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<h1>前馈网络<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h1>
<p>Neural net类用层来创建网络并提供可以获取网络信息（比如：参数）的函数。</p>
<p>示例用法：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">net</span> <span class="k">as</span> <span class="n">ffnet</span>
<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span>
<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">loss</span>
<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">layer</span>
<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span>
<span class="c1"># create net and add layers</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">ffnet</span><span class="o">.</span><span class="n">FeedForwardNet</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">(),</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">())</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="s1">&#39;conv1&#39;</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="n">input_sample_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</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="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu1&#39;</span><span class="p">))</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="s1">&#39;pool1&#39;</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">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="s1">&#39;flat&#39;</span><span class="p">))</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="s1">&#39;dense&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="c1"># init parameters</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">param_values</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">p</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">p</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="c1"># move net onto gpu</span>
<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu</span><span class="p">()</span>
<span class="n">net</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">dev</span><span class="p">)</span>
<span class="c1"># training (skipped)</span>
<span class="c1"># do prediction after training</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</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="n">dev</span><span class="p">)</span>
<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">print</span> <span class="n">tensor</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<hr class="docutils" />
<div class="section" id="class-singa-net-feedforwardnet-loss-none-metric-none">
<h2>class singa.net.FeedForwardNet(loss=None, metric=None)<a class="headerlink" href="#class-singa-net-feedforwardnet-loss-none-metric-none" title="Permalink to this headline"></a></h2>
<p>基类：<code class="docutils literal"><span class="pre">object</span></code></p>
<div class="section" id="to-device-dev">
<h3>to_device(dev)<a class="headerlink" href="#to-device-dev" title="Permalink to this headline"></a></h3>
<p>将网络移至指定设备上，包括所有参数和中间数据。</p>
</div>
<hr class="docutils" />
<div class="section" id="add-lyr-src-none">
<h3>add(lyr, src=None)<a class="headerlink" href="#add-lyr-src-none" title="Permalink to this headline"></a></h3>
<p>添加一个层到层列表中。</p>
<p>该功能将从src层获取样本形状以设置新添加的层。 对于第一层，它被设置在外部。 调用函数应确保层顺序的正确性。 如果src是None，最后一层是src层。 如果有多个src图层，则src是src层的列表。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>lyr (Layer)</strong> – 待添加的层</li>
<li><strong>src (Layer)</strong> – lyr层的父层</li>
</ul>
</div>
<hr class="docutils" />
<div class="section" id="param-values">
<h3>param_values()<a class="headerlink" href="#param-values" title="Permalink to this headline"></a></h3>
<p>返回所有参数的tensor列表。</p>
</div>
<hr class="docutils" />
<div class="section" id="param-specs">
<h3>param_specs()<a class="headerlink" href="#param-specs" title="Permalink to this headline"></a></h3>
<p>返回所有参数的ParamSpec列表。</p>
</div>
<hr class="docutils" />
<div class="section" id="param-names">
<h3>param_names()<a class="headerlink" href="#param-names" title="Permalink to this headline"></a></h3>
<p>返回所有参数名列表。。</p>
</div>
<hr class="docutils" />
<div class="section" id="train-x-y">
<h3>train(x, y)<a class="headerlink" href="#train-x-y" title="Permalink to this headline"></a></h3>
<p>运行一次BP。
目前仅支持单输出层、单损失函数及度量方法的网络。 TODO(wangwei) 考虑多损失函数和多度量值。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>x</strong> – 输入数据，一个输入tensor或字典：层名-&gt;tensor</li>
<li><strong>y</strong> – 输入数据的标签，一个tensor</li>
</ul>
<p><strong>返回值：</strong> 参数梯度，损失函数和度量值</p>
</div>
<hr class="docutils" />
<div class="section" id="evaluate-x-y">
<h3>evaluate(x, y)<a class="headerlink" href="#evaluate-x-y" title="Permalink to this headline"></a></h3>
<p>根据给定数据评估损失函数和度量值。目前仅支持单输出层、单损失函数及度量方法的网络。TODO(wangwei) 考虑多损失函数和多度量值。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>x</strong> – 输入数据，单个tensor或一个字典： 层名 -&gt; tensor</li>
<li><strong>y</strong> – 输入数据的标签，单个tensor.</li>
</ul>
</div>
<hr class="docutils" />
<div class="section" id="predict-x">
<h3>predict(x)<a class="headerlink" href="#predict-x" title="Permalink to this headline"></a></h3>
<p>向前经每个层传递数据到输出层并获得输出值。
目前仅支持单输出层的网络。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>x</strong> - 输入数据，单个tesnor或一个字典： 层名 -&gt; tensor</li>
</ul>
<p><strong>返回值：</strong> 单个输出tensor作为预测结果</p>
</div>
<hr class="docutils" />
<div class="section" id="topo-sort-layers-src-of-layer">
<h3>topo_sort(layers, src_of_layer)<a class="headerlink" href="#topo-sort-layers-src-of-layer" title="Permalink to this headline"></a></h3>
<p>对所有层进行拓扑排序。
对于多输入层，将会保留输入层的顺序。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>layers</strong> – 层列表；同个层（如slice层）的多个输出层应该以正确的顺序加入，此功能将不会改变其顺序。</li>
<li><strong>src_of_layer</strong> – 字典: src层名 -&gt; src层列表</li>
</ul>
<p><strong>返回值：</strong> 排序后层列表</p>
</div>
<hr class="docutils" />
<div class="section" id="forward-flag-x-output">
<h3>forward(flag, x, output=[])<a class="headerlink" href="#forward-flag-x-output" title="Permalink to this headline"></a></h3>
<p>将输入经过每个层向前传递。
如果一个层具有来自其他层和来自x的输入，则来自x的数据在来自其他层的数据之前被排序，例如，如果层1-&gt;层2并且x [‘layer2’]具有数据，则输入层2展平，即[x [&#8216;layer2&#8216;]，层1的输出]</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>flag</strong> – True代表训练;False代表评估;也可以是model_pb2.kTrain或model_pb2.kEval或者其他未来可能使用的值。</li>
<li><strong>x</strong> – 一个tensor或一个字典：层名 -&gt; tensor</li>
<li><strong>output(list)</strong> – 层名列表，将会和默认输出一起作为返回值</li>
</ul>
<p><strong>返回值：</strong> 如果只有一个输出层，返回输出tensor;否则返回字典：层名-&gt;输出tensor</p>
</div>
<hr class="docutils" />
<div class="section" id="backward">
<h3>backward()<a class="headerlink" href="#backward" title="Permalink to this headline"></a></h3>
<p>运行向后传递</p>
<p><strong>返回值：</strong> 所有参数的梯度tensor列表。</p>
</div>
<hr class="docutils" />
<div class="section" id="save-f-buffer-size-10-use-pickle-false">
<h3>save(f, buffer_size=10, use_pickle=False)<a class="headerlink" href="#save-f-buffer-size-10-use-pickle-false" title="Permalink to this headline"></a></h3>
<p>用io/snapshot保存模型参数。</p>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><strong>f</strong> – 文件名</li>
<li><strong>buffer_size</strong> – 输入输出的大小(MB)，默认为10MB；请确保它比任何一个参数对象要大。</li>
<li><strong>use_pickle(boolean)</strong> – 如果为真，将使用pickle保存；否则，将用protobuf做序列化，会占用较少空间。</li>
</ul>
</div>
<hr class="docutils" />
<div class="section" id="load-f-buffer-size-10-use-pickle-false">
<h3>load(f, buffer_size=10, use_pickle=False)<a class="headerlink" href="#load-f-buffer-size-10-use-pickle-false" title="Permalink to this headline"></a></h3>
<p>用io/snapshot加载模型参数。请参照save()的参数描述。</p>
<hr class="docutils" />
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