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| <div class="section" id="creating-custom-operators-with-numpy"> |
| <span id="creating-custom-operators-with-numpy"></span><h1>Creating custom operators with numpy<a class="headerlink" href="#creating-custom-operators-with-numpy" title="Permalink to this headline">¶</a></h1> |
| <p>In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:</p> |
| <ul class="simple"> |
| <li>Custom operator without any <code class="docutils literal"><span class="pre">Parameter</span></code>s</li> |
| <li>Custom operator with <code class="docutils literal"><span class="pre">Parameter</span></code>s</li> |
| </ul> |
| <p>Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</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="p">,</span> <span class="n">autograd</span> |
| </pre></div> |
| </div> |
| <div class="section" id="parameter-less-operators"> |
| <span id="parameter-less-operators"></span><h2>Parameter-less operators<a class="headerlink" href="#parameter-less-operators" title="Permalink to this headline">¶</a></h2> |
| <p>This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator <code class="docutils literal"><span class="pre">mx.nd.relu</span></code>.</p> |
| <div class="section" id="forward-backward-implementation"> |
| <span id="forward-backward-implementation"></span><h3>Forward & backward implementation<a class="headerlink" href="#forward-backward-implementation" title="Permalink to this headline">¶</a></h3> |
| <p>First we implement the forward and backward computation by sub-classing <code class="docutils literal"><span class="pre">mx.operator.CustomOp</span></code>:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Sigmoid</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOp</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span> |
| <span class="sd">"""Implements forward computation.</span> |
| |
| <span class="sd"> is_train : bool, whether forwarding for training or testing.</span> |
| <span class="sd"> req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.</span> |
| <span class="sd"> in_data : list of NDArray, input data.</span> |
| <span class="sd"> out_data : list of NDArray, pre-allocated output buffers.</span> |
| <span class="sd"> aux : list of NDArray, mutable auxiliary states. Usually not used.</span> |
| <span class="sd"> """</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">out_grad</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">in_grad</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span> |
| <span class="sd">"""Implements backward computation</span> |
| |
| <span class="sd"> req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad</span> |
| <span class="sd"> out_grad : list of NDArray, gradient w.r.t. output data.</span> |
| <span class="sd"> in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.</span> |
| <span class="sd"> """</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">dy</span> <span class="o">=</span> <span class="n">out_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">dx</span> <span class="o">=</span> <span class="n">dy</span><span class="o">*</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span><span class="o">*</span><span class="n">y</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">in_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dx</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="register-custom-operator"> |
| <span id="register-custom-operator"></span><h3>Register custom operator<a class="headerlink" href="#register-custom-operator" title="Permalink to this headline">¶</a></h3> |
| <p>Then we need to register the custom op and describe it’s properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing <code class="docutils literal"><span class="pre">mx.operator.CustomOpProp</span></code>:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nd">@mx.operator.register</span><span class="p">(</span><span class="s2">"sigmoid"</span><span class="p">)</span> <span class="c1"># register with name "sigmoid"</span> |
| <span class="k">class</span> <span class="nc">SigmoidProp</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOpProp</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="nb">super</span><span class="p">(</span><span class="n">SigmoidProp</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="bp">True</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">list_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># this can be omitted if you only have 1 input.</span> |
| <span class="k">return</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">list_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># this can be omitted if you only have 1 output.</span> |
| <span class="k">return</span> <span class="p">[</span><span class="s1">'output'</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">):</span> |
| <span class="sd">"""Calculate output shapes from input shapes. This can be</span> |
| <span class="sd"> omited if all your inputs and outputs have the same shape.</span> |
| |
| <span class="sd"> in_shapes : list of shape. Shape is described by a tuple of int.</span> |
| <span class="sd"> """</span> |
| <span class="n">data_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">output_shape</span> <span class="o">=</span> <span class="n">data_shape</span> |
| <span class="c1"># return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">,),</span> <span class="p">(</span><span class="n">output_shape</span><span class="p">,),</span> <span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">create_operator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_dtypes</span><span class="p">):</span> |
| <span class="c1"># create and return the CustomOp class.</span> |
| <span class="k">return</span> <span class="n">Sigmoid</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="example-usage"> |
| <span id="example-usage"></span><h3>Example Usage<a class="headerlink" href="#example-usage" title="Permalink to this headline">¶</a></h3> |
| <p>We can now use this operator by calling <code class="docutils literal"><span class="pre">mx.nd.Custom</span></code>:</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">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| <span class="c1"># attach gradient buffer to x for autograd</span> |
| <span class="n">x</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span> |
| <span class="c1"># forward in a record() section to save computation graph for backward</span> |
| <span class="c1"># see autograd tutorial to learn more.</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">y</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">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">)</span> |
| <span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># call backward computation</span> |
| <span class="n">y</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="c1"># gradient is now saved to the grad buffer we attached previously</span> |
| <span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="parametrized-operator"> |
| <span id="parametrized-operator"></span><h2>Parametrized Operator<a class="headerlink" href="#parametrized-operator" title="Permalink to this headline">¶</a></h2> |
| <p>In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.</p> |
| <p>The dense operator performs a dot product between data and weight, then add bias to it.</p> |
| <div class="section" id="forward-backward-implementation"> |
| <span id="id1"></span><h3>Forward & backward implementation<a class="headerlink" href="#forward-backward-implementation" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Dense</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOp</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="n">bias</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="n">bias</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">weight</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">out_grad</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">in_grad</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">dy</span> <span class="o">=</span> <span class="n">out_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">dx</span> <span class="o">=</span> <span class="n">dy</span><span class="o">.</span><span class="n">T</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="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">in_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dx</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="registration"> |
| <span id="registration"></span><h3>Registration<a class="headerlink" href="#registration" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nd">@mx.operator.register</span><span class="p">(</span><span class="s2">"dense"</span><span class="p">)</span> <span class="c1"># register with name "sigmoid"</span> |
| <span class="k">class</span> <span class="nc">DenseProp</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOpProp</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="n">bias</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DenseProp</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="bp">True</span><span class="p">)</span> |
| <span class="c1"># we use constant bias here to illustrate how to pass arguments</span> |
| <span class="c1"># to operators. All arguments are in string format so you need</span> |
| <span class="c1"># to convert them back to the type you want.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bias</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">list_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">,</span> <span class="s1">'weight'</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">list_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># this can be omitted if you only have 1 output.</span> |
| <span class="k">return</span> <span class="p">[</span><span class="s1">'output'</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">):</span> |
| <span class="n">data_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">weight_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">output_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">weight_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="c1"># return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">,</span> <span class="n">weight_shape</span><span class="p">),</span> <span class="p">(</span><span class="n">output_shape</span><span class="p">,),</span> <span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">create_operator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_dtypes</span><span class="p">):</span> |
| <span class="c1"># create and return the CustomOp class.</span> |
| <span class="k">return</span> <span class="n">Dense</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_bias</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="use-customop-together-with-block"> |
| <span id="use-customop-together-with-block"></span><h3>Use CustomOp together with Block<a class="headerlink" href="#use-customop-together-with-block" title="Permalink to this headline">¶</a></h3> |
| <p>Parameterized CustomOp are ususally used together with Blocks, which holds the parameter.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DenseBlock</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Block</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="n">in_channels</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">bias</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">DenseBlock</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="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="n">bias</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">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">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">context</span> |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_bias</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'dense'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="example-usage"> |
| <span id="id2"></span><h3>Example usage<a class="headerlink" href="#example-usage" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dense</span> <span class="o">=</span> <span class="n">DenseBlock</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="mf">0.1</span><span class="p">)</span> |
| <span class="n">dense</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">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">dense</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> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
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| <h3><a href="../../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Creating custom operators with numpy</a><ul> |
| <li><a class="reference internal" href="#parameter-less-operators">Parameter-less operators</a><ul> |
| <li><a class="reference internal" href="#forward-backward-implementation">Forward & backward implementation</a></li> |
| <li><a class="reference internal" href="#register-custom-operator">Register custom operator</a></li> |
| <li><a class="reference internal" href="#example-usage">Example Usage</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#parametrized-operator">Parametrized Operator</a><ul> |
| <li><a class="reference internal" href="#forward-backward-implementation">Forward & backward implementation</a></li> |
| <li><a class="reference internal" href="#registration">Registration</a></li> |
| <li><a class="reference internal" href="#use-customop-together-with-block">Use CustomOp together with Block</a></li> |
| <li><a class="reference internal" href="#example-usage">Example usage</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
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