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From wang...@apache.org
Subject svn commit: r1833193 [9/17] - 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/comm...
Date Fri, 08 Jun 2018 15:29:02 GMT
Added: incubator/singa/site/trunk/zh/docs/image_tool.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/image_tool.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/image_tool.html (added)
+++ incubator/singa/site/trunk/zh/docs/image_tool.html Fri Jun  8 15:29:00 2018
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">图像工具</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-image-tool-imagetool">class singa.image_tool.ImageTool</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#color-cast-offset-20-inplace-true">color_cast(offset=20, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#crop3-patch-num-case-1-inplace-true">crop3(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#crop5-patch-num-case-1-inplace-true">crop5(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#crop8-patch-num-case-1-inplace-true">crop8(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#enhance-scale-0-2-inplace-true">enhance(scale=0.2, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#flip-num-case-1-inplace-true">flip(num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#num-augmentation">num_augmentation()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#random-crop-patch-inplace-true">random_crop(patch, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#resize-by-list-size-list-num-case-1-inplace-true">resize_by_list(size_list, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#resize-by-range-rng-inplace-true">resize_by_range(rng, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#rotate-by-list-angle-list-num-case-1-inplace-true">rotate_by_list(angle_list, num_case=1, inplace=True)</a></li>
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+      <li>图像工具</li>
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+  <div class="section" id="">
+<span id="id1"></span><h1>图像工具<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h1>
+<p>图像增强的模型。</p>
+<p>示例用法:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">image_tool</span>
+
+<span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span>
+<span class="n">imgs</span> <span class="o">=</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;input.png&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">resize_by_list</span><span class="p">([</span><span class="mi">112</span><span class="p">])</span><span class="o">.</span><span class="n">crop5</span><span class="p">((</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">enhance</span><span class="p">()</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">imgs</span><span class="p">):</span>
+    <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">.png&#39;</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span>
+</pre></div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-image-tool-imagetool">
+<span id="class-singa-image-tool-imagetool"></span><h2>class singa.image_tool.ImageTool<a class="headerlink" href="#class-singa-image-tool-imagetool" title="Permalink to this headline">¶</a></h2>
+<p>一个图像增强工具。 对于inplace = True的操作,返回的值是ImageTool实例,用于链接多个操作; 否则,将返回预处理过的图像。 对于具有可数预处理情况的操作,可以设置参数num_case来决定要应用的预处理情况的数量。 通常,训练阶段设置为1,测试阶段设置为最大。</p>
+<hr class="docutils" />
+<div class="section" id="color-cast-offset-20-inplace-true">
+<span id="color-cast-offset-20-inplace-true"></span><h3>color_cast(offset=20, inplace=True)<a class="headerlink" href="#color-cast-offset-20-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>对每个通道加上一个随机偏移值[-offset, offset]。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>offset</strong> – 偏移, &gt;0 and &lt;255</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="crop3-patch-num-case-1-inplace-true">
+<span id="crop3-patch-num-case-1-inplace-true"></span><h3>crop3(patch, num_case=1, inplace=True)<a class="headerlink" href="#crop3-patch-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>对给定位置,截取可能的最大方框并缩放到给定尺寸。 按照图像尺寸,截取位置可以是(左, 中, 右)或(上, 中, 下)之一。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>patch (tuple)</strong> – 输出图像的高和宽</li>
+<li><strong>num_case</strong> – 情况数目, 必须在[1,3]</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="crop5-patch-num-case-1-inplace-true">
+<span id="crop5-patch-num-case-1-inplace-true"></span><h3>crop5(patch, num_case=1, inplace=True)<a class="headerlink" href="#crop5-patch-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>截取位置可以是[左上, 左下, 右上, 右下, 中间]。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>patch (tuple)</strong> – 输出图像的高和宽</li>
+<li><strong>num_case</strong> – 情况数目, 必须在[1,5]</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="crop8-patch-num-case-1-inplace-true">
+<span id="crop8-patch-num-case-1-inplace-true"></span><h3>crop8(patch, num_case=1, inplace=True)<a class="headerlink" href="#crop8-patch-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>这是patch_5和patch_and_scale的并集。 你可以依照这个例子取任何情况的并集。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="enhance-scale-0-2-inplace-true">
+<span id="enhance-scale-0-2-inplace-true"></span><h3>enhance(scale=0.2, inplace=True)<a class="headerlink" href="#enhance-scale-0-2-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>对色度、对比度、亮度和锐度采用随机增强。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>scale (float)</strong> – 增强范围 [1-scale, 1+scale]</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="flip-num-case-1-inplace-true">
+<span id="flip-num-case-1-inplace-true"></span><h3>flip(num_case=1, inplace=True)<a class="headerlink" href="#flip-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>随机向左或向右翻转图像。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>num_case</strong> – 情况数目,必须是 {1,2}; 如果是2,则会返回原图以及翻转的图像。</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="num-augmentation">
+<span id="num-augmentation"></span><h3>num_augmentation()<a class="headerlink" href="#num-augmentation" title="Permalink to this headline">¶</a></h3>
+<p>返回每张图像被增强后的总数</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="random-crop-patch-inplace-true">
+<span id="random-crop-patch-inplace-true"></span><h3>random_crop(patch, inplace=True)<a class="headerlink" href="#random-crop-patch-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p>根据随机偏移截取指定大小的图像</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>patch (tuple)</strong> – 截取图像块的高和宽</li>
+<li><strong>inplace (Boolean)</strong> – 如果为真,直接用新图像块替换原始图像内容;否则返回新图像块。</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="resize-by-list-size-list-num-case-1-inplace-true">
+<span id="resize-by-list-size-list-num-case-1-inplace-true"></span><h3>resize_by_list(size_list, num_case=1, inplace=True)<a class="headerlink" href="#resize-by-list-size-list-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>num_case</strong> – 缩放操作数目, 必须不超过size_list的长度</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="resize-by-range-rng-inplace-true">
+<span id="resize-by-range-rng-inplace-true"></span><h3>resize_by_range(rng, inplace=True)<a class="headerlink" href="#resize-by-range-rng-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>rng</strong> – 元组 (起始值, 结束值), 包括起始值但不包括结束值</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="rotate-by-list-angle-list-num-case-1-inplace-true">
+<span id="rotate-by-list-angle-list-num-case-1-inplace-true"></span><h3>rotate_by_list(angle_list, num_case=1, inplace=True)<a class="headerlink" href="#rotate-by-list-angle-list-num-case-1-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>num_case</strong> – 旋转操作数目, 必须不超过angle_list的长度</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="rotate-by-range-rng-inplace-true">
+<span id="rotate-by-range-rng-inplace-true"></span><h3>rotate_by_range(rng, inplace=True)<a class="headerlink" href="#rotate-by-range-rng-inplace-true" title="Permalink to this headline">¶</a></h3>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>rng</strong> – 表示旋转角度范围的元组 (起始值, 结束值), 包括起始值但不包括结束值</li>
+<li><strong>inplace</strong> – 对原图对象操作或返回一张新图</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-color-cast-img-offset">
+<span id="singa-image-tool-color-cast-img-offset"></span><h3>singa.image_tool.color_cast(img, offset)<a class="headerlink" href="#singa-image-tool-color-cast-img-offset" title="Permalink to this headline">¶</a></h3>
+<p>对每个通道加上一个随机偏移值[-offset, offset]。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-crop-img-patch-position">
+<span id="singa-image-tool-crop-img-patch-position"></span><h3>singa.image_tool.crop(img, patch, position)<a class="headerlink" href="#singa-image-tool-crop-img-patch-position" title="Permalink to this headline">¶</a></h3>
+<p>截取给定位置和给定大小的图像块。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>patch (tuple)</strong> – 宽,高</li>
+<li><strong>position (list(str))</strong> – 左上,左下,右上,右下,中间</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-crop-and-resize-img-patch-position">
+<span id="singa-image-tool-crop-and-resize-img-patch-position"></span><h3>singa.image_tool.crop_and_resize(img, patch, position)<a class="headerlink" href="#singa-image-tool-crop-and-resize-img-patch-position" title="Permalink to this headline">¶</a></h3>
+<p>对给定位置,截取可能的最大方框并缩放到给定尺寸。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>patch (tuple)</strong> – 宽,高</li>
+<li><strong>position (list(str))</strong> – 左,中,右,上,中,下</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-enhance-img-scale">
+<span id="singa-image-tool-enhance-img-scale"></span><h3>singa.image_tool.enhance(img, scale)<a class="headerlink" href="#singa-image-tool-enhance-img-scale" title="Permalink to this headline">¶</a></h3>
+<p>对色度、对比度、亮度和锐度采用随机增强。</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>scale (float)</strong> – 增强范围 [1-scale, 1+scale]</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-load-img-path-grayscale-false">
+<span id="singa-image-tool-load-img-path-grayscale-false"></span><h3>singa.image_tool.load_img(path, grayscale=False)<a class="headerlink" href="#singa-image-tool-load-img-path-grayscale-false" title="Permalink to this headline">¶</a></h3>
+<p>从给定路径读取图像。</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-image-tool-resize-img-small-size">
+<span id="singa-image-tool-resize-img-small-size"></span><h3>singa.image_tool.resize(img, small_size)<a class="headerlink" href="#singa-image-tool-resize-img-small-size" title="Permalink to this headline">¶</a></h3>
+<p>缩放图像使得最短边达到给定大小。</p>
+<hr class="docutils" />
+</div>
+</div>
+</div>
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Added: incubator/singa/site/trunk/zh/docs/index.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/index.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/index.html (added)
+++ incubator/singa/site/trunk/zh/docs/index.html Fri Jun  8 15:29:00 2018
@@ -0,0 +1,672 @@
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+</ul>
+<p class="caption"><span class="caption-text">社区</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../community/source-repository.html">源代码库</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="device.html#singa-device-get-default-device">singa.device.get_default_device()</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tensor.html">张量(Tensor)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tensor.html#python-api">PYTHON API</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tensor.html#class-singa-tensor-tensor-shape-none-device-none-dtype-0">class singa.tensor.Tensor(shape=None, device=None, dtype=0)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#t">T()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#add-column-v">add_column(v)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#add-row-v">add_row(v)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#bernoulli-p">bernoulli(p)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#clone">clone()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#copy">copy()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#copy-data-t">copy_data(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#copy-from-numpy-np-array-offset-0">copy_from_numpy(np_array, offset=0)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#deepcopy">deepcopy()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#div-column-v">div_column(v)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#div-row-v">div_row(v)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#gaussian-mean-std">gaussian(mean, std)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#is-empty">is_empty()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#is-transpose">is_transpose()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#l1">l1()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#l2">l2()</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="tensor.html#ndim">ndim()</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="tensor.html#set-value-x">set_value(x)</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-add-lhs-rhs-ret-none">singa.tensor.add(lhs, rhs, ret=None)</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-average-t-axis-none">singa.tensor.average(t, axis=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-axpy-alpha-x-y">singa.tensor.axpy(alpha, x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-bernoulli-p-t">singa.tensor.bernoulli(p, t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-copy-data-to-from-dst-src-size-dst-offset-0-src-offset-0">singa.tensor.copy_data_to_from(dst, src, size, dst_offset=0, src_offset=0)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-div-lhs-rhs-ret-none">singa.tensor.div(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-eltwise-mult-lhs-rhs-ret-none">singa.tensor.eltwise_mult(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-exp-t">singa.tensor.exp(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-from-numpy-np-array">singa.tensor.from_numpy(np_array)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-gaussian-mean-std-t">singa.tensor.gaussian(mean, std, t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-ge-t-x">singa.tensor.ge(t, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-gt-t-x">singa.tensor.gt(t, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-le-t-x">singa.tensor.le(t, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-lt-t-x">singa.tensor.lt(t, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-log-t">singa.tensor.log(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-mult-a-b-c-none-alpha-1-0-beta-0-0">singa.tensor.mult(A, B, C=None, alpha=1.0, beta=0.0)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-pow-t-x-out-none">singa.tensor.pow(t, x, out=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-relu-t">singa.tensor.relu(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-reshape-t-s">singa.tensor.reshape(t, s)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sigmoid-t">singa.tensor.sigmoid(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sign-t">singa.tensor.sign(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sizeof-dtype">singa.tensor.sizeof(dtype)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-softmax-t-out-none">singa.tensor.softmax(t, out=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sqrt-t">singa.tensor.sqrt(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-square-t">singa.tensor.square(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sub-lhs-rhs-ret-none">singa.tensor.sub(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sum-t-axis-none">singa.tensor.sum(t, axis=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sum-columns-m">singa.tensor.sum_columns(M)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-sum-rows-m">singa.tensor.sum_rows(M)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-tanh-t">singa.tensor.tanh(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-to-host-t">singa.tensor.to_host(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-to-numpy-t">singa.tensor.to_numpy(t)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tensor.html#singa-tensor-uniform-low-high-t">singa.tensor.uniform(low, high, t)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="layer.html">层(Layer)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="layer.html#python-api">Python API</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#singa-layer-engine-cudnn">singa.layer.engine = ‘cudnn’</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-layer-name-conf-none-kwargs">class singa.layer.Layer(name, conf=None, **kwargs)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#setup-in-shapes">setup(in_shapes)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#caffe-layer">caffe_layer()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#param-names">param_names()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#param-values">param_values()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-flag-dy">backward(flag, dy)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#to-device-device">to_device(device)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-dummy-name-input-sample-shape-none">class singa.layer.Dummy(name, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-falg-dy">backward(falg, dy)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-conv2d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-data-format-nchw-use-bias-true-w-specs-none-b-specs-none-pad-none-input-sample-shape-none">class singa.layer.Conv2D(name, nb_kernels, kernel=3, stride=1, border_mode=’same’, cudnn_prefer=’fatest’, data_format=’NCHW’, use_bias=True, W_specs=None, b_specs=None, pad=None, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-conv1d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-use-bias-true-w-specs-init-xavier-b-specs-init-constant-value-0-pad-none-input-sample-shape-none">class singa.layer.Conv1D(name, nb_kernels, kernel=3, stride=1, border_mode=’same’, cudnn_prefer=’fatest’, use_bias=True, W_specs={‘init’: ‘Xavier’}, b_specs={‘init’: ‘Constant’, ‘value’: 0}, pad=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-pooling2d-name-mode-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.Pooling2D(name, mode, kernel=3, stride=2, border_mode=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-maxpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.MaxPooling2D(name, kernel=3, stride=2, border_mode=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-avgpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.AvgPooling2D(name, kernel=3, stride=2, border_mode=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-maxpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.MaxPooling1D(name, kernel=3, stride=2, border_mode=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-avgpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.AvgPooling1D(name, kernel=3, stride=2, border_mode=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-batchnormalization-name-momentum-0-9-beta-specs-none-gamma-specs-none-input-sample-shape-none">class singa.layer.BatchNormalization(name, momentum=0.9, beta_specs=None, gamma_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-lrn-name-size-5-alpha-1-beta-0-75-mode-cross-channel-k-1-input-sample-shape-none">class singa.layer.LRN(name, size=5, alpha=1, beta=0.75, mode=’cross_channel’, k=1, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-dense-name-num-output-use-bias-true-w-specs-none-b-specs-none-w-transpose-false-input-sample-shape-none">class singa.layer.Dense(name, num_output, use_bias=True, W_specs=None, b_specs=None, W_transpose=False, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-dropout-name-p-0-5-input-sample-shape-none">class singa.layer.Dropout(name, p=0.5, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-activation-name-mode-relu-input-sample-shape-none">class singa.layer.Activation(name, mode=’relu’, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-softmax-name-axis-1-input-sample-shape-none">class singa.layer.Softmax(name, axis=1, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-flatten-name-axis-1-input-sample-shape-none">class singa.layer.Flatten(name, axis=1, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-merge-name-input-sample-shape-none">class singa.layer.Merge(name, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#setup-in-shape">setup(in_shape)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-flag-grad">backward(flag, grad)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-split-name-num-output-input-sample-shape-none">class singa.layer.Split(name, num_output, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#setup-in-shape">setup(in_shape)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward">forward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward">backward()</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-concat-name-axis-input-sample-shapes-none">class singa.layer.Concat(name, axis, input_sample_shapes=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-flag-dy">backward(flag, dy)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-slice-name-axis-slice-point-input-sample-shape-none">class singa.layer.Slice(name, axis, slice_point, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-flag-grads">backward(flag, grads)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-rnn-name-hidden-size-rnn-mode-lstm-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.RNN(name, hidden_size, rnn_mode=’lstm’, dropout=0.0, num_stacks=1, input_mode=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#backward-flag-grad">backward(flag, grad)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-lstm-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.LSTM(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="layer.html#class-singa-layer-gru-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.GRU(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="layer.html#singa-layer-get-layer-list">singa.layer.get_layer_list()</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="net.html">前馈网络</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="net.html#class-singa-net-feedforwardnet-loss-none-metric-none">class singa.net.FeedForwardNet(loss=None, metric=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="net.html#to-device-dev">to_device(dev)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#add-lyr-src-none">add(lyr, src=None)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#param-values">param_values()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#param-specs">param_specs()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#param-names">param_names()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#train-x-y">train(x, y)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#evaluate-x-y">evaluate(x, y)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#predict-x">predict(x)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#topo-sort-layers-src-of-layer">topo_sort(layers, src_of_layer)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#forward-flag-x-output">forward(flag, x, output=[])</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#backward">backward()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#save-f-buffer-size-10-use-pickle-false">save(f, buffer_size=10, use_pickle=False)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="net.html#load-f-buffer-size-10-use-pickle-false">load(f, buffer_size=10, use_pickle=False)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="initializer.html">初始化器(Initializer)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="initializer.html#python-api">Python API</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="initializer.html#singa-initializer-uniform-t-fan-in-0-fan-out-0">singa.initializer.uniform(t, fan_in=0, fan_out=0)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="initializer.html#singa-initializer-gaussian-t-fan-in-0-fan-out-0">singa.initializer.gaussian(t, fan_in=0, fan_out=0)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="loss.html">损失(Loss)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="loss.html#class-singa-loss-loss">class singa.loss.Loss</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#backward">backward()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="loss.html#class-singa-loss-softmaxcrossentropy">class singa.loss.SoftmaxCrossEntropy</a></li>
+<li class="toctree-l2"><a class="reference internal" href="loss.html#class-singa-loss-squarederror">class singa.loss.SquaredError</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#backward">backward()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="loss.html#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="metric.html">度量(Metric)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="metric.html#class-singa-metric-metric">class singa.metric.Metric</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="metric.html#forward-x-y">forward(x, y)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="metric.html#evaluate-x-y">evaluate(x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="metric.html#class-singa-metric-accuracy">class singa.metric.Accuracy</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="optimizer.html">优化器(Optimizer)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-optimizer-lr-none-momentum-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Optimizer(lr=None, momentum=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#register-name-specs">register(name, specs)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-regularizer-constraint-epoch-value-grad-name-none-step-1">apply_regularizer_constraint(epoch, value, grad, name=None, step=-1)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-none-step-1">apply_with_lr(epoch, lr, grad, value, name=None, step=-1)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-grad-value-name-none-step-1">apply(epoch, grad, value, name=None, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-sgd-lr-none-momentum-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.SGD(lr=None, momentum=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-nesterov-lr-none-momentum-0-9-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Nesterov(lr=None, momentum=0.9, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-rmsprop-rho-0-9-epsilon-1e-08-lr-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.RMSProp(rho=0.9, epsilon=1e-08, lr=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-adagrad-epsilon-1e-08-lr-none-weight-decay-none-lr-gen-none-regularizer-none-constraint-none">class singa.optimizer.AdaGrad(epsilon=1e-08, lr=None, weight_decay=None, lr_gen=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-adam-beta-1-0-9-beta-2-0-999-epsilon-1e-08-lr-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-08, lr=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-regularizer">class singa.optimizer.Regularizer</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-cppregularizer-conf">class singa.optimizer.CppRegularizer(conf)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-l2regularizer-coefficient">class singa.optimizer.L2Regularizer(coefficient)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-constraint">class singa.optimizer.Constraint</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-cppconstraint-conf">class singa.optimizer.CppConstraint(conf)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html#class-singa-optimizer-l2constraint-threshold-none">class singa.optimizer.L2Constraint(threshold=None)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="data.html">数据(Data)</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="data.html#class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10">class singa.data.ImageBatchIter(img_list_file, batch_size, image_transform, shuffle=True, delimiter=’ ‘, image_folder=None, capacity=10)</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="image_tool.html">图像工具</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="image_tool.html#class-singa-image-tool-imagetool">class singa.image_tool.ImageTool</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#color-cast-offset-20-inplace-true">color_cast(offset=20, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#crop3-patch-num-case-1-inplace-true">crop3(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#crop5-patch-num-case-1-inplace-true">crop5(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#crop8-patch-num-case-1-inplace-true">crop8(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#enhance-scale-0-2-inplace-true">enhance(scale=0.2, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#flip-num-case-1-inplace-true">flip(num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#num-augmentation">num_augmentation()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#random-crop-patch-inplace-true">random_crop(patch, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#resize-by-list-size-list-num-case-1-inplace-true">resize_by_list(size_list, num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#resize-by-range-rng-inplace-true">resize_by_range(rng, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#rotate-by-list-angle-list-num-case-1-inplace-true">rotate_by_list(angle_list, num_case=1, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#rotate-by-range-rng-inplace-true">rotate_by_range(rng, inplace=True)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-color-cast-img-offset">singa.image_tool.color_cast(img, offset)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-crop-img-patch-position">singa.image_tool.crop(img, patch, position)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-crop-and-resize-img-patch-position">singa.image_tool.crop_and_resize(img, patch, position)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-enhance-img-scale">singa.image_tool.enhance(img, scale)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-load-img-path-grayscale-false">singa.image_tool.load_img(path, grayscale=False)</a></li>
+<li class="toctree-l3"><a class="reference internal" href="image_tool.html#singa-image-tool-resize-img-small-size">singa.image_tool.resize(img, small_size)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="snapshot.html">Snapshot</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="snapshot.html#class-singa-snapshot-snapshot-f-mode-buffer-size-10">class singa.snapshot.Snapshot(f, mode, buffer_size=10)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="snapshot.html#read">read()</a></li>
+<li class="toctree-l3"><a class="reference internal" href="snapshot.html#write-param-name-param-val">write(param_name, param_val)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="utils.html">Utils</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="utils.html#singa-utils-update-progress-progress-info">singa.utils.update_progress(progress, info)</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="model_zoo/index.html">模型库</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/cifar10/README.html">在Cifar-10上训练CNN</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="model_zoo/cifar10/README.html#">操作说明</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/cifar10/README.html#singa">SINGA安装</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/cifar10/README.html#">数据准备</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/cifar10/README.html#">训练</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/cifar10/README.html#">预测</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/char-rnn/README.html">在文本上训练Char-RNN</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="model_zoo/char-rnn/README.html#">操作说明</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html">在ImageNet上训练AlexNet</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html#">操作说明</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html#singa">编译SINGA</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html#">数据下载</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html#">数据预处理</a></li>
+<li class="toctree-l4"><a class="reference internal" href="model_zoo/imagenet/alexnet/README.html#">训练</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/imagenet/googlenet/README.html">用GoogleNet做图像分类</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="model_zoo/imagenet/googlenet/README.html#">操作说明</a></li>
+<li class="toctree-l3"><a class="reference internal" href="model_zoo/imagenet/googlenet/README.html#">详细信息</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</div>
+
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+           </div>
+           
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+          <footer>
+  
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="installation.html" class="btn btn-neutral float-right" title="安装" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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+  <span class="rst-current-version" data-toggle="rst-current-version">
+    <span class="fa fa-book"> incubator-singa </span>
+    v: latest
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+  <div class="rst-other-versions">
+      <dl>
+          <dt>Languages</dt>
+          <dd><a href="../../en/index.html">English</a></dd>
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+      </dl>
+      <dl>
+          <dt>Versions</dt>
+          <dd><a href="http://singa.apache.org/v0.3.0/">0.3</a></dd>
+          <dd><a href="http://singa.apache.org/v1.1.0/">1.1</a></dd>
+      </dl>
+
+  </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> 
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