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Posted to commits@singa.apache.org by wa...@apache.org on 2020/09/29 01:55:13 UTC
[singa-site] branch master updated: compile the site for v3.1.0.rc1
This is an automated email from the ASF dual-hosted git repository.
wangwei pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/singa-site.git
The following commit(s) were added to refs/heads/master by this push:
new 88b81dd compile the site for v3.1.0.rc1
88b81dd is described below
commit 88b81dd9473a44b3b760eaa986b9c633109afb87
Author: wang wei <wa...@gmail.com>
AuthorDate: Tue Sep 29 09:54:54 2020 +0800
compile the site for v3.1.0.rc1
---
content/docs/2.0.0/software-stack.html | 2 +-
content/docs/2.0.0/software-stack/index.html | 2 +-
content/docs/next/autograd.html | 93 +++++++------
content/docs/next/autograd/index.html | 93 +++++++------
content/docs/next/build.html | 2 +-
content/docs/next/build/index.html | 2 +-
content/docs/next/contribute-code.html | 2 +-
content/docs/next/contribute-code/index.html | 2 +-
content/docs/next/contribute-docs.html | 2 +-
content/docs/next/contribute-docs/index.html | 2 +-
content/docs/next/device.html | 2 +-
content/docs/next/device/index.html | 2 +-
content/docs/next/dist-train.html | 4 +-
content/docs/next/dist-train/index.html | 4 +-
content/docs/next/download-singa.html | 4 +-
content/docs/next/download-singa/index.html | 4 +-
content/docs/next/examples.html | 2 +-
content/docs/next/examples/index.html | 2 +-
content/docs/next/git-workflow.html | 2 +-
content/docs/next/git-workflow/index.html | 2 +-
content/docs/next/graph.html | 8 +-
content/docs/next/graph/index.html | 8 +-
content/docs/next/how-to-release.html | 2 +-
content/docs/next/how-to-release/index.html | 2 +-
content/docs/next/installation.html | 2 +-
content/docs/next/installation/index.html | 2 +-
content/docs/next/onnx.html | 2 +-
content/docs/next/onnx/index.html | 2 +-
content/docs/next/optimizer.html | 189 ++++++++++++++++++++++++++-
content/docs/next/optimizer/index.html | 189 ++++++++++++++++++++++++++-
content/docs/next/software-stack.html | 2 +-
content/docs/next/software-stack/index.html | 2 +-
content/docs/next/tensor.html | 2 +-
content/docs/next/tensor/index.html | 2 +-
content/sitemap.xml | 2 +-
35 files changed, 513 insertions(+), 133 deletions(-)
diff --git a/content/docs/2.0.0/software-stack.html b/content/docs/2.0.0/software-stack.html
index 2773886..420fac6 100644
--- a/content/docs/2.0.0/software-stack.html
+++ b/content/docs/2.0.0/software-stack.html
@@ -89,7 +89,7 @@
<li><p><a href="initializer.md">Initializer</a> and its subclasses provide variant methods of initializing model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.</p></li>
<li><p><a href="loss.md">Loss</a> and its subclasses defines the training objective loss functions. Both functions of computing the loss values and computing the gradient of the prediction w.r.t the objective loss are implemented. Example loss functions include squared error and cross entropy.</p></li>
<li><p><a href="metric.md">Metric</a> and its subclasses provide the function to measure the performance of the model, e.g., the accuracy.</p></li>
-<li><p><a href="optimizer.md">Optimizer</a> and its subclasses implement the methods for updating model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li>
+<li><p><a href="/docs/2.0.0/optimizer">Optimizer</a> and its subclasses implement the methods for updating model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li>
</ul>
<h2><a class="anchor" aria-hidden="true" id="io"></a><a href="#io" 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 [...]
<p>The IO module consists of classes for data loading, data preprocessing and message passing.</p>
diff --git a/content/docs/2.0.0/software-stack/index.html b/content/docs/2.0.0/software-stack/index.html
index 2773886..420fac6 100644
--- a/content/docs/2.0.0/software-stack/index.html
+++ b/content/docs/2.0.0/software-stack/index.html
@@ -89,7 +89,7 @@
<li><p><a href="initializer.md">Initializer</a> and its subclasses provide variant methods of initializing model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.</p></li>
<li><p><a href="loss.md">Loss</a> and its subclasses defines the training objective loss functions. Both functions of computing the loss values and computing the gradient of the prediction w.r.t the objective loss are implemented. Example loss functions include squared error and cross entropy.</p></li>
<li><p><a href="metric.md">Metric</a> and its subclasses provide the function to measure the performance of the model, e.g., the accuracy.</p></li>
-<li><p><a href="optimizer.md">Optimizer</a> and its subclasses implement the methods for updating model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li>
+<li><p><a href="/docs/2.0.0/optimizer">Optimizer</a> and its subclasses implement the methods for updating model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li>
</ul>
<h2><a class="anchor" aria-hidden="true" id="io"></a><a href="#io" 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 [...]
<p>The IO module consists of classes for data loading, data preprocessing and message passing.</p>
diff --git a/content/docs/next/autograd.html b/content/docs/next/autograd.html
index 2bd1a2f..cb42ec1 100644
--- a/content/docs/next/autograd.html
+++ b/content/docs/next/autograd.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -212,7 +212,7 @@ propagation.</p>
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 0-2-1.2 [...]
-<p>The following
+<p>The following <<<<<<< HEAD
<a href="https://github.com/apache/singa/blob/master/examples/cnn/model/cnn.py">example</a>
implements a CNN model using the <a href="./graph">Model API</a>.</p>
<h4><a class="anchor" aria-hidden="true" id="define-the-subclass-of-model"></a><a href="#define-the-subclass-of-model" 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 [...]
@@ -222,60 +222,75 @@ will be analyzed. The operations in the graph will be scheduled and executed
efficiently. Layers can also be included in the model class.</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, optimizer)</span>:</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.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>)
- self.w0.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>)
- self.b0 = Tensor(shape=(<span class="hljs-number">3</span>,), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>)
- self.b0.set_value(<span class="hljs-number">0.0</span>)
-
- self.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>)
- self.w1.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>)
- self.b1 = Tensor(shape=(<span class="hljs-number">2</span>,), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>)
- self.b1.set_value(<span class="hljs-number">0.0</span>)
-
- <span class="hljs-comment"># init the optimizer</span>
- self.optimizer = optimizer
+ 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>
- x = autograd.matmul(inputs, self.w0)
- x = autograd.add_bias(x, self.b0)
- x = autograd.relu(x)
- x = autograd.matmul(x, self.w1)
- x = autograd.add_bias(x, self.b1)
- <span class="hljs-keyword">return</span> x
+ 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">loss</span><span class="hljs-params">(self, out, target)</span>:</span> <span class="hljs-comment"># define the loss function</span>
- <span class="hljs-comment"># can use the loss operations provided by SINGA or self-defined function</span>
- <span class="hljs-keyword">return</span> autograd.softmax_cross_entropy(out, target)
+ <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">optim</span><span class="hljs-params">(self, loss)</span>:</span> <span class="hljs-comment"># define the optim function</span>
- <span class="hljs-comment"># can use the optimizer provided by SINGA or self-defined function</span>
- <span class="hljs-keyword">return</span> self.optimizer.backward_and_update(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="training-2"></a><a href="#training-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 [...]
<pre><code class="hljs css language-python"><span class="hljs-comment"># create a model instance</span>
-model = MLP(sgd)
-<span class="hljs-comment"># declare what device to train on</span>
-model.on_device(dev)
-<span class="hljs-comment"># declare execution mode and order</span>
-model.graph(graph, sequential)
+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-keyword">for</span> i <span class="hljs-keyword">in</span> range(niters):
- out = model(inputs)
- loss = model.loss(out, target)
- model.optim(loss)
+<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="save-a-model-checkpoint"></a><a href="#save-a-model-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 [...]
+<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="load-a-model-checkpoint"></a><a href="#load-a-model-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 [...]
+<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-keyword">if</span> i % (niters / <span class="hljs-number">10</span>) == <span class="hljs-number">0</span> <span class="hljs-keyword">and</span> rank_in_global == <span class="hljs-number">0</span>:
- print(<span class="hljs-string">"training loss = "</span>, tensor.to_numpy(loss)[<span class="hljs-number">0</span>], flush=<span class="hljs-literal">True</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 [...]
<p>Refer
<a href="https://singa.readthedocs.io/en/latest/autograd.html#module-singa.autograd">here</a>
for more details of Python API.</p>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/23/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/tensor"><span class="arrow-prev">← </span><span>Tensor</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="#relevant-modules">Relevant Modules</a><ul class="toc-headings"><li><a h [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/27/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/tensor"><span class="arrow-prev">← </span><span>Tensor</span></a><a class="docs-next button" href="/docs/next/optimizer"><span>Optimizer</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#relevant-modules">Relevant Modules</a><ul class="toc-headings"> [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/autograd/index.html b/content/docs/next/autograd/index.html
index 2bd1a2f..cb42ec1 100644
--- a/content/docs/next/autograd/index.html
+++ b/content/docs/next/autograd/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -212,7 +212,7 @@ propagation.</p>
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 0-2-1.2 [...]
-<p>The following
+<p>The following <<<<<<< HEAD
<a href="https://github.com/apache/singa/blob/master/examples/cnn/model/cnn.py">example</a>
implements a CNN model using the <a href="./graph">Model API</a>.</p>
<h4><a class="anchor" aria-hidden="true" id="define-the-subclass-of-model"></a><a href="#define-the-subclass-of-model" 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 [...]
@@ -222,60 +222,75 @@ will be analyzed. The operations in the graph will be scheduled and executed
efficiently. Layers can also be included in the model class.</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, optimizer)</span>:</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.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>)
- self.w0.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>)
- self.b0 = Tensor(shape=(<span class="hljs-number">3</span>,), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>)
- self.b0.set_value(<span class="hljs-number">0.0</span>)
-
- self.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>)
- self.w1.gaussian(<span class="hljs-number">0.0</span>, <span class="hljs-number">0.1</span>)
- self.b1 = Tensor(shape=(<span class="hljs-number">2</span>,), requires_grad=<span class="hljs-literal">True</span>, stores_grad=<span class="hljs-literal">True</span>)
- self.b1.set_value(<span class="hljs-number">0.0</span>)
-
- <span class="hljs-comment"># init the optimizer</span>
- self.optimizer = optimizer
+ 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>
- x = autograd.matmul(inputs, self.w0)
- x = autograd.add_bias(x, self.b0)
- x = autograd.relu(x)
- x = autograd.matmul(x, self.w1)
- x = autograd.add_bias(x, self.b1)
- <span class="hljs-keyword">return</span> x
+ 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">loss</span><span class="hljs-params">(self, out, target)</span>:</span> <span class="hljs-comment"># define the loss function</span>
- <span class="hljs-comment"># can use the loss operations provided by SINGA or self-defined function</span>
- <span class="hljs-keyword">return</span> autograd.softmax_cross_entropy(out, target)
+ <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">optim</span><span class="hljs-params">(self, loss)</span>:</span> <span class="hljs-comment"># define the optim function</span>
- <span class="hljs-comment"># can use the optimizer provided by SINGA or self-defined function</span>
- <span class="hljs-keyword">return</span> self.optimizer.backward_and_update(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="training-2"></a><a href="#training-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 [...]
<pre><code class="hljs css language-python"><span class="hljs-comment"># create a model instance</span>
-model = MLP(sgd)
-<span class="hljs-comment"># declare what device to train on</span>
-model.on_device(dev)
-<span class="hljs-comment"># declare execution mode and order</span>
-model.graph(graph, sequential)
+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-keyword">for</span> i <span class="hljs-keyword">in</span> range(niters):
- out = model(inputs)
- loss = model.loss(out, target)
- model.optim(loss)
+<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="save-a-model-checkpoint"></a><a href="#save-a-model-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 [...]
+<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="load-a-model-checkpoint"></a><a href="#load-a-model-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 [...]
+<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-keyword">if</span> i % (niters / <span class="hljs-number">10</span>) == <span class="hljs-number">0</span> <span class="hljs-keyword">and</span> rank_in_global == <span class="hljs-number">0</span>:
- print(<span class="hljs-string">"training loss = "</span>, tensor.to_numpy(loss)[<span class="hljs-number">0</span>], flush=<span class="hljs-literal">True</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 [...]
<p>Refer
<a href="https://singa.readthedocs.io/en/latest/autograd.html#module-singa.autograd">here</a>
for more details of Python API.</p>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/23/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/tensor"><span class="arrow-prev">← </span><span>Tensor</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="#relevant-modules">Relevant Modules</a><ul class="toc-headings"><li><a h [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/27/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/tensor"><span class="arrow-prev">← </span><span>Tensor</span></a><a class="docs-next button" href="/docs/next/optimizer"><span>Optimizer</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#relevant-modules">Relevant Modules</a><ul class="toc-headings"> [...]
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the Apache SINGA project logos are trademarks of The
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index bc7b06f..e414694 100644
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@@ -4,7 +4,7 @@
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index bc7b06f..e414694 100644
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+++ b/content/docs/next/build/index.html
@@ -4,7 +4,7 @@
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)
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index 729e16f..583f650 100644
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+++ b/content/docs/next/contribute-code.html
@@ -4,7 +4,7 @@
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)
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index 729e16f..583f650 100644
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index ff5bedf..684d976 100644
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+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -422,7 +422,7 @@ partitioned, so that each process can evaluate the sub-gradient based on the
partitioned training data. Once the sub-gradient is calculated on each
processes, the overall stochastic gradient is obtained by all-reducing the
sub-gradients evaluated by all processes.</p>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/18/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/onnx"><span class="arrow-prev">← </span><span>ONNX</span></a><a class="docs-next button" href="/docs/next/download-singa"><span>Download SINGA</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#usage">Usage</a><ul class="toc-headings"><li><a href="#ex [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/18/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/onnx"><span class="arrow-prev">← </span><span>ONNX</span></a><a class="docs-next button" href="/docs/next/time-profiling"><span>Time Profiling</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#usage">Usage</a><ul class="toc-headings"><li><a href="#ex [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/dist-train/index.html b/content/docs/next/dist-train/index.html
index a2abdd0..9c5fe3f 100644
--- a/content/docs/next/dist-train/index.html
+++ b/content/docs/next/dist-train/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -422,7 +422,7 @@ partitioned, so that each process can evaluate the sub-gradient based on the
partitioned training data. Once the sub-gradient is calculated on each
processes, the overall stochastic gradient is obtained by all-reducing the
sub-gradients evaluated by all processes.</p>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/18/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/onnx"><span class="arrow-prev">← </span><span>ONNX</span></a><a class="docs-next button" href="/docs/next/download-singa"><span>Download SINGA</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#usage">Usage</a><ul class="toc-headings"><li><a href="#ex [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/18/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/onnx"><span class="arrow-prev">← </span><span>ONNX</span></a><a class="docs-next button" href="/docs/next/time-profiling"><span>Time Profiling</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#usage">Usage</a><ul class="toc-headings"><li><a href="#ex [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/download-singa.html b/content/docs/next/download-singa.html
index 4278bfd..4afc3b9 100644
--- a/content/docs/next/download-singa.html
+++ b/content/docs/next/download-singa.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -243,7 +243,7 @@ models, energy models and RNN models.</li>
<li>Unit test using gtest</li>
</ul></li>
</ul>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/19/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/dist-train"><span class="arrow-prev">← </span><span>Distributed Training</span></a><a class="docs-next button" href="/docs/next/build"><span>Build SINGA from Source</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#verify">Verify</a></li><li><a href= [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/19/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/time-profiling"><span class="arrow-prev">← </span><span>Time Profiling</span></a><a class="docs-next button" href="/docs/next/build"><span>Build SINGA from Source</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#verify">Verify</a></li><li><a href="# [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/download-singa/index.html b/content/docs/next/download-singa/index.html
index 4278bfd..4afc3b9 100644
--- a/content/docs/next/download-singa/index.html
+++ b/content/docs/next/download-singa/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -243,7 +243,7 @@ models, energy models and RNN models.</li>
<li>Unit test using gtest</li>
</ul></li>
</ul>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/19/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/dist-train"><span class="arrow-prev">← </span><span>Distributed Training</span></a><a class="docs-next button" href="/docs/next/build"><span>Build SINGA from Source</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#verify">Verify</a></li><li><a href= [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/19/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/time-profiling"><span class="arrow-prev">← </span><span>Time Profiling</span></a><a class="docs-next button" href="/docs/next/build"><span>Build SINGA from Source</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#verify">Verify</a></li><li><a href="# [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/examples.html b/content/docs/next/examples.html
index de28186..d9fda2a 100644
--- a/content/docs/next/examples.html
+++ b/content/docs/next/examples.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
diff --git a/content/docs/next/examples/index.html b/content/docs/next/examples/index.html
index de28186..d9fda2a 100644
--- a/content/docs/next/examples/index.html
+++ b/content/docs/next/examples/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
diff --git a/content/docs/next/git-workflow.html b/content/docs/next/git-workflow.html
index 23b8644..5257da9 100644
--- a/content/docs/next/git-workflow.html
+++ b/content/docs/next/git-workflow.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
diff --git a/content/docs/next/git-workflow/index.html b/content/docs/next/git-workflow/index.html
index 23b8644..5257da9 100644
--- a/content/docs/next/git-workflow/index.html
+++ b/content/docs/next/git-workflow/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
diff --git a/content/docs/next/graph.html b/content/docs/next/graph.html
index aaddd69..c7dc7b1 100644
--- a/content/docs/next/graph.html
+++ b/content/docs/next/graph.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -122,6 +122,7 @@ optimizations done over the graph.</p>
<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"># initialize device</span>
dev = device.create_cuda_gpu()
@@ -168,14 +169,11 @@ model.compile([tx], is_train=<span class="hljs-literal">True</span>, use_graph=<
as an example. The operation is called in the <code>forward</code> function of the MLP
class</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>
-<<<<<<< HEAD
-=======
<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__()
self.linear1 = layer.Linear(perceptron_size)
...
->>>>>>> upstream/master
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(self, inputs)</span>:</span>
y = self.linear1(inputs)
@@ -561,7 +559,7 @@ memory footprint.</li>
<li>Currently, there is a little improvement in terms of speed. More optimizations
can be done towards the efficiency.</li>
</ul>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/24/2020</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/onnx"><span>ONNX</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#example">Example</a></li><li><a href="#implementation">Implementation [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/28/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/optimizer"><span class="arrow-prev">← </span><span>Optimizer</span></a><a class="docs-next button" href="/docs/next/onnx"><span>ONNX</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#example">Example</a></li><li><a href="#implementation">Implementati [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/graph/index.html b/content/docs/next/graph/index.html
index aaddd69..c7dc7b1 100644
--- a/content/docs/next/graph/index.html
+++ b/content/docs/next/graph/index.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
});
- </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
+ </script><script src="/js/scrollSpy.js"></script><link rel="stylesheet" href="/css/main.css"/><script src="/js/codetabs.js"></script></head><body class="sideNavVisible separateOnPageNav"><div class="fixedHeaderContainer"><div class="headerWrapper wrapper"><header><a href="/"><img class="logo" src="/img/singa.png" alt="Apache SINGA"/></a><a href="/versions"><h3>next</h3></a><div class="navigationWrapper navigationSlider"><nav class="slidingNav"><ul class="nav-site nav-site-interna [...]
var coll = document.getElementsByClassName('collapsible');
var checkActiveCategory = true;
for (var i = 0; i < coll.length; i++) {
@@ -122,6 +122,7 @@ optimizations done over the graph.</p>
<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"># initialize device</span>
dev = device.create_cuda_gpu()
@@ -168,14 +169,11 @@ model.compile([tx], is_train=<span class="hljs-literal">True</span>, use_graph=<
as an example. The operation is called in the <code>forward</code> function of the MLP
class</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>
-<<<<<<< HEAD
-=======
<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__()
self.linear1 = layer.Linear(perceptron_size)
...
->>>>>>> upstream/master
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(self, inputs)</span>:</span>
y = self.linear1(inputs)
@@ -561,7 +559,7 @@ memory footprint.</li>
<li>Currently, there is a little improvement in terms of speed. More optimizations
can be done towards the efficiency.</li>
</ul>
-</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/24/2020</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/onnx"><span>ONNX</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#example">Example</a></li><li><a href="#implementation">Implementation [...]
+</span></div></article></div><div class="docLastUpdate"><em>Last updated on 9/28/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/optimizer"><span class="arrow-prev">← </span><span>Optimizer</span></a><a class="docs-next button" href="/docs/next/onnx"><span>ONNX</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#example">Example</a></li><li><a href="#implementation">Implementati [...]
The Apache Software Foundation. All rights reserved.
Apache SINGA, Apache, the Apache feather logo, and
the Apache SINGA project logos are trademarks of The
diff --git a/content/docs/next/how-to-release.html b/content/docs/next/how-to-release.html
index 556c201..5a6e6f1 100644
--- a/content/docs/next/how-to-release.html
+++ b/content/docs/next/how-to-release.html
@@ -4,7 +4,7 @@
{"zIndex":100}
)
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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 0-2-1.2 [...]
+<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-. [...]
+<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 1 [...]
+<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="#creat [...]
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+ </script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"><a class="edit-page-link button" href="https://github.com/apache/singa-doc/blob/master/docs-site/docs/optimizer.md" target="_blank" rel="noreferrer noopener">Edit</a><h1 id="__docusaurus" class="postHeaderTitle">Optimizer</h1></header><article><div><span><!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor licens [...]
+<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 0-2-1.2 [...]
+<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-. [...]
+<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 1 [...]
+<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="#creat [...]
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