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<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>
<li><p><a 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%.</p></li>
<li><p><a href="http://torch.ch/blog/2015/07/30/cifar.html">VGGNet</a>, the best validation
accuracy (without data augmentation) we achieved was about 89%.</p></li>
<li><p><a href="https://github.com/facebook/fb.resnet.torch">ResNet</a>, the best validation
accuracy (without data augmentation) we achieved was about 83%.</p></li>
<li><p><a href="https://github.com/BVLC/caffe/tree/master/examples/cifar10">Alexnet from Caffe</a>,
SINGA is able to convert model from Caffe seamlessly.</p></li>
</ol>
<h2><a class="anchor" aria-hidden="true" id="instructions"></a><a href="#instructions" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Instructions</h2>
<h3><a class="anchor" aria-hidden="true" id="singa-installation"></a><a href="#singa-installation" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>SINGA installation</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 <a href="/docs/installation">installation page</a> for
detailed instructions.</p>
<blockquote>
<p>Please <code>cd</code> to <code>singa/examples/cifar10/</code> for the following commands</p>
</blockquote>
<h3><a class="anchor" aria-hidden="true" id="data-preparation"></a><a href="#data-preparation" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Data preparation</h3>
<p>The binary Cifar-10 dataset could be downloaded by</p>
<pre><code class="hljs css language-shell">python download_data.py bin
</code></pre>
<p>The Python version could be downloaded by</p>
<pre><code class="hljs css language-shell">python download_data.py py
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="training"></a><a href="#training" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Training</h3>
<p>There are four training programs</p>
<ol>
<li><p><code>train.py</code>. The following command would train the VGG model using the python
version of the Cifar-10 dataset in <code>cifar-10-batches-py</code> folder.
<code>shell python train.py vgg cifar-10-batches-py</code></p>
<pre><code class="hljs">To train other models, please replace 'vgg' to 'alexnet', 'resnet' or 'caffe',
where 'caffe' 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
```shell
python train.py vgg cifar-10-batches-py  --use_cpu
```
</code></pre></li>
<li><p><code>alexnet.cc</code>. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p>
<pre><code class="hljs css language-shell">./run.sh
</code></pre></li>
<li><p><code>alexnet-parallel.cc</code>. 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. <code>shell ./run-parallel.sh</code></p></li>
<li><p><code>vgg-parallel.cc</code>. It trains the VGG model using the CPP APIs on two CudaGPU
devices similar to <code>alexnet-parallel.cc</code>.</p></li>
</ol>
<h3><a class="anchor" aria-hidden="true" id="prediction"></a><a href="#prediction" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Prediction</h3>
<p><code>predict.py</code> includes the prediction function</p>
<pre><code class="hljs css language-python"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">predict</span><span class="hljs-params">(net, images, dev, topk=<span class="hljs-number">5</span>)</span>:</span>
</code></pre>
<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 file's main function provides an example of using the pre-trained
alexnet model to do prediction for new images. The 'model.bin' file generated by
the training program should be placed at the cifar10 folder to run</p>
<pre><code class="hljs css language-shell">python predict.py
</code></pre>
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