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From wang...@apache.org
Subject svn commit: r1833193 [13/17] - 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/metric.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/metric.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/metric.html (added)
+++ incubator/singa/site/trunk/zh/docs/metric.html Fri Jun  8 15:29:00 2018
@@ -0,0 +1,369 @@
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">度量(Metric)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-metric-metric">class singa.metric.Metric</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#forward-x-y">forward(x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#evaluate-x-y">evaluate(x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-metric-accuracy">class singa.metric.Accuracy</a></li>
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+      <li>度量(Metric)</li>
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+  <div class="section" id="metric">
+<span id="metric"></span><h1>度量(Metric)<a class="headerlink" href="#metric" title="Permalink to this headline">¶</a></h1>
+<p>该模块包含一组用于评估模型性能的度量类。 特定的度量类可以由C++的实现转换或直接使用Python实现。</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">tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</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">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># randomly genearte the prediction activation</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>  <span class="c1"># normalize the prediction into probabilities</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span>  <span class="c1"># set the truth</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span>
+<span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</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"># averaged accuracy over all 3 samples in x</span>
+</pre></div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-metric-metric">
+<span id="class-singa-metric-metric"></span><h2>class singa.metric.Metric<a class="headerlink" href="#class-singa-metric-metric" title="Permalink to this headline">¶</a></h2>
+<p>基类:<code class="docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>度量类的基类</p>
+<p>封装C++度量类的子类可以使用继承的前向函数,并评估此基类的函数。 其他子类需要重写这些函数。 用户需要提供预测值和真实值来获取度量值。</p>
+<hr class="docutils" />
+<div class="section" id="forward-x-y">
+<span id="forward-x-y"></span><h3>forward(x, y)<a class="headerlink" href="#forward-x-y" title="Permalink to this headline">¶</a></h3>
+<p>为每个样本计算度量值</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>x (Tensor)</strong> – 预测值,每行代表一个样本的预测值</li>
+<li><strong>y (Tensor)</strong> – 真实值,每行代表一个样本的真实值</li>
+</ul>
+<p><strong>返回值:</strong> 浮点数组成的tensor,每个样本对应一个浮点数输出</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="evaluate-x-y">
+<span id="evaluate-x-y"></span><h3>evaluate(x, y)<a class="headerlink" href="#evaluate-x-y" title="Permalink to this headline">¶</a></h3>
+<p>计算样本的平均度量值</p>
+<p><strong>参数:</strong></p>
+<ul class="simple">
+<li><strong>x (Tensor)</strong> – 预测值,每列代表一个样本的预测值</li>
+<li><strong>y (Tensor)</strong> – 真实值,每列代表一个样本的真实值</li>
+</ul>
+<p><strong>返回值:</strong> 浮点数组成的tensor,每个样本对应一个浮点数输出</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-metric-accuracy">
+<span id="class-singa-metric-accuracy"></span><h2>class singa.metric.Accuracy<a class="headerlink" href="#class-singa-metric-accuracy" title="Permalink to this headline">¶</a></h2>
+<p>基类:<code class="docutils literal notranslate"><span class="pre">singa.metric.Metric</span></code></p>
+<p>对于单标签预测任务,计算top-1精确度。它调用C++函数实现计算。</p>
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Added: incubator/singa/site/trunk/zh/docs/model_zoo/char-rnn/README.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/model_zoo/char-rnn/README.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/model_zoo/char-rnn/README.html (added)
+++ incubator/singa/site/trunk/zh/docs/model_zoo/char-rnn/README.html Fri Jun  8 15:29:00 2018
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">在文本上训练Char-RNN</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#">操作说明</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">在ImageNet上训练AlexNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">用GoogleNet做图像分类</a></li>
+</ul>
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+<p class="caption"><span class="caption-text">开发</span></p>
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+          <li><a href="../index.html">模型库</a> &raquo;</li>
+        
+      <li>在文本上训练Char-RNN</li>
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+  <div class="section" id="char-rnn">
+<span id="char-rnn"></span><h1>在文本上训练Char-RNN<a class="headerlink" href="#char-rnn" title="Permalink to this headline">¶</a></h1>
+<p>递归神经网络(RNN)被广泛用于建模顺序数据,例如自然语言句子。 本示例介绍如何使用SINGA的RNN层实现RNN应用程序(或模型)。 我们将使用<a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a>模型作为示例,它将训练语句或源代码,并将每个字符作为输入单位。 特别是,我们将使用GRU在Linux内核源代码上训练一个RNN。 经过训练,我们希望从模型中生成有意义的代码。</p>
+<div class="section" id="">
+<span id="id1"></span><h2>操作说明<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">编译并安装SINGA。目前,RNN的实现是基于CuDNN(&gt;=5.05)。.</p>
+</li>
+<li><p class="first">准备数据集。下载<a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">内核源代码</a>。其他文本数据也可被使用。</p>
+</li>
+<li><p class="first">开始训练,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>  <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">linux_input</span><span class="o">.</span><span class="n">txt</span>
+</pre></div>
+</div>
+<p>一些超参数可以在命令行参数中设置,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>  <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">h</span>
+</pre></div>
+</div>
+</li>
+<li><p class="first">通过提供要采样的字符数和种子字符串来从模型中采样字符。</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>  <span class="n">python</span> <span class="n">sample</span><span class="o">.</span><span class="n">py</span> <span class="s1">&#39;model.bin&#39;</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">&#39;#include &lt;std&#39;</span>
+</pre></div>
+</div>
+<p>请用其中一个checkpoint路径替换’model.bin’的路径。</p>
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+  <div class="section" id="cifar-10cnn">
+<span id="cifar-10cnn"></span><h1>在Cifar-10上训练CNN<a class="headerlink" href="#cifar-10cnn" title="Permalink to this headline">¶</a></h1>
+<p>卷积神经网络(CNN)是一种被广泛用于图像和视频分类的前馈人造神经网络。在此例子中,我们将在Cifar-10数据集上训练三个深度CNN模型来进行图像分类,</p>
+<ol class="simple">
+<li><a class="reference external" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg">AlexNet</a>,我们在验证集上能达到的最高准确度(不做数据增强)在82%左右。</li>
+<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>,我们在验证集上能达到的最高准确度(不做数据增强)在89%左右。</li>
+<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch">ResNet</a>,我们在验证集上能达到的最高准确度(不做数据增强)在83%左右。</li>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">来自Caffe的AlexNet</a>,SINGA能够无缝转换Caffe模型。</li>
+</ol>
+<div class="section" id="">
+<span id="id1"></span><h2>操作说明<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa">
+<span id="singa"></span><h3>SINGA安装<a class="headerlink" href="#singa" title="Permalink to this headline">¶</a></h3>
+<p>用户可以从源码编译和安装C++或者Python版本的SINGA。代码可以在CPU和GPU上执行。对于GPU上做训练,CUDA和CUDNN(V4或V5)是需要的。请参考安装界面以获得详细指示。</p>
+</div>
+<div class="section" id="">
+<span id="id2"></span><h3>数据准备<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<p>Cifar-10的二进制数据集文件可以由如下指令下载</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="nb">bin</span>
+</pre></div>
+</div>
+<p>Python版本可以由如下指令下载</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="n">py</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="">
+<span id="id3"></span><h3>训练<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<p>这里有4个训练程序</p>
+<p>1.train.py。下面的指令将会用’cifar-10-batches-py’底下的python版本的Cifar-10数据集训练VGG模型。</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>要训练其他模型,请用’alexnet’,’resnet’或’caffe’替换’vgg’。其中,’caffe’指由caffe的AlexNet转换的模型。默认情况下,模型将在CudaGPU设备上训练;若想要在CppCPU上运行,需添加额外的参数</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span>  <span class="o">--</span><span class="n">use_cpu</span>
+</pre></div>
+</div>
+<p>2.alexnet.cc。它通过调用CPP API在CudaGPU上训练AlexNet模型。</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="o">./</span><span class="n">run</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>3.alexnet-parallel.cc。它通过调用CPP API在两个CudaGPU上训练AlexNet模型。两个设备上同时运行并计算模型参数的梯度,然后它们会在CPU设备上取平均并用于更新参数。</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="o">./</span><span class="n">run</span><span class="o">-</span><span class="n">parallel</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>4.vgg-parallel.cc。它调用CPP API在两个CudaGPU设备上训练VGG模型,同alexnet-parallel.cc类似。</p>
+</div>
+<div class="section" id="">
+<span id="id4"></span><h3>预测<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<p>predict.py包含预测函数</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">,</span> <span class="n">dev</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>net通过加载先前训练好的模型来被创建;images包含图像的numpy数组(每张图像一行);dev是训练时的设备,例如,一个CudaGPU设备或者CppCPU设备;它将返回每个实例的topk标签。</p>
+<p>Predict.py文件主函数提供了用预训练的模型为新图片做预测的例子。’mode.bin’文件由训练程序生成,需要被放置在cifar目录下执行</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
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+  <div class="section" id="imagenetalexnet">
+<span id="imagenetalexnet"></span><h1>在ImageNet上训练AlexNet<a class="headerlink" href="#imagenetalexnet" title="Permalink to this headline">¶</a></h1>
+<p>卷积神经网络(CNN)是一种广泛用于图像和视频分类的前馈神经网络。 在这个例子中,我们将使用<a class="reference external" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">深度CNN模型</a>来对ImageNet数据集进行图像分类。</p>
+<div class="section" id="">
+<span id="id1"></span><h2>操作说明<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa">
+<span id="singa"></span><h3>编译SINGA<a class="headerlink" href="#singa" title="Permalink to this headline">¶</a></h3>
+<p>请用CUDA,CUDNN和OpenCV编译SINGA。 您可以手动打开CMakeLists.txt中的选项或在build /文件夹中运行<code class="docutils literal notranslate"><span class="pre">ccmake</span> <span class="pre">..</span></code>进行配置。</p>
+<p>我们已经在CuDNN V4和V5(V5需要Cuda7.5)上进行了测试。</p>
+</div>
+<div class="section" id="">
+<span id="id2"></span><h3>数据下载<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>请参考创建<a class="reference external" href="https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data">ImageNet 2012数据集</a>的步骤1-3下载和加压数据。</li>
+<li>你可以通过<a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh">get_ilsvrc_aux.sh</a>或从<a class="reference external" href="http://www.image-net.org/download-images">ImageNet</a>下载训练和验证集。</li>
+</ul>
+</div>
+<div class="section" id="">
+<span id="id3"></span><h3>数据预处理<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">假设你已经下载了数据和描述文件。 现在我们应该将数据转换为二进制文件。你可以运行:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">sh</span> <span class="n">create_data</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+<p>这个脚本会在指定输出目录下生成一个测试文件(<code class="docutils literal notranslate"><span class="pre">test.bin</span></code>),均值文件(<code class="docutils literal notranslate"><span class="pre">mean.bin</span></code>)和一些训练文件(<code class="docutils literal notranslate"><span class="pre">trainX.bin</span></code>)。</p>
+</li>
+<li><p class="first">你也可以改变<code class="docutils literal notranslate"><span class="pre">create_data.sh</span></code>的参数。</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">-trainlist</span> <span class="pre">&lt;file&gt;</span></code>: 训练数据列表文件;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-trainfolder</span> <span class="pre">&lt;folder&gt;</span></code>: 训练图片所在文件夹;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-testlist</span> <span class="pre">&lt;file&gt;</span></code>: 测试数据列表文件;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-testfolder</span> <span class="pre">&lt;floder&gt;</span></code>: 测试图像所在文件夹;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-outdata</span> <span class="pre">&lt;folder&gt;</span></code>: 保存输出文件的文件夹,包括平均值,训练和测试文件。 该脚本将在指定的文件夹中生成这些文件;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-filesize</span> <span class="pre">&lt;int&gt;</span></code>: 在每个二进制文件中存储的训练图片个数.</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="section" id="">
+<span id="id4"></span><h3>训练<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p class="first">准备好数据后,你可以运行下面指令来训练AlexNet模型。</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>    <span class="n">sh</span> <span class="n">run</span><span class="o">.</span><span class="n">sh</span>
+</pre></div>
+</div>
+</li>
+<li><p class="first">你可以改变<code class="docutils literal notranslate"><span class="pre">run.sh</span></code>的参数。</p>
+<ul class="simple">
+<li><code class="docutils literal notranslate"><span class="pre">-epoch</span> <span class="pre">&lt;int&gt;</span></code>: 要训练的epoch数目,默认为90;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-lr</span> <span class="pre">&lt;float&gt;</span></code>: 基础学习率,学习率将减少每20个时期,更具体地,lr = lr * exp(0.1 *(epoch / 20));</li>
+<li><code class="docutils literal notranslate"><span class="pre">-batchsize</span> <span class="pre">&lt;int&gt;</span></code>: 批数目,它应该根据你的内存而改变;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-filesize</span> <span class="pre">&lt;int&gt;</span></code>: 存储在每个二进制文件中的训练图像的数量,与数据预处理中的文件大小相同;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-ntrain</span> <span class="pre">&lt;int&gt;</span></code>: 训练图片的数目;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-ntest</span> <span class="pre">&lt;int&gt;</span></code>: 测试图片的数目;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-data</span> <span class="pre">&lt;folder&gt;</span></code>: 存储二进制文件的文件夹,它恰好是数据预处理步骤中的输出文件夹;</li>
+<li><code class="docutils literal notranslate"><span class="pre">-pfreq</span> <span class="pre">&lt;int&gt;</span></code>: 打印当前模型状态(损失和准确度)的频率(以批数据为单位);</li>
+<li><code class="docutils literal notranslate"><span class="pre">-nthreads</span> <span class="pre">&lt;int&gt;</span></code>: 加载传给模型的数据所有的线程数。</li>
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