Rescale the gradient to make the L2 norm <= a given threshold</p> +<dl class="method"> +<dt id="singa.optimizer.L2Constraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="autograd.html" class="btn btn-neutral float-right" title="Autograd in Singa" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="metric.html" class="btn btn-neutral float-left" title="Metric" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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<li><a href="index.html">Documentation</a> »</li> + + <li>Snapshot</li> + + + <li class="wy-breadcrumbs-aside"> + + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <div class="section" id="module-singa.snapshot"> +<span id="snapshot"></span><h1>Snapshot<a class="headerlink" href="#module-singa.snapshot" title="Permalink to this headline">¶</a></h1> +<p>This script includes io::snapshot class and its methods.</p> +<p>Example usages:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">snapshot</span> + +<span class="n">sn1</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> +<span class="n">params</span> <span class="o">=</span> <span class="n">sn1</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="c1"># read all params as a dictionary</span> + +<span class="n">sn2</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param_new'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> +<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">iteritems</span><span class="p">():</span> + <span class="n">sn2</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.snapshot.Snapshot"> +<em class="property">class </em><code class="descclassname">singa.snapshot.</code><code class="descname">Snapshot</code><span class="sig-paren">(</span><em>f</em>, <em>mode</em>, <em>buffer_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot" title="Permalink to this definition">¶</a></dt> +<dd><p>Class and member functions for singa::Snapshot.</p> +<dl class="method"> +<dt id="singa.snapshot.Snapshot.read"> +<code class="descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.read" title="Permalink to this definition">¶</a></dt> +<dd><p>Call read method to load all (param_name, param_val)</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>a dict of (parameter name, parameter Tensor)</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.snapshot.Snapshot.write"> +<code class="descname">write</code><span class="sig-paren">(</span><em>param_name</em>, <em>param_val</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.write" title="Permalink to this definition">¶</a></dt> +<dd><p>Call Write method to write a parameter</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>param_name</strong> (<em>string</em>) â name of the parameter</p></li> +<li><p><strong>param_val</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â value tensor of the parameter</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="converter.html" class="btn btn-neutral float-right" title="Caffe Converter" 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href="../index.html">incubator-singa</a> + + </nav> + + + <div class="wy-nav-content"> + + <div class="rst-content"> + + + + + + + + + + + + + + + + + +<div role="navigation" aria-label="breadcrumbs navigation"> + + <ul class="wy-breadcrumbs"> + + <li><a href="../index.html">Docs</a> »</li> + + <li><a href="index.html">Documentation</a> »</li> + + <li>Software Stack</li> + + + <li class="wy-breadcrumbs-aside"> + + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><div class="section" id="software-stack"> +<h1>Software Stack<a class="headerlink" href="#software-stack" title="Permalink to this headline">¶</a></h1> +<p>SINGAâs software stack includes three major components, namely, core, IO and +model. Figure 1 illustrates these components together with the hardware. +The core component provides memory management and tensor operations; +IO has classes for reading (and writing) data from (to) disk and network; The +model component provides data structures and algorithms for machine learning models, +e.g., layers for neural network models, optimizers/initializer/metric/loss for +general machine learning models.</p> +<img src="../_static/images/singav1-sw.png" align="center" width="500px"/> +<br/> +<span><strong>Figure 1 - SINGA V1 software stack.</strong></span><div class="section" id="core"> +<h2>Core<a class="headerlink" href="#core" title="Permalink to this headline">¶</a></h2> +<p><a class="reference external" href="tensor.html">Tensor</a> and <a class="reference external" href="device.html">Device</a> are two core abstractions in SINGA. Tensor class represents a +multi-dimensional array, which stores model variables and provides linear algebra +operations for machine learning +algorithms, including matrix multiplication and random functions. Each tensor +instance (i.e. a tensor) is allocated on a Device instance. +Each Device instance (i.e. a device) is created against one hardware device, +e.g. a GPU card or a CPU core. Devices manage the memory of tensors and execute +tensor operations on its execution units, e.g. CPU threads or CUDA streams.</p> +<p>Depending on the hardware and the programming language, SINGA have implemented +the following specific device classes:</p> +<ul class="simple"> +<li><p><strong>CudaGPU</strong> represents an Nvidia GPU card. The execution units are the CUDA streams.</p></li> +<li><p><strong>CppCPU</strong> represents a normal CPU. The execution units are the CPU threads.</p></li> +<li><p><strong>OpenclGPU</strong> represents normal GPU card from both Nvidia and AMD. +The execution units are the CommandQueues. Given that OpenCL is compatible with +many hardware devices, e.g. FPGA and ARM, the OpenclGPU has the potential to be +extended for other devices.</p></li> +</ul> +<p>Different types of devices use different programming languages to write the kernel +functions for tensor operations,</p> +<ul class="simple"> +<li><p>CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for CppCPU</p></li> +<li><p>CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for CudaGPU</p></li> +<li><p>OpenclMath (tensor_math_opencl.h) implements the tensor operations using OpenCL for OpenclGPU</p></li> +</ul> +<p>In addition, different types of data, such as float32 and float16, could be supported by adding +the corresponding tensor functions.</p> +<p>Typically, users would create a device instance and pass it to create multiple +tensor instances. When users call the Tensor functions, these function would invoke +the corresponding implementation (CppMath/CudaMath/OpenclMath) automatically. In +other words, the implementation of Tensor operations is transparent to users.</p> +<p>Most machine learning algorithms could be expressed using (dense or sparse) tensors. +Therefore, with the Tensor abstraction, SINGA would be able to run a wide range of models, +including deep learning models and other traditional machine learning models.</p> +<p>The Tensor and Device abstractions are extensible to support a wide range of hardware device +using different programming languages. A new hardware device would be supported by +adding a new Device subclass and the corresponding implementation of the Tensor +operations (xxxMath).</p> +<p>Optimizations in terms of speed and memory could be implemented by Device, which +manages both operation execution and memory malloc/free. More optimization details +would be described in the <a class="reference external" href="device.html">Device page</a>.</p> +</div> +<div class="section" id="model"> +<h2>Model<a class="headerlink" href="#model" title="Permalink to this headline">¶</a></h2> +<p>On top of the Tensor and Device abstractions, SINGA provides some higher level +classes for machine learning modules.</p> +<ul class="simple"> +<li><p><a class="reference external" href="layer.html">Layer</a> and its subclasses are specific for neural networks. Every layer provides +functions for forward propagating features and backward propagating gradients w.r.t the training loss functions. +They wraps the complex layer operations so that users can easily create neural nets +by connecting a set of layers.</p></li> +<li><p><a class="reference external" href="initializer.html">Initializer</a> and its subclasses provide variant methods of initializing +model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.</p></li> +<li><p><a class="reference external" href="loss.html">Loss</a> and its subclasses defines the training objective loss functions. +Both functions of computing the loss values and computing the gradient of the prediction w.r.t the +objective loss are implemented. Example loss functions include squared error and cross entropy.</p></li> +<li><p><a class="reference external" href="metric.html">Metric</a> and its subclasses provide the function to measure the +performance of the model, e.g., the accuracy.</p></li> +<li><p><a class="reference external" href="optimizer.html">Optimizer</a> and its subclasses implement the methods for updating +model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li> +</ul> +</div> +<div class="section" id="io"> +<h2>IO<a class="headerlink" href="#io" title="Permalink to this headline">¶</a></h2> +<p>The IO module consists of classes for data loading, data preprocessing and message passing.</p> +<ul class="simple"> +<li><p>Reader and its subclasses load string records from disk files</p></li> +<li><p>Writer and its subclasses write string records to disk files</p></li> +<li><p>Encoder and its subclasses encode Tensor instances into string records</p></li> +<li><p>Decoder and its subclasses decodes string records into Tensor instances</p></li> +<li><p>Endpoint represents a communication endpoint which provides functions for passing messages to each other.</p></li> +<li><p>Message represents communication messages between Endpoint instances. It carries both meta data and payload.</p></li> +</ul> +</div> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="device.html" class="btn btn-neutral float-right" title="Device" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="installation.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. 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