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| |
| <h1 id="basics">Basics</h1> |
| |
| <p>This tutorial provides basic usages of the C++ package through the classical handwritten digits |
| identification database--<a href="http://yann.lecun.com/exdb/mnist/">MNIST</a>.</p> |
| |
| <p>The following contents assume that the working directory is <code>/path/to/mxnet/cpp-package/example</code>.</p> |
| |
| <h2 id="load-data">Load Data</h2> |
| |
| <p>Before going into codes, we need to fetch MNIST data. You can either use the script <code>/path/to/mxnet/cpp-package/example/get_data.sh</code>, |
| or download mnist data by yourself from Lecun's <a href="http://yann.lecun.com/exdb/mnist/">website</a> |
| and decompress them into <code>data/mnist_data</code> folder.</p> |
| |
| <p>Except linking the MXNet shared library, the C++ package itself is a header-only package, |
| which means all you need to do is to include the header files. Among the header files, |
| <code>op.h</code> is special since it is generated dynamically. The generation should be done when |
| <a href="/versions/1.9.1/api/cpp/">building the C++ package</a>. |
| It is important to note that you need to <strong>copy the shared library</strong> (<code>libmxnet.so</code> in Linux and MacOS, |
| <code>libmxnet.dll</code> in Windows) from <code>/path/to/mxnet/lib</code> to the working directory. |
| We do not recommend you to use pre-built binaries because MXNet is under heavy development, |
| the operator definitions in <code>op.h</code> may be incompatible with the pre-built version.</p> |
| |
| <p>In order to use functionalities provides by the C++ package, first we include the general |
| header file <code>MxNetCpp.h</code> and specify the namespaces.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="cp">#include "mxnet-cpp/MxNetCpp.h" |
| </span> |
| <span class="k">using</span> <span class="k">namespace</span> <span class="n">std</span><span class="p">;</span> |
| <span class="k">using</span> <span class="k">namespace</span> <span class="n">mxnet</span><span class="o">::</span><span class="n">cpp</span><span class="p">;</span> |
| </code></pre></div> |
| <p>Next we can use the data iter to load MNIST data (separated to training sets and validation sets). |
| The digits in MNIST are 2-dimension arrays, so we should set <code>flat</code> to true to flatten the data.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="k">auto</span> <span class="n">train_iter</span> <span class="o">=</span> <span class="n">MXDataIter</span><span class="p">(</span><span class="s">"MNISTIter"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"image"</span><span class="p">,</span> <span class="s">"./data/mnist_data/train-images-idx3-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"./data/mnist_data/train-labels-idx1-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"batch_size"</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"flat"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">CreateDataIter</span><span class="p">();</span> |
| <span class="k">auto</span> <span class="n">val_iter</span> <span class="o">=</span> <span class="n">MXDataIter</span><span class="p">(</span><span class="s">"MNISTIter"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"image"</span><span class="p">,</span> <span class="s">"./data/mnist_data/t10k-images-idx3-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"label"</span><span class="p">,</span> <span class="s">"./data/mnist_data/t10k-labels-idx1-ubyte"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"batch_size"</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">SetParam</span><span class="p">(</span><span class="s">"flat"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">CreateDataIter</span><span class="p">();</span> |
| </code></pre></div> |
| <p>The data have been successfully loaded. We can now easily construct various models to identify |
| the digits with the help of C++ package.</p> |
| |
| <h2 id="multilayer-perceptron">Multilayer Perceptron</h2> |
| |
| <p>If you are not familiar with multilayer perceptron, you can get some basic information |
| <a href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">here</a>. We only focus on |
| the implementation in this tutorial.</p> |
| |
| <p>Constructing multilayer perceptron model is straightforward, assume we store the hidden size |
| for each layer in <code>layers</code>, and each layer uses |
| <a href="https://en.wikipedia.org/wiki/Rectifier_(neural_networks)">ReLu</a> function as activation.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">Symbol</span> <span class="nf">mlp</span><span class="p">(</span><span class="k">const</span> <span class="n">vector</span><span class="o"><</span><span class="kt">int</span><span class="o">></span> <span class="o">&</span><span class="n">layers</span><span class="p">)</span> <span class="p">{</span> |
| <span class="k">auto</span> <span class="n">x</span> <span class="o">=</span> <span class="n">Symbol</span><span class="o">::</span><span class="n">Variable</span><span class="p">(</span><span class="s">"X"</span><span class="p">);</span> |
| <span class="k">auto</span> <span class="n">label</span> <span class="o">=</span> <span class="n">Symbol</span><span class="o">::</span><span class="n">Variable</span><span class="p">(</span><span class="s">"label"</span><span class="p">);</span> |
| |
| <span class="n">vector</span><span class="o"><</span><span class="n">Symbol</span><span class="o">></span> <span class="n">weights</span><span class="p">(</span><span class="n">layers</span><span class="p">.</span><span class="n">size</span><span class="p">());</span> |
| <span class="n">vector</span><span class="o"><</span><span class="n">Symbol</span><span class="o">></span> <span class="n">biases</span><span class="p">(</span><span class="n">layers</span><span class="p">.</span><span class="n">size</span><span class="p">());</span> |
| <span class="n">vector</span><span class="o"><</span><span class="n">Symbol</span><span class="o">></span> <span class="n">outputs</span><span class="p">(</span><span class="n">layers</span><span class="p">.</span><span class="n">size</span><span class="p">());</span> |
| |
| <span class="k">for</span> <span class="p">(</span><span class="kt">int</span> <span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span> <span class="n">i</span><span class="o"><</span><span class="n">layers</span><span class="p">.</span><span class="n">size</span><span class="p">();</span> <span class="o">++</span><span class="n">i</span><span class="p">)</span> <span class="p">{</span> |
| <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">Symbol</span><span class="o">::</span><span class="n">Variable</span><span class="p">(</span><span class="s">"w"</span> <span class="o">+</span> <span class="n">to_string</span><span class="p">(</span><span class="n">i</span><span class="p">));</span> |
| <span class="n">biases</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">Symbol</span><span class="o">::</span><span class="n">Variable</span><span class="p">(</span><span class="s">"b"</span> <span class="o">+</span> <span class="n">to_string</span><span class="p">(</span><span class="n">i</span><span class="p">));</span> |
| <span class="n">Symbol</span> <span class="n">fc</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span> |
| <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="o">?</span> <span class="n">x</span> <span class="o">:</span> <span class="n">outputs</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> |
| <span class="n">biases</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> |
| <span class="n">layers</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="p">);</span> |
| <span class="n">outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> <span class="o">==</span> <span class="n">layers</span><span class="p">.</span><span class="n">size</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span> <span class="o">?</span> <span class="n">fc</span> <span class="o">:</span> <span class="n">Activation</span><span class="p">(</span><span class="n">fc</span><span class="p">,</span> <span class="n">ActivationActType</span><span class="o">::</span><span class="n">relu</span><span class="p">);</span> |
| <span class="p">}</span> |
| |
| <span class="k">return</span> <span class="n">SoftmaxOutput</span><span class="p">(</span><span class="n">outputs</span><span class="p">.</span><span class="n">back</span><span class="p">(),</span> <span class="n">label</span><span class="p">);</span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <p>The above function defines a multilayer perceptron model where hidden sizes are specified |
| by <code>layers</code>.</p> |
| |
| <p>We now create and initialize the parameters after the model is constructed. MXNet can help |
| you to infer shapes of most of the parameters. Basically only the shape of data and label |
| is needed.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">std</span><span class="o">::</span><span class="n">map</span><span class="o"><</span><span class="n">string</span><span class="p">,</span> <span class="n">NDArray</span><span class="o">></span> <span class="n">args</span><span class="p">;</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]</span> <span class="o">=</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">Shape</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">image_size</span><span class="o">*</span><span class="n">image_size</span><span class="p">),</span> <span class="n">ctx</span><span class="p">);</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">Shape</span><span class="p">(</span><span class="n">batch_size</span><span class="p">),</span> <span class="n">ctx</span><span class="p">);</span> |
| <span class="c1">// Let MXNet infer shapes other parameters such as weights</span> |
| <span class="n">net</span><span class="p">.</span><span class="n">InferArgsMap</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="o">&</span><span class="n">args</span><span class="p">,</span> <span class="n">args</span><span class="p">);</span> |
| |
| <span class="c1">// Initialize all parameters with uniform distribution U(-0.01, 0.01)</span> |
| <span class="k">auto</span> <span class="n">initializer</span> <span class="o">=</span> <span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">);</span> |
| <span class="k">for</span> <span class="p">(</span><span class="k">auto</span><span class="o">&</span> <span class="n">arg</span> <span class="o">:</span> <span class="n">args</span><span class="p">)</span> <span class="p">{</span> |
| <span class="c1">// arg.first is parameter name, and arg.second is the value</span> |
| <span class="n">initializer</span><span class="p">(</span><span class="n">arg</span><span class="p">.</span><span class="n">first</span><span class="p">,</span> <span class="o">&</span><span class="n">arg</span><span class="p">.</span><span class="n">second</span><span class="p">);</span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <p>The rest is to train the model with an optimizer. |
| ```c++ |
| // Create sgd optimizer |
| Optimizer* opt = OptimizerRegistry::Find("sgd"); |
| opt->SetParam("rescale_grad", 1.0/batch_size);</p> |
| |
| <p>// Start training |
| for (int iter = 0; iter < max_epoch; ++iter) { |
| train_iter.Reset();</p> |
| |
| <p>while (train_iter.Next()) { |
| auto data_batch = train_iter.GetDataBatch(); |
| // Set data and label |
| args["X"] = data_batch.data; |
| args["label"] = data_batch.label;</p> |
| <div class="highlight"><pre><code class="language-" data-lang="">// Create executor by binding parameters to the model |
| auto *exec = net.SimpleBind(ctx, args); |
| // Compute gradients |
| exec->Forward(true); |
| exec->Backward(); |
| // Update parameters |
| exec->UpdateAll(opt, learning_rate, weight_decay); |
| // Remember to free the memory |
| delete exec; |
| </code></pre></div> |
| <p>} |
| } |
| ```</p> |
| |
| <p>We also want to see how our model performs. The C++ package provides convenient APIs for |
| evaluating. Here we use accuracy as metric. The inference is almost the same as training, |
| except that we don't need gradients.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">Accuracy</span> <span class="n">acc</span><span class="p">;</span> |
| <span class="n">val_iter</span><span class="p">.</span><span class="n">Reset</span><span class="p">();</span> |
| <span class="k">while</span> <span class="p">(</span><span class="n">val_iter</span><span class="p">.</span><span class="n">Next</span><span class="p">())</span> <span class="p">{</span> |
| <span class="k">auto</span> <span class="n">data_batch</span> <span class="o">=</span> <span class="n">val_iter</span><span class="p">.</span><span class="n">GetDataBatch</span><span class="p">();</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">data</span><span class="p">;</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">;</span> |
| <span class="k">auto</span> <span class="o">*</span><span class="n">exec</span> <span class="o">=</span> <span class="n">net</span><span class="p">.</span><span class="n">SimpleBind</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">args</span><span class="p">);</span> |
| <span class="c1">// Forward pass is enough as no gradient is needed when evaluating</span> |
| <span class="n">exec</span><span class="o">-></span><span class="n">Forward</span><span class="p">(</span><span class="nb">false</span><span class="p">);</span> |
| <span class="n">acc</span><span class="p">.</span><span class="n">Update</span><span class="p">(</span><span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">,</span> <span class="n">exec</span><span class="o">-></span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span> |
| <span class="k">delete</span> <span class="n">exec</span><span class="p">;</span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <p>You can find the complete code in <code>mlp_cpu.cpp</code>. Use <code>make mlp_cpu</code> to compile it, |
| and <code>./mlp_cpu</code> to run it. If it complains that the shared library <code>libmxnet.so</code> is not found |
| after typing <code>./mlp_cpu</code>, you will need to specify the path to the shared library in |
| the environment variable <code>LD_LIBRARY_PATH</code> in Linux and <code>DYLD_LIBRARY_PATH</code> |
| in MacOS. For example, if you are using MacOS, typing |
| <code>DYLD_LIBRARY_PATH+=. ./mlp_cpu</code> would solve the problem. It basically tells the system |
| to find the shared library under the current directory since we have just copied it here.</p> |
| |
| <h2 id="gpu-support">GPU Support</h2> |
| |
| <p>It's worth noting that changing context from <code>Context::cpu()</code> to <code>Context::gpu()</code> is not enough, |
| because the data read by data iter are stored in memory, we cannot assign it directly to the |
| parameters. To bridge this gap, NDArray provides data synchronization functionalities between |
| GPU and CPU. We will illustrate it by making the mlp code run on GPU.</p> |
| |
| <p>In the previous code, data are used like</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">data</span><span class="p">;</span> |
| <span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">;</span> |
| </code></pre></div> |
| <p>It will be problematic if other parameters are created in the context of GPU. We can use |
| <code>NDArray::CopyTo</code> to solve this problem.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="c1">// Data provided by DataIter are stored in memory, should be copied to GPU first.</span> |
| <span class="n">data_batch</span><span class="p">.</span><span class="n">data</span><span class="p">.</span><span class="n">CopyTo</span><span class="p">(</span><span class="o">&</span><span class="n">args</span><span class="p">[</span><span class="s">"X"</span><span class="p">]);</span> |
| <span class="n">data_batch</span><span class="p">.</span><span class="n">label</span><span class="p">.</span><span class="n">CopyTo</span><span class="p">(</span><span class="o">&</span><span class="n">args</span><span class="p">[</span><span class="s">"label"</span><span class="p">]);</span> |
| <span class="c1">// CopyTo is imperative, need to wait for it to complete.</span> |
| <span class="n">NDArray</span><span class="o">::</span><span class="n">WaitAll</span><span class="p">();</span> |
| </code></pre></div> |
| <p>By replacing the former code to the latter one, we successfully port the code to GPU. |
| You can find the complete code in <code>mlp_gpu.cpp</code>. Compilation is similar to the cpu version. |
| Note that the shared library must be built with GPU support enabled.</p> |
| </div> |
| </div> |
| |
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