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| <p>SINGA supports various popular optimizers including stochastic gradient descent |
| with momentum, Adam, RMSProp, and AdaGrad, etc. For each of the optimizer, it |
| supports to use a decay schedular to schedule the learning rate to be applied in |
| different epochs. The optimizers and the decay schedulers are included in |
| <code>singa/opt.py</code>.</p> |
| <h2><a class="anchor" aria-hidden="true" id="create-an-optimizer"></a><a href="#create-an-optimizer" 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>Create an optimizer</h2> |
| <ol> |
| <li>SGD with momentum</li> |
| </ol> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define hyperparameter learning rate</span> |
| lr = <span class="hljs-number">0.001</span> |
| <span class="hljs-comment"># define hyperparameter momentum</span> |
| momentum = <span class="hljs-number">0.9</span> |
| <span class="hljs-comment"># define hyperparameter weight decay</span> |
| weight_decay = <span class="hljs-number">0.0001</span> |
| |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| sgd = opt.SGD(lr=lr, momentum=momentum, weight_decay=weight_decay) |
| </code></pre> |
| <ol start="2"> |
| <li>RMSProp</li> |
| </ol> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define hyperparameter learning rate</span> |
| lr = <span class="hljs-number">0.001</span> |
| <span class="hljs-comment"># define hyperparameter rho</span> |
| rho = <span class="hljs-number">0.9</span> |
| <span class="hljs-comment"># define hyperparameter epsilon</span> |
| epsilon = <span class="hljs-number">1e-8</span> |
| <span class="hljs-comment"># define hyperparameter weight decay</span> |
| weight_decay = <span class="hljs-number">0.0001</span> |
| |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| sgd = opt.RMSProp(lr=lr, rho=rho, epsilon=epsilon, weight_decay=weight_decay) |
| </code></pre> |
| <ol start="3"> |
| <li>AdaGrad</li> |
| </ol> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define hyperparameter learning rate</span> |
| lr = <span class="hljs-number">0.001</span> |
| <span class="hljs-comment"># define hyperparameter epsilon</span> |
| epsilon = <span class="hljs-number">1e-8</span> |
| <span class="hljs-comment"># define hyperparameter weight decay</span> |
| weight_decay = <span class="hljs-number">0.0001</span> |
| |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| sgd = opt.AdaGrad(lr=lr, epsilon=epsilon, weight_decay=weight_decay) |
| </code></pre> |
| <ol start="4"> |
| <li>Adam</li> |
| </ol> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define hyperparameter learning rate</span> |
| lr = <span class="hljs-number">0.001</span> |
| <span class="hljs-comment"># define hyperparameter beta 1</span> |
| beta_1= <span class="hljs-number">0.9</span> |
| <span class="hljs-comment"># define hyperparameter beta 2</span> |
| beta_1= <span class="hljs-number">0.999</span> |
| <span class="hljs-comment"># define hyperparameter epsilon</span> |
| epsilon = <span class="hljs-number">1e-8</span> |
| <span class="hljs-comment"># define hyperparameter weight decay</span> |
| weight_decay = <span class="hljs-number">0.0001</span> |
| |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| sgd = opt.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, weight_decay=weight_decay) |
| </code></pre> |
| <h2><a class="anchor" aria-hidden="true" id="create-a-decay-scheduler"></a><a href="#create-a-decay-scheduler" 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>Create a Decay Scheduler</h2> |
| <pre><code class="hljs css language-python"><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| |
| <span class="hljs-comment"># define initial learning rate</span> |
| lr_init = <span class="hljs-number">0.001</span> |
| <span class="hljs-comment"># define the rate of decay in the decay scheduler</span> |
| decay_rate = <span class="hljs-number">0.95</span> |
| <span class="hljs-comment"># define whether the learning rate schedule is a staircase shape</span> |
| staircase=<span class="hljs-literal">True</span> |
| <span class="hljs-comment"># define the decay step of the decay scheduler (in this example the lr is decreased at every 2 steps)</span> |
| decay_steps = <span class="hljs-number">2</span> |
| |
| <span class="hljs-comment"># create the decay scheduler, the schedule of lr becomes lr_init * (decay_rate ^ (step // decay_steps) )</span> |
| lr = opt.ExponentialDecay(<span class="hljs-number">0.1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0.5</span>, <span class="hljs-literal">True</span>) |
| <span class="hljs-comment"># Use the lr to create an optimizer</span> |
| sgd = opt.SGD(lr=lr, momentum=<span class="hljs-number">0.9</span>, weight_decay=<span class="hljs-number">0.0001</span>) |
| </code></pre> |
| <h2><a class="anchor" aria-hidden="true" id="use-the-optimizer-in-model-api"></a><a href="#use-the-optimizer-in-model-api" 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>Use the optimizer in Model API</h2> |
| <p>When we create the model, we need to attach the optimizer to the model.</p> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># create a CNN using the Model API</span> |
| model = CNN() |
| |
| <span class="hljs-comment"># initialize optimizer and attach it to the model</span> |
| sgd = opt.SGD(lr=<span class="hljs-number">0.005</span>, momentum=<span class="hljs-number">0.9</span>, weight_decay=<span class="hljs-number">1e-5</span>) |
| model.set_optimizer(sgd) |
| </code></pre> |
| <p>Then, when we call the model, it runs the <code>train_one_batch</code> method that utilizes |
| the optimizer.</p> |
| <p>Hence, an example of an iterative loop to optimize the model is:</p> |
| <pre><code class="hljs css language-python"><span class="hljs-keyword">for</span> b <span class="hljs-keyword">in</span> range(num_train_batch): |
| <span class="hljs-comment"># generate the next mini-batch</span> |
| x, y = ... |
| |
| <span class="hljs-comment"># Copy the data into input tensors</span> |
| tx.copy_from_numpy(x) |
| ty.copy_from_numpy(y) |
| |
| <span class="hljs-comment"># Training with one batch</span> |
| out, loss = model(tx, ty) |
| </code></pre> |
| </span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/7/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/autograd"><span class="arrow-prev">← </span><span>Autograd</span></a><a class="docs-next button" href="/docs/next/graph"><span>Model</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#create-an-optimizer">Create an optimizer</a></li><li><a href="#create-a-decay-scheduler">Create a Decay Scheduler</a></li><li><a href="#use-the-optimizer-in-model-api">Use the optimizer in Model API</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><a href="/" class="nav-home"><img src="/img/singa-logo-square.png" alt="Apache SINGA" width="66" height="58"/></a><div><h5>Docs</h5><a href="/docs/installation">Getting Started</a><a href="/docs/device">Guides</a><a href="/en/https://apache-singa.readthedocs.io/en/latest/">API Reference</a><a href="/docs/examples">Examples</a><a href="/docs/download-singa">Development</a></div><div><h5>Community</h5><a href="/en/users.html">User Showcase</a><a href="/docs/history-singa">SINGA History</a><a href="/docs/team-list">SINGA Team</a><a href="/blog">SINGA News</a><a href="https://github.com/apache/singa">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa" data-count-href="/apache/singa/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star this project on GitHub">apache/singa-doc</a></div><div class="social"><a href="https://twitter.com/ApacheSINGA" class="twitter-follow-button">Follow @ApacheSINGA</a></div></div><div><h5>Apache Software Foundation</h5><a href="https://apache.org/" target="_blank" rel="noreferrer noopener">Foundation</a><a href="http://www.apache.org/licenses/" target="_blank" rel="noreferrer noopener">License</a><a href="http://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noreferrer noopener">Sponsorship</a><a href="http://www.apache.org/foundation/thanks.html" target="_blank" rel="noreferrer noopener">Thanks</a><a href="http://www.apache.org/events/current-event" target="_blank" rel="noreferrer noopener">Events</a><a href="http://www.apache.org/security/" target="_blank" rel="noreferrer noopener">Security</a></div></section><div style="width:100%;text-align:center"><a href="https://apache.org/" target="_blank" rel="noreferrer noopener" class="ApacheOpenSource"><img src="/img/asf_logo_wide.svg" alt="Apache Open Source"/></a><section class="copyright" style="max-width:60%;margin:0 auto">Copyright © 2020 |
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