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From wang...@apache.org
Subject svn commit: r1833193 [16/17] - in /incubator/singa/site/trunk: en/ en/_sources/ en/_sources/develop/ en/_sources/docs/ en/_sources/docs/model_zoo/examples/imagenet/densenet/ en/_sources/docs/model_zoo/examples/imagenet/vgg/ en/_sources/releases/ en/com...
Date Fri, 08 Jun 2018 15:29:02 GMT
Added: incubator/singa/site/trunk/zh/docs/tensor.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/tensor.html?rev=1833193&view=auto
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--- incubator/singa/site/trunk/zh/docs/tensor.html (added)
+++ incubator/singa/site/trunk/zh/docs/tensor.html Fri Jun  8 15:29:00 2018
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+<li class="toctree-l4"><a class="reference internal" href="#class-singa-tensor-tensor-shape-none-device-none-dtype-0">class singa.tensor.Tensor(shape=None, device=None, dtype=0)</a></li>
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+  <div class="section" id="tensor">
+<span id="tensor"></span><h1>张量(Tensor)<a class="headerlink" href="#tensor" title="Permalink to this headline">¶</a></h1>
+<p>每个Tensor实例都是一个分配在特定Device实例上的多维数组。 Tensor实例存储了变量并提供了用户不可见的支持多种设备的代数操作。注意,用户需要确保除了拷贝之外的tensor操作都是在相同的设备上进行的。</p>
+<p>Tensor的实现</p>
+<p>SINGA有三种Tensor函数的实现,分别在不同设备上。</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">tensor_math_cpp.h</span></code> 用Cpp实现了CppCPU上的各种操作</li>
+<li><code class="docutils literal notranslate"><span class="pre">tensor_math_cuda.h</span></code> 用Cuda (和cuBLAS)实现了CudaGPU上的各种操作</li>
+<li><code class="docutils literal notranslate"><span class="pre">tensor_math_opencl.h</span></code> 用OpenCL实现了OpenclGPU上的各种操作</li>
+</ul>
+<div class="section" id="python-api">
+<span id="python-api"></span><h2>PYTHON API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
+<p>用法示例:</p>
+<div class="highlight-python notranslate"><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">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</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 a tensor with shape (2,3), default CppCPU device and float32</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="n">x</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
+
+<span class="c1"># create a tensor from a numpy array</span>
+<span class="n">npy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</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">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">npy</span><span class="p">)</span>
+
+<span class="n">y</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="c1"># sample values from the uniform distribution</span>
+
+<span class="n">z</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">mult</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>  <span class="c1"># gemm -&gt; z of shape (2, 3)</span>
+
+<span class="n">x</span> <span class="o">+=</span> <span class="n">z</span>  <span class="c1"># element-wise addition</span>
+
+<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span>
+<span class="n">x</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"># move the data to a gpu device</span>
+
+<span class="n">r</span> <span class="o">=</span> <span class="n">tensor</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="n">s</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>  <span class="c1"># tensor -&gt; numpy array</span>
+</pre></div>
+</div>
+<p>有两种类型的tensor函数:</p>
+<p><strong>Tensor成员函数</strong></p>
+<p>将会改变Tensor实例的状态</p>
+<p><strong>Tensor模块化函数</strong></p>
+<p>接受Tensor实例作为自变量以及返回Tensor实例</p>
+<p>每个Tensor实例在读取数据前都必须做初始化</p>
+<hr class="docutils" />
+<div class="section" id="class-singa-tensor-tensor-shape-none-device-none-dtype-0">
+<span id="class-singa-tensor-tensor-shape-none-device-none-dtype-0"></span><h3>class singa.tensor.Tensor(shape=None, device=None, dtype=0)<a class="headerlink" href="#class-singa-tensor-tensor-shape-none-device-none-dtype-0" title="Permalink to this headline">¶</a></h3>
+<p>创建Py Tensor,封装了一个基于swig转换的CPP Tensor。
+三个参数分别是Tensor的三个属性。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>shape (list<int>)</strong> – 一个列表的整形数据作为Tensor的形状。如果shape没有指定,将会创建一个伪Tensor。</li>
+<li><strong>device</strong> – swig转化的使用设备模块化的Device实例。 如果为None,默认的CPU设备将会被使用。</li>
+<li><strong>dtype</strong> – 数据类型。 目前,大多数操作仅支持kFloat32。</li>
+</ul>
+<hr class="docutils" />
+<div class="section" id="t">
+<span id="t"></span><h4>T()<a class="headerlink" href="#t" title="Permalink to this headline">¶</a></h4>
+<p>浅拷贝。</p>
+<p><strong>返回值:</strong> 一个新Tensor,共享底层数据所占内存,但标记为该tensor的转置版本。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="add-column-v">
+<span id="add-column-v"></span><h4>add_column(v)<a class="headerlink" href="#add-column-v" title="Permalink to this headline">¶</a></h4>
+<p>对该Tensor每列加上一个tensor。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 被作为一列加到原tensor的Tensor</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="add-row-v">
+<span id="add-row-v"></span><h4>add_row(v)<a class="headerlink" href="#add-row-v" title="Permalink to this headline">¶</a></h4>
+<p>对该tensor每行加一个tensor。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 被作为行加到原tensor的Tensor</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="bernoulli-p">
+<span id="bernoulli-p"></span><h4>bernoulli(p)<a class="headerlink" href="#bernoulli-p" title="Permalink to this headline">¶</a></h4>
+<p>对每个元素,按照给定概率从0/1中取样。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>p (float)</strong> – 以概率p取样一个元素为1</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="clone">
+<span id="clone"></span><h4>clone()<a class="headerlink" href="#clone" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> 一个新Tensor,是待拷贝Tensor的深拷贝</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="copy">
+<span id="copy"></span><h4>copy()<a class="headerlink" href="#copy" title="Permalink to this headline">¶</a></h4>
+<p>调用singa::Tensor的拷贝构造器进行浅拷贝。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="copy-data-t">
+<span id="copy-data-t"></span><h4>copy_data(t)<a class="headerlink" href="#copy-data-t" title="Permalink to this headline">¶</a></h4>
+<p>从另一个Tensor实例拷贝数据。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 源Tensor</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="copy-from-numpy-np-array-offset-0">
+<span id="copy-from-numpy-np-array-offset-0"></span><h4>copy_from_numpy(np_array, offset=0)<a class="headerlink" href="#copy-from-numpy-np-array-offset-0" title="Permalink to this headline">¶</a></h4>
+<p>从numpy数组中拷贝数据。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>np_array</strong> – 源numpy数组</li>
+<li><strong>offset (int)</strong> – 目标偏移</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="deepcopy">
+<span id="deepcopy"></span><h4>deepcopy()<a class="headerlink" href="#deepcopy" title="Permalink to this headline">¶</a></h4>
+<p>同clone()</p>
+<p><strong>返回值:</strong> 新Tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="div-column-v">
+<span id="div-column-v"></span><h4>div_column(v)<a class="headerlink" href="#div-column-v" title="Permalink to this headline">¶</a></h4>
+<p>将Tensor每列除以v。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 1维tensor,和源tensor的列长相同</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="div-row-v">
+<span id="div-row-v"></span><h4>div_row(v)<a class="headerlink" href="#div-row-v" title="Permalink to this headline">¶</a></h4>
+<p>将Tensor每行除以v。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 1维tensor,和源tensor的行长相同</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="gaussian-mean-std">
+<span id="gaussian-mean-std"></span><h4>gaussian(mean, std)<a class="headerlink" href="#gaussian-mean-std" title="Permalink to this headline">¶</a></h4>
+<p>按照高斯分布对每个元素采样。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>mean (float)</strong> – 分布的均值</li>
+<li><strong>std (float)</strong> – 分布的标准差</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="is-empty">
+<span id="is-empty"></span><h4>is_empty()<a class="headerlink" href="#is-empty" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> 根据tensor的形状,如果是空的返回True</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="is-transpose">
+<span id="is-transpose"></span><h4>is_transpose()<a class="headerlink" href="#is-transpose" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> 如果内部数据被转置则返回True,否则返回False</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="l1">
+<span id="l1"></span><h4>l1()<a class="headerlink" href="#l1" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> L1 norm</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="l2">
+<span id="l2"></span><h4>l2()<a class="headerlink" href="#l2" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> L2 norm</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="memsize">
+<span id="memsize"></span><h4>memsize()<a class="headerlink" href="#memsize" title="Permalink to this headline">¶</a></h4>
+<ul class="simple">
+<li><strong>返回值:</strong> 被分配给该tensor的Bytes数</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="mult-column-v">
+<span id="mult-column-v"></span><h4>mult_column(v)<a class="headerlink" href="#mult-column-v" title="Permalink to this headline">¶</a></h4>
+<p>将tensor每列和v做元素级别乘法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 1维tensor,同源tensor列长等长</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="mult-row-v">
+<span id="mult-row-v"></span><h4>mult_row(v)<a class="headerlink" href="#mult-row-v" title="Permalink to this headline">¶</a></h4>
+<p>将tensor每行和v做元素级别乘法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>v (Tensor)</strong> – 1维tensor,同源tensor行长等长</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="ndim">
+<span id="ndim"></span><h4>ndim()<a class="headerlink" href="#ndim" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> tensor的维度</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="reset-like-t">
+<span id="reset-like-t"></span><h4>reset_like(t)<a class="headerlink" href="#reset-like-t" title="Permalink to this headline">¶</a></h4>
+<p>根据给定tensor重置源tensor形状,数据类型和设备。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 需要重置的tensor</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="set-value-x">
+<span id="set-value-x"></span><h4>set_value(x)<a class="headerlink" href="#set-value-x" title="Permalink to this headline">¶</a></h4>
+<p>设置所有元素值为给定值。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>x(float)</strong> - 待设定的值</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="size">
+<span id="size"></span><h4>size()<a class="headerlink" href="#size" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> tensor中的元素个数</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="to-device-device">
+<span id="to-device-device"></span><h4>to_device(device)<a class="headerlink" href="#to-device-device" title="Permalink to this headline">¶</a></h4>
+<p>将tensor中数据传到指定设备上。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>device</strong> - 从CudaGPU/CppCPU/OpenclGPU转换的swig设备</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="to-host">
+<span id="to-host"></span><h4>to_host()<a class="headerlink" href="#to-host" title="Permalink to this headline">¶</a></h4>
+<p>将tensor数据传到默认的CppCPU设备上。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="uniform-low-high">
+<span id="uniform-low-high"></span><h4>uniform(low, high)<a class="headerlink" href="#uniform-low-high" title="Permalink to this headline">¶</a></h4>
+<p>从均匀分布中进行采样。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>low (float)</strong> – 下界</li>
+<li><strong>high (float)</strong> – 上界</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-abs-t">
+<span id="singa-tensor-abs-t"></span><h4>singa.tensor.abs(t)<a class="headerlink" href="#singa-tensor-abs-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t(Tensor)</strong> - 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> 一个新tensor,其元素值为y=abs(x),x是t中的元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-add-lhs-rhs-ret-none">
+<span id="singa-tensor-add-lhs-rhs-ret-none"></span><h4>singa.tensor.add(lhs, rhs, ret=None)<a class="headerlink" href="#singa-tensor-add-lhs-rhs-ret-none" title="Permalink to this headline">¶</a></h4>
+<p>元素级别加法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>lhs (Tensor)</strong> – 左操作tensor</li>
+<li><strong>rhs (Tensor)</strong> – 右操作tensor</li>
+<li><strong>ret (Tensor, optional)</strong> – 如果不是空, 结果将被保存在其中;否则,一个新tensor会被创建以保存结果。</li>
+</ul>
+<p><strong>返回值:</strong> 新tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-add-column-alpha-v-beta-m">
+<span id="singa-tensor-add-column-alpha-v-beta-m"></span><h4>singa.tensor.add_column(alpha, v, beta, M)<a class="headerlink" href="#singa-tensor-add-column-alpha-v-beta-m" title="Permalink to this headline">¶</a></h4>
+<p>将v加到M的每个列向量, 定义M一列为m,m=alpha * v + beta * m</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>alpha (float)</strong> – v的系数</li>
+<li><strong>v (Tensor)</strong> – 1维tensor</li>
+<li><strong>beta (float)</strong> – M的系数</li>
+<li><strong>M (Tensor)</strong> – 2维tensor</li>
+</ul>
+<p><strong>返回值:</strong> M</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-add-row-alpha-v-beta-m">
+<span id="singa-tensor-add-row-alpha-v-beta-m"></span><h4>singa.tensor.add_row(alpha, v, beta, M)<a class="headerlink" href="#singa-tensor-add-row-alpha-v-beta-m" title="Permalink to this headline">¶</a></h4>
+<p>将v加到M的每个行向量, 定义M一行为m,m=alpha * v + beta * m。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>alpha (float)</strong> – v的系数</li>
+<li><strong>v (Tensor)</strong> – 1维tensor</li>
+<li><strong>beta (float)</strong> – M的系数</li>
+<li><strong>M (Tensor)</strong> – 2维tensor</li>
+</ul>
+<p><strong>返回值:</strong> M</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-average-t-axis-none">
+<span id="singa-tensor-average-t-axis-none"></span><h4>singa.tensor.average(t, axis=None)<a class="headerlink" href="#singa-tensor-average-t-axis-none" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入Tensor</li>
+<li><strong>axis (int, optional)</strong> – 如果为空,取所有元素的平均值;否则,取给定维度的元素平均值。0表示列均值,1表示行均值。</li>
+</ul>
+<p><strong>返回值:</strong> 如果axis是空,返回一个float值;否则,返回一个新tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-axpy-alpha-x-y">
+<span id="singa-tensor-axpy-alpha-x-y"></span><h4>singa.tensor.axpy(alpha, x, y)<a class="headerlink" href="#singa-tensor-axpy-alpha-x-y" title="Permalink to this headline">¶</a></h4>
+<p>元素级别操作 y += alpha * x。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>alpha (float)</strong> – x的系数</li>
+<li><strong>x (Tensor)</strong> – 被加的tensor</li>
+<li><strong>y (Tensor)</strong> – 原tensor</li>
+</ul>
+<p><strong>返回值:</strong> y</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-bernoulli-p-t">
+<span id="singa-tensor-bernoulli-p-t"></span><h4>singa.tensor.bernoulli(p, t)<a class="headerlink" href="#singa-tensor-bernoulli-p-t" title="Permalink to this headline">¶</a></h4>
+<p>对每个元素生成一个二进制位。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>p (float)</strong> – each element is 1 with probability p; and 0 with 1 - p</li>
+<li><strong>t (Tensor)</strong> – the results are put into t</li>
+</ul>
+<p><strong>返回值:</strong> t</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-copy-data-to-from-dst-src-size-dst-offset-0-src-offset-0">
+<span id="singa-tensor-copy-data-to-from-dst-src-size-dst-offset-0-src-offset-0"></span><h4>singa.tensor.copy_data_to_from(dst, src, size, dst_offset=0, src_offset=0)<a class="headerlink" href="#singa-tensor-copy-data-to-from-dst-src-size-dst-offset-0-src-offset-0" title="Permalink to this headline">¶</a></h4>
+<p>将数据从一个tensor实例拷贝到另一个tensor实例。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>dst (Tensor)</strong> – 目标Tensor</li>
+<li><strong>src (Tensor)</strong> – 源Tensor</li>
+<li><strong>size (int)</strong> – 拷贝元素数目</li>
+<li><strong>dst_offset (int)</strong> – 拷贝到dst元素在dst的起始偏移</li>
+<li><strong>src_offset (int)</strong> – 待拷贝的元素在src中的起始偏移</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-div-lhs-rhs-ret-none">
+<span id="singa-tensor-div-lhs-rhs-ret-none"></span><h4>singa.tensor.div(lhs, rhs, ret=None)<a class="headerlink" href="#singa-tensor-div-lhs-rhs-ret-none" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的除法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>lhs (Tensor)</strong> – 左操作tensor</li>
+<li><strong>rhs (Tensor)</strong> – 右操作tensor</li>
+<li><strong>ret (Tensor, optional)</strong> – 如果非空,将把结果写入;否则,创建一个新tensor并将结果写入</li>
+</ul>
+<p><strong>返回值:</strong> 存有运算结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-eltwise-mult-lhs-rhs-ret-none">
+<span id="singa-tensor-eltwise-mult-lhs-rhs-ret-none"></span><h4>singa.tensor.eltwise_mult(lhs, rhs, ret=None)<a class="headerlink" href="#singa-tensor-eltwise-mult-lhs-rhs-ret-none" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的乘法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>lhs (Tensor)</strong> – 左操作tensor</li>
+<li><strong>rhs (Tensor)</strong> – 右操作tensor</li>
+<li><strong>ret (Tensor, optional)</strong> – 如果非空,将把结果写入;否则,创建一个新tensor并将结果写入</li>
+</ul>
+<p><strong>返回值:</strong> 保存运算结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-exp-t">
+<span id="singa-tensor-exp-t"></span><h4>singa.tensor.exp(t)<a class="headerlink" href="#singa-tensor-exp-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> 新tensor,其中元素为 y = exp(x),x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-from-numpy-np-array">
+<span id="singa-tensor-from-numpy-np-array"></span><h4>singa.tensor.from_numpy(np_array)<a class="headerlink" href="#singa-tensor-from-numpy-np-array" title="Permalink to this headline">¶</a></h4>
+<p>根据numpy数组的形状、数据类型和数值创建一个tensor。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>np_array</strong> – numpy数组</li>
+</ul>
+<p><strong>返回值:</strong> 分配在默认CppCPU设备上的tensor实例</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-gaussian-mean-std-t">
+<span id="singa-tensor-gaussian-mean-std-t"></span><h4>singa.tensor.gaussian(mean, std, t)<a class="headerlink" href="#singa-tensor-gaussian-mean-std-t" title="Permalink to this headline">¶</a></h4>
+<p>按照给定高斯分布生成数值。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>mean (float)</strong> – 高斯分布的均值</li>
+<li><strong>std (float)</strong> – 高斯分布的标准差</li>
+<li><strong>t (Tensor)</strong> – 结果被存入t</li>
+</ul>
+<p><strong>返回值:</strong> t</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-ge-t-x">
+<span id="singa-tensor-ge-t-x"></span><h4>singa.tensor.ge(t, x)<a class="headerlink" href="#singa-tensor-ge-t-x" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的比较,t &gt;= x。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 左操作数</li>
+<li><strong>x (Tensor or float)</strong> – 右操作数</li>
+</ul>
+<p><strong>返回值:</strong> 0.0f 或 t[i] &gt;= x[i] ? 1.0f:0.0f</p>
+<p><strong>返回值类型:</strong> tensor,每个元素为 t[i] &gt;= x ? 1.0f</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-gt-t-x">
+<span id="singa-tensor-gt-t-x"></span><h4>singa.tensor.gt(t, x)<a class="headerlink" href="#singa-tensor-gt-t-x" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的比较,t &gt; x。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 左操作tensor</li>
+<li><strong>x (Tensor or float)</strong> – 右操作tensor或数</li>
+</ul>
+<p><strong>返回值:</strong> 0.0f 或 t[i] &gt; x[i] ? 1.0f:0.0f</p>
+<p><strong>返回值类型:</strong> tensor,每个元素为 t[i] &gt; x ? 1.0f</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-le-t-x">
+<span id="singa-tensor-le-t-x"></span><h4>singa.tensor.le(t, x)<a class="headerlink" href="#singa-tensor-le-t-x" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的比较,t &lt;= x。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 左操作tensor</li>
+<li><strong>x (Tensor or float)</strong> – 右操作tensor或数</li>
+</ul>
+<p><strong>返回值:</strong> 0.0f 或 t[i] &lt;= x[i] ? 1.0f:0.0f</p>
+<p><strong>返回值类型:</strong> tensor,每个元素为 t[i] &lt;= x ? 1.0f</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-lt-t-x">
+<span id="singa-tensor-lt-t-x"></span><h4>singa.tensor.lt(t, x)<a class="headerlink" href="#singa-tensor-lt-t-x" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的比较,t &lt; x。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 左操作tensor</li>
+<li><strong>x (Tensor or float)</strong> – 右操作tensor或数</li>
+</ul>
+<p><strong>返回值:</strong> 0.0f 或 t[i] &lt; x[i] ? 1.0f:0.0f</p>
+<p><strong>返回值类型:</strong> tensor,每个元素为 t[i] &lt; x ? 1.0f</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-log-t">
+<span id="singa-tensor-log-t"></span><h4>singa.tensor.log(t)<a class="headerlink" href="#singa-tensor-log-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> 一个新tensor,其元素值为y = log(x),x是t中的元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-mult-a-b-c-none-alpha-1-0-beta-0-0">
+<span id="singa-tensor-mult-a-b-c-none-alpha-1-0-beta-0-0"></span><h4>singa.tensor.mult(A, B, C=None, alpha=1.0, beta=0.0)<a class="headerlink" href="#singa-tensor-mult-a-b-c-none-alpha-1-0-beta-0-0" title="Permalink to this headline">¶</a></h4>
+<p>矩阵-矩阵或矩阵-向量乘法, 函数返回 C = alpha * A * B + beta * C。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>A (Tensor)</strong> – 2维Tensor</li>
+<li><strong>B (Tensor)</strong> – 如果B是1维Tensor, 将调用GEMV做矩阵-向量乘法;否则将调用GEMM。</li>
+<li><strong>C (Tensor, optional)</strong> – 存储结果;如果为空,将创建新tensor存储结果。</li>
+<li><strong>alpha (float)</strong> – A * B 的系数</li>
+<li><strong>beta (float)</strong> – C 的系数</li>
+</ul>
+<p><strong>返回值:</strong> 保存运算结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-pow-t-x-out-none">
+<span id="singa-tensor-pow-t-x-out-none"></span><h4>singa.tensor.pow(t, x, out=None)<a class="headerlink" href="#singa-tensor-pow-t-x-out-none" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+<li><strong>x (float or Tensor)</strong> – 如果x是浮点数 y[i] = t[i]^x; 否则 y[i]= t[i]^x[i]</li>
+<li><strong>out (None or Tensor)</strong> – 如果非空,将存入结果;否则,将创建一个新tensor保存结果。</li>
+</ul>
+<p><strong>返回值:</strong> 保存运算结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-relu-t">
+<span id="singa-tensor-relu-t"></span><h4>singa.tensor.relu(t)<a class="headerlink" href="#singa-tensor-relu-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> tensor,其中元素为 y = x 若x &gt;0;否则y = 0,x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-reshape-t-s">
+<span id="singa-tensor-reshape-t-s"></span><h4>singa.tensor.reshape(t, s)<a class="headerlink" href="#singa-tensor-reshape-t-s" title="Permalink to this headline">¶</a></h4>
+<p>改变tensor的形状。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 待改变形状的tensor</li>
+<li><strong>s (list<int>)</strong> – 新形状,体积和原tensor体积相同</li>
+</ul>
+<p><strong>返回值:</strong> 新tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sigmoid-t">
+<span id="singa-tensor-sigmoid-t"></span><h4>singa.tensor.sigmoid(t)<a class="headerlink" href="#singa-tensor-sigmoid-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> tensor,其中元素为 y = sigmoid(x),x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sign-t">
+<span id="singa-tensor-sign-t"></span><h4>singa.tensor.sign(t)<a class="headerlink" href="#singa-tensor-sign-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> tensor,其中元素为 y = sign(x),x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sizeof-dtype">
+<span id="singa-tensor-sizeof-dtype"></span><h4>singa.tensor.sizeof(dtype)<a class="headerlink" href="#singa-tensor-sizeof-dtype" title="Permalink to this headline">¶</a></h4>
+<p><strong>返回值:</strong> 依据core.proto中定义的SINGA数据类型,返回给定类型所占Byte数目</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-softmax-t-out-none">
+<span id="singa-tensor-softmax-t-out-none"></span><h4>singa.tensor.softmax(t, out=None)<a class="headerlink" href="#singa-tensor-softmax-t-out-none" title="Permalink to this headline">¶</a></h4>
+<p>对tensor每行做SoftMax。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入1维或2维tensor</li>
+<li><strong>out (Tensor, 可选)</strong> – 如果非空,将存入结果</li>
+</ul>
+<p><strong>返回值:</strong> 保存操作结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sqrt-t">
+<span id="singa-tensor-sqrt-t"></span><h4>singa.tensor.sqrt(t)<a class="headerlink" href="#singa-tensor-sqrt-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> tensor,其中元素为 y = sqrt(x),x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-square-t">
+<span id="singa-tensor-square-t"></span><h4>singa.tensor.square(t)<a class="headerlink" href="#singa-tensor-square-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> tensor,其中元素为 y = x * x,x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sub-lhs-rhs-ret-none">
+<span id="singa-tensor-sub-lhs-rhs-ret-none"></span><h4>singa.tensor.sub(lhs, rhs, ret=None)<a class="headerlink" href="#singa-tensor-sub-lhs-rhs-ret-none" title="Permalink to this headline">¶</a></h4>
+<p>元素级别的减法。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>lhs (Tensor)</strong> – 左操作tensor</li>
+<li><strong>rhs (Tensor)</strong> – 右操作tensor</li>
+<li><strong>ret (Tensor, 可选)</strong> – 如果非空,将存入结果;否则,将创建一个新tensor保存</li>
+</ul>
+<p><strong>返回值:</strong> 存放结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sum-t-axis-none">
+<span id="singa-tensor-sum-t-axis-none"></span><h4>singa.tensor.sum(t, axis=None)<a class="headerlink" href="#singa-tensor-sum-t-axis-none" title="Permalink to this headline">¶</a></h4>
+<p>在给定的维度上求和。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入Tensor</li>
+<li><strong>axis (int, 可选)</strong> – 如果为空,将对所有元素求和;如果给定数值,将沿给定维度求和,比如:0 - 按列求和;1 - 按行求和。</li>
+</ul>
+<p><strong>返回值:</strong> 如果是对整体求和,返回一个浮点数;否则返回tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sum-columns-m">
+<span id="singa-tensor-sum-columns-m"></span><h4>singa.tensor.sum_columns(M)<a class="headerlink" href="#singa-tensor-sum-columns-m" title="Permalink to this headline">¶</a></h4>
+<p>按列求和。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>M (Tensor)</strong> – 输入的2维tensor</li>
+</ul>
+<p><strong>返回值:</strong> 产生求和结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-sum-rows-m">
+<span id="singa-tensor-sum-rows-m"></span><h4>singa.tensor.sum_rows(M)<a class="headerlink" href="#singa-tensor-sum-rows-m" title="Permalink to this headline">¶</a></h4>
+<p>按行求和。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>M (Tensor)</strong> – 输入的2维tensor</li>
+</ul>
+<p><strong>返回值:</strong> 产生求和结果的tensor</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-tanh-t">
+<span id="singa-tensor-tanh-t"></span><h4>singa.tensor.tanh(t)<a class="headerlink" href="#singa-tensor-tanh-t" title="Permalink to this headline">¶</a></h4>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong>tensor,其中元素为 y = tanh(x),x为t中元素</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-to-host-t">
+<span id="singa-tensor-to-host-t"></span><h4>singa.tensor.to_host(t)<a class="headerlink" href="#singa-tensor-to-host-t" title="Permalink to this headline">¶</a></h4>
+<p>将数据拷贝到host设备上。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-to-numpy-t">
+<span id="singa-tensor-to-numpy-t"></span><h4>singa.tensor.to_numpy(t)<a class="headerlink" href="#singa-tensor-to-numpy-t" title="Permalink to this headline">¶</a></h4>
+<p>拷贝tensor数据到numpy数组。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>t (Tensor)</strong> – 输入tensor</li>
+</ul>
+<p><strong>返回值:</strong> numpy数组</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-tensor-uniform-low-high-t">
+<span id="singa-tensor-uniform-low-high-t"></span><h4>singa.tensor.uniform(low, high, t)<a class="headerlink" href="#singa-tensor-uniform-low-high-t" title="Permalink to this headline">¶</a></h4>
+<p>按照均匀分布生成数值。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>low (float)</strong> – 下界</li>
+<li><strong>hight (float)</strong> – 上届</li>
+<li><strong>t (Tensor)</strong> – 结果存入t</li>
+</ul>
+<p><strong>返回值:</strong> t</p>
+<hr class="docutils" />
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Added: incubator/singa/site/trunk/zh/docs/utils.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/utils.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/utils.html (added)
+++ incubator/singa/site/trunk/zh/docs/utils.html Fri Jun  8 15:29:00 2018
@@ -0,0 +1,325 @@
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+           <div itemprop="articleBody">
+            
+  <div class="section" id="utils">
+<span id="utils"></span><h1>Utils<a class="headerlink" href="#utils" title="Permalink to this headline">¶</a></h1>
+<hr class="docutils" />
+<div class="section" id="singa-utils-update-progress-progress-info">
+<span id="singa-utils-update-progress-progress-info"></span><h2>singa.utils.update_progress(progress, info)<a class="headerlink" href="#singa-utils-update-progress-progress-info" title="Permalink to this headline">¶</a></h2>
+<p>显示完成进程条和用户信息。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>progress (float)</strong> – 完成度[0, 1],负数表示被中断,&gt;=1表示以完成</li>
+<li><strong>info (str)</strong> – 待展示信息的字符串</li>
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+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
+</div>
+
+ <a href="https://github.com/apache/incubator-singa">
+    <img style="position: absolute; top: 0; right: 0; border: 0; z-index: 10000;"
+        src="https://s3.amazonaws.com/github/ribbons/forkme_right_orange_ff7600.png"
+        alt="Fork me on GitHub">
+</a>
+
+ 
+
+
+</body>
+</html>
\ No newline at end of file

Modified: incubator/singa/site/trunk/zh/downloads.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/downloads.html?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
--- incubator/singa/site/trunk/zh/downloads.html (original)
+++ incubator/singa/site/trunk/zh/downloads.html Fri Jun  8 15:29:00 2018
@@ -30,9 +30,19 @@
   <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="开发时间表" href="develop/schedule.html" />
+    <link rel="next" title="文档" href="docs/index.html" />
     <link rel="prev" title="SINGA 中文文档" href="index.html" />
     <link href="_static/style.css" rel="stylesheet" type="text/css">
+    <!--link href="_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"-->
+	<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css" integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous">
+    <style>
+	.fa:hover {
+	    opacity: 0.7;
+	}
+	.fab:hover {
+	    opacity: 0.7;
+	}
+    </style>
 
 
   
@@ -91,6 +101,7 @@
             
               <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">下载 SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a></li>
 </ul>
 <p class="caption"><span class="caption-text">开发</span></p>
 <ul>
@@ -301,7 +312,7 @@ ASF.</p>
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="develop/schedule.html" class="btn btn-neutral float-right" title="开发时间表" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="docs/index.html" class="btn btn-neutral float-right" title="文档" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="index.html" class="btn btn-neutral" title="SINGA 中文文档" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
@@ -380,6 +391,15 @@ ASF.</p>
 
   </div>
   <a href="http://incubator.apache.org/"> <img src= "_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
 </div>
 
  <a href="https://github.com/apache/incubator-singa">

Modified: incubator/singa/site/trunk/zh/genindex.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/genindex.html?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
--- incubator/singa/site/trunk/zh/genindex.html (original)
+++ incubator/singa/site/trunk/zh/genindex.html Fri Jun  8 15:29:00 2018
@@ -32,6 +32,16 @@
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" />
     <link href="_static/style.css" rel="stylesheet" type="text/css">
+    <!--link href="_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"-->
+	<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css" integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous">
+    <style>
+	.fa:hover {
+	    opacity: 0.7;
+	}
+	.fab:hover {
+	    opacity: 0.7;
+	}
+    </style>
 
 
   
@@ -90,6 +100,7 @@
             
               <ul>
 <li class="toctree-l1"><a class="reference internal" href="downloads.html">下载 SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a></li>
 </ul>
 <p class="caption"><span class="caption-text">开发</span></p>
 <ul>
@@ -250,6 +261,15 @@
 
   </div>
   <a href="http://incubator.apache.org/"> <img src= "_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
 </div>
 
  <a href="https://github.com/apache/incubator-singa">

Modified: incubator/singa/site/trunk/zh/index.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/index.html?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
--- incubator/singa/site/trunk/zh/index.html (original)
+++ incubator/singa/site/trunk/zh/index.html Fri Jun  8 15:29:00 2018
@@ -32,6 +32,16 @@
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="下载 SINGA" href="downloads.html" />
     <link href="_static/style.css" rel="stylesheet" type="text/css">
+    <!--link href="_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"-->
+	<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css" integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous">
+    <style>
+	.fa:hover {
+	    opacity: 0.7;
+	}
+	.fab:hover {
+	    opacity: 0.7;
+	}
+    </style>
 
 
   
@@ -90,6 +100,7 @@
             
               <ul>
 <li class="toctree-l1"><a class="reference internal" href="downloads.html">下载 SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a></li>
 </ul>
 <p class="caption"><span class="caption-text">开发</span></p>
 <ul>
@@ -347,6 +358,15 @@
 
   </div>
   <a href="http://incubator.apache.org/"> <img src= "_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
 </div>
 
  <a href="https://github.com/apache/incubator-singa">

Modified: incubator/singa/site/trunk/zh/objects.inv
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/objects.inv?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
Binary files - no diff available.

Modified: incubator/singa/site/trunk/zh/search.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/search.html?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
--- incubator/singa/site/trunk/zh/search.html (original)
+++ incubator/singa/site/trunk/zh/search.html Fri Jun  8 15:29:00 2018
@@ -31,6 +31,16 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="#" />
     <link href="_static/style.css" rel="stylesheet" type="text/css">
+    <!--link href="_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"-->
+	<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css" integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous">
+    <style>
+	.fa:hover {
+	    opacity: 0.7;
+	}
+	.fab:hover {
+	    opacity: 0.7;
+	}
+    </style>
 
 
   
@@ -89,6 +99,7 @@
             
               <ul>
 <li class="toctree-l1"><a class="reference internal" href="downloads.html">下载 SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a></li>
 </ul>
 <p class="caption"><span class="caption-text">开发</span></p>
 <ul>
@@ -262,6 +273,15 @@
 
   </div>
   <a href="http://incubator.apache.org/"> <img src= "_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
 </div>
 
  <a href="https://github.com/apache/incubator-singa">



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