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<p>SINGA hỗ trợ đa dạng các thuật toán tối ưu (optimizers) phổ biến bao gồm
stochastic gradient descent với momentum, Adam, RMSProp, và AdaGrad, etc. Với
mỗi thuật toán tối ưu, SINGA hỗ trợ để sử dụng decay schedular để lên kế hoạch
learning rate áp dụng trong các epochs khác nhau. Các mỗi thuật toán tối ưu và
decay schedulers có trong <code>singa/opt.py</code>.</p>
<h2><a class="anchor" aria-hidden="true" id="tạo-thuật-toán-tối-ưu"></a><a href="#tạo-thuật-toán-tối-ưu" 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>Tạo thuật toán tối ưu</h2>
<ol>
<li>SGD với momentum</li>
</ol>
<pre><code class="hljs css language-python"><span class="hljs-comment"># xác định hyperparameter learning rate</span>
lr = <span class="hljs-number">0.001</span>
<span class="hljs-comment"># xác định hyperparameter momentum</span>
momentum = <span class="hljs-number">0.9</span>
<span class="hljs-comment"># xác định 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"># xác định hyperparameter learning rate</span>
lr = <span class="hljs-number">0.001</span>
<span class="hljs-comment"># xác định hyperparameter rho</span>
rho = <span class="hljs-number">0.9</span>
<span class="hljs-comment"># xác định hyperparameter epsilon</span>
epsilon = <span class="hljs-number">1e-8</span>
<span class="hljs-comment"># xác định 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"># xác định hyperparameter learning rate</span>
lr = <span class="hljs-number">0.001</span>
<span class="hljs-comment"># xác định hyperparameter epsilon</span>
epsilon = <span class="hljs-number">1e-8</span>
<span class="hljs-comment"># xác định 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"># xác định hyperparameter learning rate</span>
lr = <span class="hljs-number">0.001</span>
<span class="hljs-comment"># xác định hyperparameter beta 1</span>
beta_1= <span class="hljs-number">0.9</span>
<span class="hljs-comment"># xác định hyperparameter beta 2</span>
beta_1= <span class="hljs-number">0.999</span>
<span class="hljs-comment"># xác định hyperparameter epsilon</span>
epsilon = <span class="hljs-number">1e-8</span>
<span class="hljs-comment"># xác định 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="tạo-decay-scheduler"></a><a href="#tạo-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>Tạo 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"># xác định learning rate ban đầu</span>
lr_init = <span class="hljs-number">0.001</span>
<span class="hljs-comment"># xác định rate của decay trong decay scheduler</span>
decay_rate = <span class="hljs-number">0.95</span>
<span class="hljs-comment"># xác định learning rate schedule có ở dạng staircase shape</span>
staircase=<span class="hljs-literal">True</span>
<span class="hljs-comment"># xác định bước decay của decay scheduler (trong ví dụ này lr giảm sau mỗi 2 bước)</span>
decay_steps = <span class="hljs-number">2</span>
<span class="hljs-comment"># tạo decay scheduler, schedule của lr trở thành 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"># sử dụng lr để tạo một thuật toán tối ưu</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="sử-dụng-thuật-toán-tối-ưu-trong-model-api"></a><a href="#sử-dụng-thuật-toán-tối-ưu-trong-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>Sử dụng thuật toán tối ưu trong Model API</h2>
<p>Khi tạo mô hình model, cần đính kèm thuật toán tối ưu vào model.</p>
<pre><code class="hljs css language-python"><span class="hljs-comment"># tạo CNN sử dụng Model API</span>
model = CNN()
<span class="hljs-comment"># khởi tạo thuật toán tối ưu và đính vào 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>Sau đó, khi gọi hàm model, chạy phương pháp <code>train_one_batch</code> để sử dụng thuật
toán tối ưu.</p>
<p>Do vậy, một ví dụ cho lặp lại loop để tối ưu hoá model là:</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"># tạo mini-batch tiếp theo</span>
x, y = ...
<span class="hljs-comment"># Copy dữ liệu vào tensors đầu vào</span>
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
<span class="hljs-comment"># Train với một batch</span>
out, loss = model(tx, ty)
</code></pre>
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