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| <p>实现autograd有两种典型的方式，一种是通过如<a href="http://deeplearning.net/software/theano/index.html">Theano</a>的符号微分（symbolic differentiation）或通过如<a href="https://pytorch.org/docs/stable/notes/autograd.html">Pytorch</a>的反向微分（reverse differentialtion）。SINGA遵循Pytorch方式，即通过记录计算图，并在正向传播后自动应用反向传播。自动传播算法的详细解释请参阅<a href="https://pytorch.org/docs/stable/notes/autograd.html">这里</a>。我们接下来对SINGA中的相关模块进行解释，并举例说明其使用方法。</p> |
| <h2><a class="anchor" aria-hidden="true" id="相关模块"></a><a href="#相关模块" 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>相关模块</h2> |
| <p>在autograd中涉及三个类，分别是<code>singa.tensor.Tensor</code>，<code>singa.autograd.Operation</code>和<code>singa.autograd.Layer</code>。在本篇的后续部分中，我们使用Tensor、Operation和Layer来指代这三个类。</p> |
| <h3><a class="anchor" aria-hidden="true" id="tensor"></a><a href="#tensor" 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>Tensor</h3> |
| <p>Tensor的三个属性被autograd使用：</p> |
| <ul> |
| <li><code>.creator</code>是一个<code>Operation</code>实例。它记录了产生Tensor实例的这个操作。</li> |
| <li><code>.request_grad</code>是一个布尔变量。它用于指示autograd算法是否需要计算张量的梯度。例如，在反向传播的过程中，线性层的权重矩阵和卷积层（非底层）的特征图的张量梯度应该被计算。</li> |
| <li><code>.store_grad</code>是一个布尔变量。它用于指示张量的梯度是否应该被存储并由后向函数输出。例如，特征图的梯度是在反向传播过程中计算出来的，但不包括在反向函数的输出中。</li> |
| </ul> |
| <p>开发者可以改变Tensor实例的<code>requires_grad</code>和<code>stores_grad</code>。例如，如果将后者设置为True，那么相应的梯度就会被包含在后向函数的输出。需要注意的是，如果<code>stores_grad</code>是True，那么 <code>requires_grad</code>一定是真，反之亦然。</p> |
| <h3><a class="anchor" aria-hidden="true" id="operation"></a><a href="#operation" 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>Operation</h3> |
| <p>它将一个或多个<code>Tensor</code>实例作为输入，然后输出一个或多个<code>Tensor</code>实例。例如，ReLU可以作为一个具体的Operation子类来实现。当一个<code>Operation</code>实例被调用时（实例化后），会执行以下两个步骤。</p> |
| <p>1.记录源操作，即输入张量的<code>创建者</code>。 |
| 2.通过调用成员函数<code>.forward()</code>进行计算。</p> |
| <p>有两个成员函数用于前向和反向传播，即<code>.forward()</code>和<code>.backward()</code>。它们以<code>Tensor.data</code>作为输入（类型为<code>CTensor</code>），并输出<code>Ctensor</code>s。要添加一个特定的操作，子类<code>Operation</code>应该实现自己的<code>.forward()</code>和<code>.backward()</code>函数。在后向传播过程中，autograd的<code>backward()</code>函数会自动调用<code>backward()</code>函数来计算输入的梯度（根据<code>require_grad</code>字段的参数和约束）。</p> |
| <h3><a class="anchor" aria-hidden="true" id="layer"></a><a href="#layer" 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>Layer</h3> |
| <p>对于那些需要参数的Operation，我们把它们封装成一个新的类，<code>Layer</code>。例如，卷积操作被封装到卷积层(Convolution layer)中。<code>层</code>管理（存储）参数，并调用相应的<code>Operation</code>来实现变换。</p> |
| <h2><a class="anchor" aria-hidden="true" id="样例"></a><a href="#样例" 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>样例</h2> |
| <p>在<a href="https://github.com/apache/singa/tree/master/examples/autograd">example folder</a>中提供了很多样例。在这里我我们分析两个最具代表性的例子。</p> |
| <h3><a class="anchor" aria-hidden="true" id="只使用operation"></a><a href="#只使用operation" 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>只使用Operation</h3> |
| <p>下一段代码展示了一个只使用<code>Operation</code>的多层感知机（MLP）模型：</p> |
| <h4><a class="anchor" aria-hidden="true" id="调用依赖包"></a><a href="#调用依赖包" 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>调用依赖包</h4> |
| <pre><code class="hljs css language-python"><span class="hljs-keyword">from</span> singa.tensor <span class="hljs-keyword">import</span> Tensor |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> autograd |
| <span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="创建权重矩阵和偏置向量"></a><a href="#创建权重矩阵和偏置向量" 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>创建权重矩阵和偏置向量</h4> |
| <p>在将<code>requires_grad</code>和<code>stores_grad</code>都设置为<code>True</code>的情况下，创建参数张量。</p> |
| <pre><code class="hljs css language-python">w0 = Tensor(shape=(<span class="hljs-number">2</span>, <span class="hljs-number">3</span>), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>) |
| w0.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>) |
| b0 = Tensor(shape=(<span class="hljs-number">1</span>, <span class="hljs-number">3</span>), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>) |
| b0.set_value(<span class="hljs-number">0.0</span>) |
| |
| w1 = Tensor(shape=(<span class="hljs-number">3</span>, <span class="hljs-number">2</span>), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>) |
| w1.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>) |
| b1 = Tensor(shape=(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>) |
| b1.set_value(<span class="hljs-number">0.0</span>) |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="训练"></a><a href="#训练" 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>训练</h4> |
| <pre><code class="hljs css language-python">inputs = Tensor(data=data) <span class="hljs-comment"># data matrix</span> |
| target = Tensor(data=label) <span class="hljs-comment"># label vector</span> |
| autograd.training = <span class="hljs-literal">True</span> <span class="hljs-comment"># for training</span> |
| sgd = opt.SGD(<span class="hljs-number">0.05</span>) <span class="hljs-comment"># optimizer</span> |
| |
| <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(<span class="hljs-number">10</span>): |
| x = autograd.matmul(inputs, w0) <span class="hljs-comment"># matrix multiplication</span> |
| x = autograd.add_bias(x, b0) <span class="hljs-comment"># add the bias vector</span> |
| x = autograd.relu(x) <span class="hljs-comment"># ReLU activation operation</span> |
| |
| x = autograd.matmul(x, w1) |
| x = autograd.add_bias(x, b1) |
| |
| loss = autograd.softmax_cross_entropy(x, target) |
| |
| <span class="hljs-keyword">for</span> p, g <span class="hljs-keyword">in</span> autograd.backward(loss): |
| sgd.update(p, g) |
| </code></pre> |
| <h3><a class="anchor" aria-hidden="true" id="使用operation和layer"></a><a href="#使用operation和layer" 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>使用Operation和Layer</h3> |
| <p>下面的<a href="https://github.com/apache/singa/blob/master/examples/autograd/mnist_cnn.py">例子</a>使用autograd模块提供的层实现了一个CNN模型。</p> |
| <h4><a class="anchor" aria-hidden="true" id="创建层"></a><a href="#创建层" 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>创建层</h4> |
| <pre><code class="hljs css language-python">conv1 = autograd.Conv2d(<span class="hljs-number">1</span>, <span class="hljs-number">32</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-number">1</span>, bias=<span class="hljs-literal">False</span>) |
| bn1 = autograd.BatchNorm2d(<span class="hljs-number">32</span>) |
| pooling1 = autograd.MaxPool2d(<span class="hljs-number">3</span>, <span class="hljs-number">1</span>, padding=<span class="hljs-number">1</span>) |
| conv21 = autograd.Conv2d(<span class="hljs-number">32</span>, <span class="hljs-number">16</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-number">1</span>) |
| conv22 = autograd.Conv2d(<span class="hljs-number">32</span>, <span class="hljs-number">16</span>, <span class="hljs-number">3</span>, padding=<span class="hljs-number">1</span>) |
| bn2 = autograd.BatchNorm2d(<span class="hljs-number">32</span>) |
| linear = autograd.Linear(<span class="hljs-number">32</span> * <span class="hljs-number">28</span> * <span class="hljs-number">28</span>, <span class="hljs-number">10</span>) |
| pooling2 = autograd.AvgPool2d(<span class="hljs-number">3</span>, <span class="hljs-number">1</span>, padding=<span class="hljs-number">1</span>) |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="定义正向传播函数"></a><a href="#定义正向传播函数" 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>定义正向传播函数</h4> |
| <p>在正向传播中的operations会被自动记录，用于反向传播。</p> |
| <pre><code class="hljs css language-python"><span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(x, t)</span>:</span> |
| <span class="hljs-comment"># x is the input data (a batch of images)</span> |
| <span class="hljs-comment"># t is the label vector (a batch of integers)</span> |
| y = conv1(x) <span class="hljs-comment"># Conv layer</span> |
| y = autograd.relu(y) <span class="hljs-comment"># ReLU operation</span> |
| y = bn1(y) <span class="hljs-comment"># BN layer</span> |
| y = pooling1(y) <span class="hljs-comment"># Pooling Layer</span> |
| |
| <span class="hljs-comment"># two parallel convolution layers</span> |
| y1 = conv21(y) |
| y2 = conv22(y) |
| y = autograd.cat((y1, y2), <span class="hljs-number">1</span>) <span class="hljs-comment"># cat operation</span> |
| y = autograd.relu(y) <span class="hljs-comment"># ReLU operation</span> |
| y = bn2(y) |
| y = pooling2(y) |
| |
| y = autograd.flatten(y) <span class="hljs-comment"># flatten operation</span> |
| y = linear(y) <span class="hljs-comment"># Linear layer</span> |
| loss = autograd.softmax_cross_entropy(y, t) <span class="hljs-comment"># operation</span> |
| <span class="hljs-keyword">return</span> loss, y |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="训练-1"></a><a href="#训练-1" 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>训练</h4> |
| <pre><code class="hljs css language-python">autograd.training = <span class="hljs-literal">True</span> |
| <span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> range(epochs): |
| <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(batch_number): |
| inputs = tensor.Tensor(device=dev, data=x_train[ |
| i * batch_sz:(<span class="hljs-number">1</span> + i) * batch_sz], stores_grad=<span class="hljs-literal">False</span>) |
| targets = tensor.Tensor(device=dev, data=y_train[ |
| i * batch_sz:(<span class="hljs-number">1</span> + i) * batch_sz], requires_grad=<span class="hljs-literal">False</span>, stores_grad=<span class="hljs-literal">False</span>) |
| |
| loss, y = forward(inputs, targets) <span class="hljs-comment"># forward the net</span> |
| |
| <span class="hljs-keyword">for</span> p, gp <span class="hljs-keyword">in</span> autograd.backward(loss): <span class="hljs-comment"># auto backward</span> |
| sgd.update(p, gp) |
| </code></pre> |
| <h3><a class="anchor" aria-hidden="true" id="using-the-model-api"></a><a href="#using-the-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>Using the Model API</h3> |
| <p>下面的<a href="https://github.com/apache/singa/blob/master/examples/cnn/model/cnn.py">样例</a>使用<a href="./graph">Model API</a>实现了一个CNN模型。.</p> |
| <h4><a class="anchor" aria-hidden="true" id="定义model的子类"></a><a href="#定义model的子类" 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>定义Model的子类</h4> |
| <p>定义模型类，它应该是Model的子类。只有这样，在训练阶段使用的所有操作才会形成一个计算图以便进行分析。图中的操作将被按时序规划并有效执行，模型类中也可以包含层。</p> |
| <pre><code class="hljs css language-python"><span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">MLP</span><span class="hljs-params">(model.Model)</span>:</span> <span class="hljs-comment"># the model is a subclass of Model</span> |
| |
| <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">__init__</span><span class="hljs-params">(self, data_size=<span class="hljs-number">10</span>, perceptron_size=<span class="hljs-number">100</span>, num_classes=<span class="hljs-number">10</span>)</span>:</span> |
| super(MLP, self).__init__() |
| |
| <span class="hljs-comment"># init the operators, layers and other objects</span> |
| self.relu = layer.ReLU() |
| self.linear1 = layer.Linear(perceptron_size) |
| self.linear2 = layer.Linear(num_classes) |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| |
| <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(self, inputs)</span>:</span> <span class="hljs-comment"># define the forward function</span> |
| y = self.linear1(inputs) |
| y = self.relu(y) |
| y = self.linear2(y) |
| <span class="hljs-keyword">return</span> y |
| |
| <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">train_one_batch</span><span class="hljs-params">(self, x, y)</span>:</span> |
| out = self.forward(x) |
| loss = self.softmax_cross_entropy(out, y) |
| self.optimizer(loss) |
| <span class="hljs-keyword">return</span> out, loss |
| |
| <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">set_optimizer</span><span class="hljs-params">(self, optimizer)</span>:</span> <span class="hljs-comment"># attach an optimizer</span> |
| self.optimizer = optimizer |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="训练-2"></a><a href="#训练-2" 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>训练</h4> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># create a model instance</span> |
| model = MLP() |
| <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) |
| <span class="hljs-comment"># input and target placeholders for the model</span> |
| tx = tensor.Tensor((batch_size, <span class="hljs-number">1</span>, IMG_SIZE, IMG_SIZE), dev, tensor.float32) |
| ty = tensor.Tensor((batch_size, num_classes), dev, tensor.int32) |
| <span class="hljs-comment"># compile the model before training</span> |
| model.compile([tx], is_train=<span class="hljs-literal">True</span>, use_graph=<span class="hljs-literal">True</span>, sequential=<span class="hljs-literal">False</span>) |
| |
| <span class="hljs-comment"># train the model iteratively</span> |
| <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> |
| <h4><a class="anchor" aria-hidden="true" id="保存模型checkpoint"></a><a href="#保存模型checkpoint" 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>保存模型checkpoint</h4> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define the path to save the checkpoint</span> |
| checkpointpath=<span class="hljs-string">"checkpoint.zip"</span> |
| |
| <span class="hljs-comment"># save a checkpoint</span> |
| model.save_states(fpath=checkpointpath) |
| </code></pre> |
| <h4><a class="anchor" aria-hidden="true" id="加载模型checkpoint"></a><a href="#加载模型checkpoint" 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>加载模型checkpoint</h4> |
| <pre><code class="hljs css language-python"><span class="hljs-comment"># define the path to load the checkpoint</span> |
| checkpointpath=<span class="hljs-string">"checkpoint.zip"</span> |
| |
| <span class="hljs-comment"># load a checkpoint</span> |
| <span class="hljs-keyword">import</span> os |
| <span class="hljs-keyword">if</span> os.path.exists(checkpointpath): |
| model.load_states(fpath=checkpointpath) |
| </code></pre> |
| <h3><a class="anchor" aria-hidden="true" id="python-api"></a><a href="#python-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>Python API</h3> |
| <p>关于Python API的更多细节，请参考<a href="https://singa.readthedocs.io/en/latest/autograd.html#module-singa.autograd">这里</a>。</p> |
| </span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/4.0.0_Chinese/tensor"><span class="arrow-prev">← </span><span>Tensor</span></a><a class="docs-next button" href="/docs/4.0.0_Chinese/optimizer"><span>Optimizer</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#相关模块">相关模块</a><ul class="toc-headings"><li><a href="#tensor">Tensor</a></li><li><a href="#operation">Operation</a></li><li><a href="#layer">Layer</a></li></ul></li><li><a href="#样例">样例</a><ul class="toc-headings"><li><a href="#只使用operation">只使用Operation</a></li><li><a href="#使用operation和layer">使用Operation和Layer</a></li><li><a href="#using-the-model-api">Using the Model API</a></li><li><a href="#python-api">Python API</a></li></ul></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 © 2023 |
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