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   <div class="section" id="interactive-training-using-python">
 <span id="interactive-training-using-python"></span><h1>Interactive Training using Python<a class="headerlink" href="#interactive-training-using-python" title="Permalink to this headline">¶</a></h1>
 <hr class="docutils" />
 <p><code class="docutils literal"><span class="pre">Layer</span></code> class (<a class="reference external" href="layer.py">layer.py</a>) has the following methods for an interactive training.
 For the basic usage of Python binding features, please refer to <a class="reference external" href="python.md">python.md</a>.</p>
 <p><strong>ComputeFeature(self, *srclys)</strong></p>
 <ul class="simple">
 <li>This method creates and sets up singa::Layer and maintains its source layers, then call singa::Layer::ComputeFeature(...) for data transformation.<ul>
 <li><code class="docutils literal"><span class="pre">*srclys</span></code>: (an arbtrary number of) source layers</li>
 </ul>
 </li>
 </ul>
 <p><strong>ComputeGradient(self)</strong></p>
 <ul class="simple">
 <li>This method creates calls singa::Layer::ComputeGradient(...) for gradient computation.</li>
 </ul>
 <p><strong>GetParams(self)</strong></p>
 <ul class="simple">
 <li>This method calls singa::Layer::GetParam() to retrieve parameter values of the layer. Currently, it returns weight and bias. Each parameter is a 2D numpy array.</li>
 </ul>
 <p><strong>SetParams(self, *params)</strong></p>
 <ul class="simple">
 <li>This method sets parameter values of the layer.<ul>
 <li><code class="docutils literal"><span class="pre">*params</span></code>: (an arbitrary number of) parameters, each of which is a 2D numpy array. Typically, it sets weight and bias, 2D numpy array.</li>
 </ul>
 </li>
 </ul>
 <hr class="docutils" />
 <p><code class="docutils literal"><span class="pre">Dummy</span></code> class is a subclass of <code class="docutils literal"><span class="pre">Layer</span></code>, which is provided to fetch input data and/or label information.
 Specifically, it creates singa::DummyLayer.</p>
 <p><strong>Feed(self, shape, data, aux_data)</strong></p>
 <ul class="simple">
 <li>This method sets input data and/or auxiary data such as labels.<ul>
 <li><code class="docutils literal"><span class="pre">shape</span></code>: the shape (width and height) of dataset</li>
 <li><code class="docutils literal"><span class="pre">data</span></code>: input dataset</li>
 <li><code class="docutils literal"><span class="pre">aux_data</span></code>: auxiary dataset (e.g., labels)</li>
 </ul>
 </li>
 </ul>
 <p>In addition, <code class="docutils literal"><span class="pre">Dummy</span></code> class has two subclasses named <code class="docutils literal"><span class="pre">ImageInput</span></code> and <code class="docutils literal"><span class="pre">LabelInput</span></code>.</p>
 <ul>
 <li><p class="first"><code class="docutils literal"><span class="pre">ImageInput</span></code> class will take three arguments as follows.</p>
 <p><strong>__init__(self, height=None, width=None, nb_channel=1)</strong></p>
 </li>
 <li><p class="first">Both <code class="docutils literal"><span class="pre">ImageInput</span></code> and <code class="docutils literal"><span class="pre">LabelInput</span></code> classes have their own Feed method to call Feed of Dummy class.</p>
 <p><strong>Feed(self, data)</strong></p>
 </li>
 </ul>
 <!--<p>Users can save or load model parameter (e.g., weight and bias) at anytime during training.
 The following methods are provided in <code class="docutils literal"><span class="pre">model.py</span></code>.</p>
 <p><strong>save_model_parameter(step, fout, neuralnet)</strong></p>
 <ul class="simple">
 <li>This method saves model parameters into the specified checkpoint (fout).<ul>
 <li><code class="docutils literal"><span class="pre">step</span></code>: the step id of training</li>
 <li><code class="docutils literal"><span class="pre">fout</span></code>: the name of checkpoint (output filename)</li>
 <li><code class="docutils literal"><span class="pre">neuralnet</span></code>: neural network model, i.e., a list of layers</li>
 </ul>
 </li>
 </ul>
 <p><strong>load_model_parameter(fin, neuralnet, batchsize=1, data_shape=None)</strong></p>
 <ul class="simple">
 <li>This method loads model parameters from the specified checkpoint (fin).<ul>
 <li><code class="docutils literal"><span class="pre">fin</span></code>: the name of checkpoint (input filename)</li>
 <li><code class="docutils literal"><span class="pre">neuralnet</span></code>: neural network model, i.e., a list of layers</li>
 <li><code class="docutils literal"><span class="pre">batchsize</span></code>:</li>
 <li><code class="docutils literal"><span class="pre">data_shape</span></code>:
 &#8211;&gt;</li>
 </ul>
 </li>
 </ul>
 <hr class="docutils" />
 <div class="section" id="example-scripts-for-the-interactive-training">
 <span id="example-scripts-for-the-interactive-training"></span><h2>Example scripts for the interactive training<a class="headerlink" href="#example-scripts-for-the-interactive-training" title="Permalink to this headline">¶</a></h2>
 <p>Two example scripts are provided at <a class="reference external" href="#"><code class="docutils literal"><span class="pre">train_mnist.py</span></code></a> and <a class="reference external" href="#"><code class="docutils literal"><span class="pre">train_cifar10.py</span></code></a>, one is training MLP model for MNIST dataset, and another is training CNN model for CIFAR10 dataset.</p>
 <ul class="simple">
 <li>Assume that <code class="docutils literal"><span class="pre">nn</span></code> is a neural network model, i.e., a list of layers. Currently, this examples considers sequential models. Example MLP and CNN are shown below.</li>
 <li><code class="docutils literal"><span class="pre">load_dataset()</span></code> method loads input data and corresponding labels, each of which is a 2D numpy array.
 For example, loading MNIST dataset returns x: [60000 x 784] and y: [60000 x 1]. Loading CIFAR10 dataset, x: [10000 x 3072] and y: [10000 x 1].</li>
 <li><code class="docutils literal"><span class="pre">sgd</span></code> is an Updater instance. Please see <a class="reference external" href="python.md"><code class="docutils literal"><span class="pre">python.md</span></code></a> and <a class="reference external" href="#"><code class="docutils literal"><span class="pre">model.py</span></code></a> for more details.</li>
 </ul>
 <div class="section" id="basic-steps-for-the-interactive-training">
 <span id="basic-steps-for-the-interactive-training"></span><h3>Basic steps for the interactive training<a class="headerlink" href="#basic-steps-for-the-interactive-training" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li>Step 1: Prepare batchsized data and corresponding label information, and then input the data using <code class="docutils literal"><span class="pre">Feed()</span></code> method.</li>
 <li>Step 2: (a) Transform data according to neuralnet (nn) structure using <code class="docutils literal"><span class="pre">ComputeFeature()</span></code>. Note that this example considers a sequential model, so it uses a simple loop. (b) Users need to provide <code class="docutils literal"><span class="pre">label</span></code> information for loss layer to compute loss function. (c) Users can print out the training performance, e.g., loss and accuracy.</li>
 <li>Step 3: Compute gradient in a reverse order of neuralnet (nn) structure using <code class="docutils literal"><span class="pre">ComputeGradient()</span></code>.</li>
 <li>Step 4: Update parameters, e.g., weight and bias, of layers using <code class="docutils literal"><span class="pre">Update()</span></code> of the updater.</li>
 </ul>
 <p>Here is an example script for the interactive training.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">bsize</span> <span class="o">=</span> <span class="mi">64</span>      <span class="c1"># batchsize</span>
 <span class="n">disp_freq</span> <span class="o">=</span> <span class="mi">10</span>  <span class="c1"># step to show the training accuracy</span>

 <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">()</span>

 <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">bsize</span><span class="p">):</span>

     <span class="c1"># (Step1) Input data containing &quot;bsize&quot; samples</span>
     <span class="n">xb</span><span class="p">,</span> <span class="n">yb</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="n">bsize</span><span class="p">:(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">bsize</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="o">*</span><span class="n">bsize</span><span class="p">:(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">bsize</span><span class="p">,</span> <span class="p">:]</span>
     <span class="n">nn</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">Feed</span><span class="p">(</span><span class="n">xb</span><span class="p">)</span>
     <span class="n">label</span><span class="o">.</span><span class="n">Feed</span><span class="p">(</span><span class="n">yb</span><span class="p">)</span>

     <span class="c1"># (Step2-a) Transform data according to the neuralnet (nn) structure</span>
     <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">nn</span><span class="p">)):</span>
         <span class="n">nn</span><span class="p">[</span><span class="n">h</span><span class="p">]</span><span class="o">.</span><span class="n">ComputeFeature</span><span class="p">(</span><span class="n">nn</span><span class="p">[</span><span class="n">h</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

     <span class="c1"># (Step2-b) Provide label to compute loss function</span>
     <span class="n">loss</span><span class="o">.</span><span class="n">ComputeFeature</span><span class="p">(</span><span class="n">nn</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="p">)</span>

     <span class="c1"># (Step2-c) Print out performance, e.g., loss and accuracy</span>
     <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">disp_freq</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="nb">print</span> <span class="s1">&#39;  Step </span><span class="si">{:&gt;3}</span><span class="s1">: &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span>
         <span class="n">loss</span><span class="o">.</span><span class="n">display</span><span class="p">()</span>

     <span class="c1"># (Step3) Compute gradient in a reverse order</span>
     <span class="n">loss</span><span class="o">.</span><span class="n">ComputeGradient</span><span class="p">()</span>
     <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nn</span><span class="p">)</span><span class="o">-</span><span class="mi">1</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">nn</span><span class="p">[</span><span class="n">h</span><span class="p">]</span><span class="o">.</span><span class="n">ComputeGradient</span><span class="p">()</span>
         <span class="c1"># (Step 4) Update parameter</span>
         <span class="n">sgd</span><span class="o">.</span><span class="n">Update</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">nn</span><span class="p">[</span><span class="n">h</span><span class="p">])</span>
 </pre></div>
 </div>
 <p><a id="model"></a></p>
 </div>
 <div class="section" id="a-href-model-example-mlp-a">
 <span id="a-href-model-example-mlp-a"></span><h3><a href="#model">Example MLP</a><a class="headerlink" href="#a-href-model-example-mlp-a" title="Permalink to this headline">¶</a></h3>
 <p>Here is an example MLP model with 5 fully-connected hidden layers.
 Please refer to <a class="reference external" href="python.md"><code class="docutils literal"><span class="pre">python.md</span></code></a> and <a class="reference external" href="#"><code class="docutils literal"><span class="pre">layer.py</span></code></a> for more details about layer definition. <code class="docutils literal"><span class="pre">SGD()</span></code> is an updater defined in <a class="reference external" href="#"><code class="docutils literal"><span class="pre">model.py</span></code></a>.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">ImageInput</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="c1"># image width and height</span>
 <span class="n">label</span> <span class="o">=</span> <span class="n">LabelInput</span><span class="p">()</span>

 <span class="n">nn</span> <span class="o">=</span> <span class="p">[]</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2500</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;stanh&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;stanh&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1500</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;stanh&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;stanh&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;stanh&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;uniform&#39;</span><span class="p">))</span>
 <span class="n">loss</span> <span class="o">=</span> <span class="n">Loss</span><span class="p">(</span><span class="s1">&#39;softmaxloss&#39;</span><span class="p">)</span>

 <span class="n">sgd</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">0.001</span><span class="p">,</span> <span class="n">lr_type</span><span class="o">=</span><span class="s1">&#39;step&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="a-href-model2-example-cnn-a">
 <span id="a-href-model2-example-cnn-a"></span><h3><a href="#model2">Example CNN</a><a class="headerlink" href="#a-href-model2-example-cnn-a" title="Permalink to this headline">¶</a></h3>
 <p>Here is an example MLP model with 3 convolution and pooling layers.
 Please refer to <a class="reference external" href="#"><code class="docutils literal"><span class="pre">python.md</span></code></a> and <a class="reference external" href="#"><code class="docutils literal"><span class="pre">layer.py</span></code></a> for more details about layer definition. <code class="docutils literal"><span class="pre">SGD()</span></code> is an updater defined in <a class="reference external" href="#"><code class="docutils literal"><span class="pre">model.py</span></code></a>.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">ImageInput</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="c1"># image width, height, channel</span>
 <span class="n">label</span> <span class="o">=</span> <span class="n">LabelInput</span><span class="p">()</span>

 <span class="n">nn</span> <span class="o">=</span> <span class="p">[]</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Convolution2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">5</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="n">w_std</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span> <span class="n">b_lr</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LRN2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.00005</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Convolution2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">5</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="n">b_lr</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AvgPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LRN2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.00005</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.75</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Convolution2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">5</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="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">AvgPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">w_wd</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">b_lr</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">b_wd</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
 <span class="n">loss</span> <span class="o">=</span> <span class="n">Loss</span><span class="p">(</span><span class="s1">&#39;softmaxloss&#39;</span><span class="p">)</span>

 <span class="n">sgd</span> <span class="o">=</span> <span class="n">SGD</span><span class="p">(</span><span class="n">decay</span><span class="o">=</span><span class="mf">0.004</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">lr_type</span><span class="o">=</span><span class="s1">&#39;manual&#39;</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">60000</span><span class="p">,</span><span class="mi">65000</span><span class="p">),</span> <span class="n">step_lr</span><span class="o">=</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span><span class="mf">0.0001</span><span class="p">,</span><span class="mf">0.00001</span><span class="p">))</span>
 </pre></div>
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