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svn commit: r1857927 [17/25] - in /incubator/singa/site/trunk: ./ _sources/
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+ <li>Use parameters pre-trained from Caffe in SINGA</li>
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+ <!--
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
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+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+--><div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
+<h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
+<div class="section" id="run-this-example">
+<h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2>
+<p>You can run this example by simply executing <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
+The script does the following work.</p>
+<div class="section" id="obtain-the-caffe-model">
+<h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Download caffe model prototxt and parameter binary file.</li>
+<li>Currently we only support the latest caffe format, if your model is in
+previous version of caffe, please update it to current format.(This is
+supported by caffe)</li>
+<li>After updating, we can obtain two files, i.e., the prototxt and parameter
+binary file.</li>
+</ul>
+</div>
+<div class="section" id="prepare-test-images">
+<h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3>
+<p>A few sample images are downloaded into the <code class="docutils literal notranslate"><span class="pre">test</span></code> folder.</p>
+</div>
+<div class="section" id="predict">
+<h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="n">model_txt</span> <span class="n">model_bin</span> <span class="n">imgclass</span>
+</pre></div>
+</div>
+<p>where <code class="docutils literal notranslate"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:</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> <span class="n">vgg16</span><span class="o">.</span><span class="n">prototxt</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">caffemodel</span> <span class="n">synset_words</span><span class="o">.</span><span class="n">txt</span>
+</pre></div>
+</div>
+<p>Then you type in the image path, and the program would output the top-5 labels.</p>
+<p>More Caffe models would be tested soon.</p>
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Added: incubator/singa/site/trunk/docs/model_zoo/char-rnn/README.html
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--- incubator/singa/site/trunk/docs/model_zoo/char-rnn/README.html (added)
+++ incubator/singa/site/trunk/docs/model_zoo/char-rnn/README.html Sun Apr 21 22:05:49 2019
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+<li class="toctree-l3"><a class="reference internal" href="../mnist/README.html">Train a RBM model against MNIST dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html">name: DenseNet models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">name: GoogleNet on ImageNet
+SINGA version: 1.0.1
+SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
+license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html">name: Inception V4 on ImageNet
+SINGA version: 1.1.1
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+<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html">name: Resnets on ImageNet
+SINGA version: 1.1
+SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
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+SINGA version: 1.1.1
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+<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a></li>
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+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
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+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+--><div class="section" id="train-char-rnn-over-plain-text">
+<h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
+<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGAâs RNN layers.
+We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.</p>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p>
+</li>
+<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
+Other plain text files can also be used.</p>
+</li>
+<li><p class="first">Start the training,</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>Some hyper-parameters could be set through command line,</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">Sample characters from the model by providing the number of characters to sample and the seed string.</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">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">'#include <std'</span>
+</pre></div>
+</div>
+<p>Please replace âmodel.binâ with the path to one of the checkpoint paths.</p>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">Train CNN over Cifar-10</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#instructions">Instructions</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../char-rnn/README.html">Train Char-RNN over plain text</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../mnist/README.html">Train a RBM model against MNIST dataset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html">name: DenseNet models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">name: GoogleNet on ImageNet
+SINGA version: 1.0.1
+SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
+parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
+license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html">name: Inception V4 on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
+parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56
+license: https://github.com/tensorflow/models/tree/master/slim</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html">name: Resnets on ImageNet
+SINGA version: 1.1
+SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f
+parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
+license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html">name: VGG models on ImageNet
+SINGA version: 1.1.1
+SINGA commit:
+license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a></li>
+</ul>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../downloads.html">Download SINGA</a></li>
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+<p class="caption"><span class="caption-text">Development</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../develop/schedule.html">Development Schedule</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../develop/how-contribute.html">How to Contribute to SINGA</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../develop/how-to-release.html">How to prepare a release</a></li>
+</ul>
+<p class="caption"><span class="caption-text">Community</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../community/source-repository.html">Source Repository</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../community/team-list.html">The SINGA Team</a></li>
+</ul>
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+ <li>Train CNN over Cifar-10</li>
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+ <li class="wy-breadcrumbs-aside">
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+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
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+ http://www.apache.org/licenses/LICENSE-2.0
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+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+--><div class="section" id="train-cnn-over-cifar-10">
+<h1>Train CNN over Cifar-10<a class="headerlink" href="#train-cnn-over-cifar-10" title="Permalink to this headline">¶</a></h1>
+<p>Convolution neural network (CNN) is a type of feed-forward artificial neural
+network widely used for image and video classification. In this example, we
+will train three deep CNN models to do image classification for the CIFAR-10 dataset,</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>
+the best validation accuracy (without data augmentation) we achieved was about 82%.</li>
+<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>, the best validation accuracy (without data augmentation) we achieved was about 89%.</li>
+<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch">ResNet</a>, the best validation accuracy (without data augmentation) we achieved was about 83%.</li>
+<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li>
+</ol>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="singa-installation">
+<h3>SINGA installation<a class="headerlink" href="#singa-installation" title="Permalink to this headline">¶</a></h3>
+<p>Users can compile and install SINGA from source or install the Python version.
+The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5)
+are required. Please refer to the installation page for detailed instructions.</p>
+</div>
+<div class="section" id="data-preparation">
+<h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3>
+<p>The binary Cifar-10 dataset could be downloaded by</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>The Python version could be downloaded by</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="training">
+<h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3>
+<p>There are four training programs</p>
+<ol>
+<li><p class="first">train.py. The following command would train the VGG model using the python
+version of the Cifar-10 dataset in âcifar-10-batches-pyâ folder.</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>To train other models, please replace âvggâ to âalexnetâ, âresnetâ or âcaffeâ,
+where âcaffeâ refers to the alexnet model converted from Caffe. By default
+the training would run on a CudaGPU device, to run it on CppCPU, add an additional
+argument</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>
+</li>
+<li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</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>
+</li>
+<li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices.
+The two devices run synchronously to compute the gradients of the mode parameters, which are
+averaged on the host CPU device and then be applied to update the parameters.</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>
+</li>
+<li><p class="first">vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.</p>
+</li>
+</ol>
+</div>
+<div class="section" id="prediction">
+<h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3>
+<p>predict.py includes the prediction function</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>The net is created by loading the previously trained model; Images consist of
+a numpy array of images (one row per image); dev is the training device, e.g.,
+a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p>
+<p>The predict.py fileâs main function provides an example of using the pre-trained alexnet model to do prediction for new images.
+The âmodel.binâ file generated by the training program should be placed at the cifar10 folder to run</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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+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+--><div class="section" id="use-parameters-pre-trained-from-caffe-in-singa">
+<h1>Use parameters pre-trained from Caffe in SINGA<a class="headerlink" href="#use-parameters-pre-trained-from-caffe-in-singa" title="Permalink to this headline">¶</a></h1>
+<p>In this example, we use SINGA to load the VGG parameters trained by Caffe to do image classification.</p>
+<div class="section" id="run-this-example">
+<h2>Run this example<a class="headerlink" href="#run-this-example" title="Permalink to this headline">¶</a></h2>
+<p>You can run this example by simply executing <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg16</span></code> or <code class="docutils literal notranslate"><span class="pre">run.sh</span> <span class="pre">vgg19</span></code>
+The script does the following work.</p>
+<div class="section" id="obtain-the-caffe-model">
+<h3>Obtain the Caffe model<a class="headerlink" href="#obtain-the-caffe-model" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li>Download caffe model prototxt and parameter binary file.</li>
+<li>Currently we only support the latest caffe format, if your model is in
+previous version of caffe, please update it to current format.(This is
+supported by caffe)</li>
+<li>After updating, we can obtain two files, i.e., the prototxt and parameter
+binary file.</li>
+</ul>
+</div>
+<div class="section" id="prepare-test-images">
+<h3>Prepare test images<a class="headerlink" href="#prepare-test-images" title="Permalink to this headline">¶</a></h3>
+<p>A few sample images are downloaded into the <code class="docutils literal notranslate"><span class="pre">test</span></code> folder.</p>
+</div>
+<div class="section" id="predict">
+<h3>Predict<a class="headerlink" href="#predict" title="Permalink to this headline">¶</a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">predict.py</span></code> script creates the VGG model and read the parameters,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="n">model_txt</span> <span class="n">model_bin</span> <span class="n">imgclass</span>
+</pre></div>
+</div>
+<p>where <code class="docutils literal notranslate"><span class="pre">imgclass</span></code> refers to the synsets of imagenet dataset for vgg models.
+You can start the prediction program by executing the following command:</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> <span class="n">vgg16</span><span class="o">.</span><span class="n">prototxt</span> <span class="n">vgg16</span><span class="o">.</span><span class="n">caffemodel</span> <span class="n">synset_words</span><span class="o">.</span><span class="n">txt</span>
+</pre></div>
+</div>
+<p>Then you type in the image path, and the program would output the top-5 labels.</p>
+<p>More Caffe models would be tested soon.</p>
+</div>
+</div>
+</div>
+
+
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+
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+
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+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+--><div class="section" id="train-char-rnn-over-plain-text">
+<h1>Train Char-RNN over plain text<a class="headerlink" href="#train-char-rnn-over-plain-text" title="Permalink to this headline">¶</a></h1>
+<p>Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. This example describes how to implement a RNN
+application (or model) using SINGAâs RNN layers.
+We will use the <a class="reference external" href="https://github.com/karpathy/char-rnn">char-rnn</a> model as an
+example, which trains over sentences or
+source code, with each character as an input unit. Particularly, we will train
+a RNN using GRU over Linux kernel source code. After training, we expect to
+generate meaningful code from the model.</p>
+<div class="section" id="instructions">
+<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
+<ul>
+<li><p class="first">Compile and install SINGA. Currently the RNN implementation depends on Cudnn with version >= 5.05.</p>
+</li>
+<li><p class="first">Prepare the dataset. Download the <a class="reference external" href="http://cs.stanford.edu/people/karpathy/char-rnn/">kernel source code</a>.
+Other plain text files can also be used.</p>
+</li>
+<li><p class="first">Start the training,</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>Some hyper-parameters could be set through command line,</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">Sample characters from the model by providing the number of characters to sample and the seed string.</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">'model.bin'</span> <span class="mi">100</span> <span class="o">--</span><span class="n">seed</span> <span class="s1">'#include <std'</span>
+</pre></div>
+</div>
+<p>Please replace âmodel.binâ with the path to one of the checkpoint paths.</p>
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