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+ 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="autograd-in-singa">
+<h1>Autograd in Singa<a class="headerlink" href="#autograd-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>There are two typical ways to implement autograd, via symbolic differentiation like <a class="reference external" href="http://deeplearning.net/software/theano/index.html">Theano</a> or reverse differentiation like <a class="reference external" href="https://pytorch.org/docs/stable/notes/autograd.html">Pytorch</a>. Singa follows Pytorch way, which records the computation graph and apply the backward propagation automatically after forward propagation. The autograd algorithm is explained in details <a class="reference external" href="https://pytorch.org/docs/stable/notes/autograd.html">here</a>. We explain the relevant modules in Singa and give an example to illustrate the usage.</p>
+<div class="section" id="relevant-modules">
+<h2>Relevant Modules<a class="headerlink" href="#relevant-modules" title="Permalink to this headline">¶</a></h2>
+<p>There are three classes involved in autograd, namely <code class="docutils literal notranslate"><span class="pre">singa.tensor.Tensor</span></code> , <code class="docutils literal notranslate"><span class="pre">singa.autograd.Operation</span></code>, and <code class="docutils literal notranslate"><span class="pre">singa.autograd.Layer</span></code>. In the rest of this article, we use tensor, operation and layer to refer to an instance of the respective class.</p>
+<div class="section" id="tensor">
+<h3>Tensor<a class="headerlink" href="#tensor" title="Permalink to this headline">¶</a></h3>
+<p>Three attributes of Tensor are used by autograd,</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">.creator</span></code> is an <code class="docutils literal notranslate"><span class="pre">Operation</span></code> instance. It records the operation that generates the Tensor instance.</li>
+<li><code class="docutils literal notranslate"><span class="pre">.requires_grad</span></code> is a boolean variable. It is used to indicate that the autograd algorithm needs to compute the gradient of the tensor (i.e., the owner). For example, during backpropagation, the gradients of the tensors for the weight matrix of a linear layer and the feature maps of a convolution layer (not the bottom layer) should be computed.</li>
+<li><code class="docutils literal notranslate"><span class="pre">.stores_grad</span></code> is a boolean variable. It is used to indicate that the gradient of the owner tensor should be stored and output by the backward function. For example, the gradient of the feature maps is computed during backpropagation, but is not included in the output of the backward function.</li>
+</ul>
+<p>Programmers can change <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> and <code class="docutils literal notranslate"><span class="pre">stores_grad</span></code> of a Tensor instance. For example, if later is set to True, the corresponding gradient is included in the output of the backward function. It should be noted that if <code class="docutils literal notranslate"><span class="pre">stores_grad</span></code> is True, then <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> must be true, not vice versa.</p>
+</div>
+<div class="section" id="operation">
+<h3>Operation<a class="headerlink" href="#operation" title="Permalink to this headline">¶</a></h3>
+<p>It takes one or more <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> instances as input, and then outputs one or more <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> instances. For example, ReLU can be implemented as a specific Operation subclass. When an <code class="docutils literal notranslate"><span class="pre">Operation</span></code> instance is called (after instantiation), the following two steps are executed:</p>
+<ol class="simple">
+<li>record the source operations, i.e., the <code class="docutils literal notranslate"><span class="pre">creator</span></code>s of the input tensors. 2. do calculation by calling member function <code class="docutils literal notranslate"><span class="pre">.forward()</span></code></li>
+</ol>
+<p>There are two member functions for forwarding and backwarding, i.e., <code class="docutils literal notranslate"><span class="pre">.forward()</span></code> and <code class="docutils literal notranslate"><span class="pre">.backward()</span></code>. They take <code class="docutils literal notranslate"><span class="pre">Tensor.data</span></code> as inputs (the type is <code class="docutils literal notranslate"><span class="pre">CTensor</span></code>), and output <code class="docutils literal notranslate"><span class="pre">Ctensor</span></code>s. To add a specific operation, subclass <code class="docutils literal notranslate"><span class="pre">operation</span></code> should implement their own <code class="docutils literal notranslate"><span class="pre">.forward()</span></code> and <code class="docutils literal notranslate"><span class="pre">.backward()</span></code>. The <code class="docutils literal notranslate"><span class="pre">backward()</span></code> function is called by the <c
ode class="docutils literal notranslate"><span class="pre">backward()</span></code> function of autograd automatically during backward propogation to compute the gradients of inputs (according to the <code class="docutils literal notranslate"><span class="pre">require_grad</span></code> field).</p>
+</div>
+<div class="section" id="layer">
+<h3>Layer<a class="headerlink" href="#layer" title="Permalink to this headline">¶</a></h3>
+<p>For those operations that require parameters, we package them into a new class, <code class="docutils literal notranslate"><span class="pre">Layer</span></code>. For example, convolution operation is wrapped into a convolution layer. <code class="docutils literal notranslate"><span class="pre">Layer</span></code> manages (stores) the parameters and calls the corresponding <code class="docutils literal notranslate"><span class="pre">Operation</span></code>s to implement the transformation.</p>
+</div>
+</div>
+<div class="section" id="examples">
+<h2>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h2>
+<p>Multiple examples are provided in the <a class="reference external" href="https://github.com/apache/incubator-singa/tree/master/examples/autograd">example folder</a>. We explain two representative examples here.</p>
+<div class="section" id="operation-only">
+<h3>Operation only<a class="headerlink" href="#operation-only" title="Permalink to this headline">¶</a></h3>
+<p>The following codes implement a MLP model using only Operation instances (no Layer instances).</p>
+<div class="section" id="import-packages">
+<h4>Import packages<a class="headerlink" href="#import-packages" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa.tensor</span> <span class="k">import</span> <span class="n">Tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">autograd</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">opt</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="create-weight-matrix-and-bias-vector">
+<h4>Create weight matrix and bias vector<a class="headerlink" href="#create-weight-matrix-and-bias-vector" title="Permalink to this headline">¶</a></h4>
+<p>The parameter tensors are created with both <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> and <code class="docutils literal notranslate"><span class="pre">stores_grad</span></code> set to True.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">w0</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">w0</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+<span class="n">b0</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">b0</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+
+<span class="n">w1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">w1</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+<span class="n">b1</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">shape</span><span class="o">=</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">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">b1</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="training">
+<h4>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span> <span class="c1"># data matrix</span>
+<span class="n">target</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">label</span><span class="p">)</span> <span class="c1"># label vector</span>
+<span class="n">autograd</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="kc">True</span> <span class="c1"># for training</span>
+<span class="n">sgd</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="mf">0.05</span><span class="p">)</span> <span class="c1"># optimizer</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="mi">10</span><span class="p">):</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">w0</span><span class="p">)</span> <span class="c1"># matrix multiplication</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">add_bias</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b0</span><span class="p">)</span> <span class="c1"># add the bias vector</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># ReLU activation operation</span>
+
+ <span class="n">x</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w1</span><span class="p">)</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">add_bias</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">b1</span><span class="p">)</span>
+
+ <span class="n">loss</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
+
+ <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">loss</span><span class="p">):</span>
+ <span class="n">sgd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="operation-layer">
+<h3>Operation + Layer<a class="headerlink" href="#operation-layer" title="Permalink to this headline">¶</a></h3>
+<p>The following <a class="reference external" href="https://github.com/apache/incubator-singa/blob/master/examples/autograd/mnist_cnn.py">example</a> implements a CNN model using layers provided by the autograd module.</p>
+<div class="section" id="create-the-layers">
+<h4>Create the layers<a class="headerlink" href="#create-the-layers" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conv1</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</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="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">bn1</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
+<span class="n">pooling1</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">conv21</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">conv22</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">bn2</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
+<span class="n">linear</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">32</span> <span class="o">*</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="n">pooling2</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="define-the-forward-function">
+<h4>Define the forward function<a class="headerlink" href="#define-the-forward-function" title="Permalink to this headline">¶</a></h4>
+<p>The operations in the forward pass will be recorded automatically for backward propagation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+ <span class="c1"># x is the input data (a batch of images)</span>
+ <span class="c1"># t the the label vector (a batch of integers)</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Conv layer </span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># ReLU operation</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">bn1</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># BN layer</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">pooling1</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># Pooling Layer</span>
+
+ <span class="c1"># two parallel convolution layers</span>
+ <span class="n">y1</span> <span class="o">=</span> <span class="n">conv21</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+ <span class="n">y2</span> <span class="o">=</span> <span class="n">conv22</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># cat operation</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># ReLU operation</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">bn2</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">pooling2</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+
+ <span class="n">y</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># flatten operation</span>
+ <span class="n">y</span> <span class="o">=</span> <span class="n">linear</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="c1"># Linear layer</span>
+ <span class="n">loss</span> <span class="o">=</span> <span class="n">autograd</span><span class="o">.</span><span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># operation </span>
+ <span class="k">return</span> <span class="n">loss</span><span class="p">,</span> <span class="n">y</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="id1">
+<h4>Training<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">autograd</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</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">batch_number</span><span class="p">):</span>
+ <span class="n">inputs</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="n">device</span><span class="o">=</span><span class="n">dev</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">x_train</span><span class="p">[</span>
+ <span class="n">i</span> <span class="o">*</span> <span class="n">batch_sz</span><span class="p">:(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_sz</span><span class="p">],</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+ <span class="n">targets</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="n">device</span><span class="o">=</span><span class="n">dev</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">y_train</span><span class="p">[</span>
+ <span class="n">i</span> <span class="o">*</span> <span class="n">batch_sz</span><span class="p">:(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_sz</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stores_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+ <span class="n">loss</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span> <span class="c1"># forward the net</span>
+
+ <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">gp</span> <span class="ow">in</span> <span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">loss</span><span class="p">):</span> <span class="c1"># auto backward</span>
+ <span class="n">sgd</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">gp</span><span class="p">)</span>
+</pre></div>
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+--><div class="section" id="quickstart-cifar10-example">
+<h1>Quickstart - Cifar10 example<a class="headerlink" href="#quickstart-cifar10-example" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward artificial neural network widely used for image classification. In this example, we will use a deep CNN model to do image classification for the <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10 dataset</a>.</p>
+<div class="section" id="running-instructions-for-cpp-version">
+<h2>Running instructions for CPP version<a class="headerlink" href="#running-instructions-for-cpp-version" title="Permalink to this headline">¶</a></h2>
+<p>Please refer to <a class="reference external" href="installation.html">Installation</a> page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN.</p>
+<p>The Cifar10 dataset could be downloaded by running</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># switch to cifar10 directory
+$ cd ../examples/cifar10
+# download data for CPP version
+$ python download_data.py bin
+</pre></div>
+</div>
+<p>âbinâ is for downloading binary version of Cifar10 data.</p>
+<p>During downloading, you should see the detailed output like</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+</pre></div>
+</div>
+<p>Now you have prepared the data for this Cifar10 example, the final step is to execute the <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> script,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># in SINGA_ROOT/examples/cifar10/
+$ ./run.sh
+</pre></div>
+</div>
+<p>You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> file.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Start</span> <span class="n">training</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_1</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_2</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_3</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_4</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">data_batch_5</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Reading</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="nb">bin</span><span class="o">/</span><span class="n">test_batch</span><span class="o">.</span><span class="n">bin</span>
+<span class="n">Training</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">50000</span><span class="p">,</span> <span class="n">Test</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">conv1</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">32</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">pool1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">relu1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">lrn1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">conv2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">relu2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">pool2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">lrn2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">conv3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">relu3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">pool3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">flat</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">ip</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="p">)</span>
+<span class="n">conv1_weight</span> <span class="p">:</span> <span class="mf">8.09309e-05</span>
+<span class="n">conv1_bias</span> <span class="p">:</span> <span class="mi">0</span>
+<span class="n">conv2_weight</span> <span class="p">:</span> <span class="mf">0.00797731</span>
+<span class="n">conv2_bias</span> <span class="p">:</span> <span class="mi">0</span>
+<span class="n">conv3_weight</span> <span class="p">:</span> <span class="mf">0.00795888</span>
+<span class="n">conv3_bias</span> <span class="p">:</span> <span class="mi">0</span>
+<span class="n">ip_weight</span> <span class="p">:</span> <span class="mf">0.00798683</span>
+<span class="n">ip_bias</span> <span class="p">:</span> <span class="mi">0</span>
+<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">train_perf</span>
+<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">val_perf</span>
+<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.828369</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.329420</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.561823</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.420600</span>
+<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.465898</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.469940</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.361778</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.513300</span>
+<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.320708</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.529000</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.242080</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.549100</span>
+<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.213776</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.571620</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span>
+<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.175346</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.582000</span>
+</pre></div>
+</div>
+<p>The training details are stored in <code class="docutils literal notranslate"><span class="pre">train_perf</span></code> file in the same directory and the validation details in <code class="docutils literal notranslate"><span class="pre">val_perf</span></code> file.</p>
+</div>
+<div class="section" id="running-instructions-for-python-version">
+<h2>Running instructions for Python version<a class="headerlink" href="#running-instructions-for-python-version" title="Permalink to this headline">¶</a></h2>
+<p>To run CNN example in Python version, we need to compile SINGA with Python binding,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ mkdir build && cd build
+$ cmake -DUSE_PYTHON=ON ..
+$ make
+</pre></div>
+</div>
+<p>Now download the Cifar10 dataset,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># switch to cifar10 directory
+$ cd ../examples/cifar10
+# download data for Python version
+$ python download_data.py py
+</pre></div>
+</div>
+<p>During downloading, you should see the detailed output like</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+ The tar file does exist. Extracting it now..
+ Finished!
+</pre></div>
+</div>
+<p>Then execute the <code class="docutils literal notranslate"><span class="pre">train.py</span></code> script to build the model</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python train.py
+</pre></div>
+</div>
+<p>You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">)</span>
+<span class="p">(</span><span class="mi">1024</span><span class="n">L</span><span class="p">,)</span>
+<span class="n">Start</span> <span class="n">intialization</span><span class="o">............</span>
+<span class="n">conv1_weight</span> <span class="n">gaussian</span> <span class="mf">7.938460476e-05</span>
+<span class="n">conv1_bias</span> <span class="n">constant</span> <span class="mf">0.0</span>
+<span class="n">conv2_weight</span> <span class="n">gaussian</span> <span class="mf">0.00793507322669</span>
+<span class="n">conv2_bias</span> <span class="n">constant</span> <span class="mf">0.0</span>
+<span class="n">conv3_weight</span> <span class="n">gaussian</span> <span class="mf">0.00799657031894</span>
+<span class="n">conv3_bias</span> <span class="n">constant</span> <span class="mf">0.0</span>
+<span class="n">dense_weight</span> <span class="n">gaussian</span> <span class="mf">0.00804364029318</span>
+<span class="n">dense_bias</span> <span class="n">constant</span> <span class="mf">0.0</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="o">..................</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_1</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_2</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_3</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_4</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">data_batch_5</span>
+<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span><span class="o">/</span><span class="n">test_batch</span>
+<span class="n">Epoch</span> <span class="mi">0</span>
+<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.881866</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.306360</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.420000</span>
+<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.602577</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.412200</span>
+<span class="n">Epoch</span> <span class="mi">1</span>
+<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.536011</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.441940</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.500000</span>
+<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.378170</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.507600</span>
+<span class="n">Epoch</span> <span class="mi">2</span>
+<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.333137</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.519960</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.520000</span>
+<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.272205</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540600</span>
+<span class="n">Epoch</span> <span class="mi">3</span>
+<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.185212</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.574120</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540000</span>
+<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.211573</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.567600</span>
+</pre></div>
+</div>
+<p>This script will call <code class="docutils literal notranslate"><span class="pre">alexnet.py</span></code> file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file <code class="docutils literal notranslate"><span class="pre">model.bin</span></code> in the same directory. Then we can use this <code class="docutils literal notranslate"><span class="pre">model.bin</span></code> file for prediction.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python predict.py
+</pre></div>
+</div>
+</div>
+</div>
+
+
+ </div>
+
+ </div>
+ <footer>
+
+
+ <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. All other marks mentioned may be trademarks or registered trademarks of their respective owners.
+
+ </p>
+ </div>
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Added: incubator/singa/site/trunk/docs/converter.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/converter.html?rev=1857927&view=auto
==============================================================================
--- incubator/singa/site/trunk/docs/converter.html (added)
+++ incubator/singa/site/trunk/docs/converter.html Sun Apr 21 22:05:49 2019
@@ -0,0 +1,299 @@
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