Modified: incubator/singa/site/trunk/docs/model_zoo/index.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/model_zoo/index.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/model_zoo/index.html (original) +++ incubator/singa/site/trunk/docs/model_zoo/index.html Sat Jun 29 14:42:24 2019 @@ -104,6 +104,7 @@ <li class="toctree-l1 current"><a class="reference internal" href="../index.html">Documentation</a><ul class="current"> <li class="toctree-l2"><a class="reference internal" href="../installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="../software_stack.html">Software Stack</a></li> +<li class="toctree-l2"><a class="reference internal" href="../benchmark.html">Benchmark for Distributed training</a></li> <li class="toctree-l2"><a class="reference internal" href="../device.html">Device</a></li> <li class="toctree-l2"><a class="reference internal" href="../tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="../layer.html">Layer</a></li> @@ -121,13 +122,7 @@ <li class="toctree-l2 current"><a class="current reference internal" href="#">Model Zoo</a><ul> <li class="toctree-l3"><a class="reference internal" href="cifar10/README.html">Train CNN over Cifar-10</a></li> <li class="toctree-l3"><a class="reference internal" href="char-rnn/README.html">Train Char-RNN over plain text</a></li> -<li class="toctree-l3"><a class="reference internal" href="mnist/README.html">Train a RBM model against MNIST dataset</a></li> <li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/densenet/README.html">name: DenseNet models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a></li> <li class="toctree-l3"><a class="reference internal" href="imagenet/googlenet/README.html">name: GoogleNet on ImageNet SINGA version: 1.0.1 SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744 @@ -135,24 +130,6 @@ parameter_url: https://s3-ap-southeast-1 parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</a></li> <li class="toctree-l3"><a class="reference internal" href="imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/inception/README.html">name: Inception V4 on ImageNet -SINGA version: 1.1.1 -SINGA commit: -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz -parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 -license: https://github.com/tensorflow/models/tree/master/slim</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/inception/README.html#image-classification-using-inception-v4">Image Classification using Inception V4</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/resnet/README.html">name: Resnets on ImageNet -SINGA version: 1.1 -SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz -license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/vgg/README.html">name: VGG models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li> -<li class="toctree-l3"><a class="reference internal" href="imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a></li> </ul> </li> </ul> @@ -257,10 +234,6 @@ license: https://github.com/pytorch/visi <li class="toctree-l2"><a class="reference internal" href="char-rnn/README.html#instructions">Instructions</a></li> </ul> </li> -<li class="toctree-l1"><a class="reference internal" href="mnist/README.html">Train a RBM model against MNIST dataset</a><ul> -<li class="toctree-l2"><a class="reference internal" href="mnist/README.html#running-instructions">Running instructions</a></li> -</ul> -</li> <li class="toctree-l1"><a class="reference internal" href="imagenet/alexnet/README.html">Train AlexNet over ImageNet</a><ul> <li class="toctree-l2"><a class="reference internal" href="imagenet/alexnet/README.html#instructions">Instructions</a><ul> <li class="toctree-l3"><a class="reference internal" href="imagenet/alexnet/README.html#compile-singa">Compile SINGA</a></li> @@ -271,15 +244,6 @@ license: https://github.com/pytorch/visi </li> </ul> </li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/densenet/README.html">name: DenseNet models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a><ul> -<li class="toctree-l2"><a class="reference internal" href="imagenet/densenet/README.html#instructions">Instructions</a></li> -<li class="toctree-l2"><a class="reference internal" href="imagenet/densenet/README.html#details">Details</a></li> -</ul> -</li> <li class="toctree-l1"><a class="reference internal" href="imagenet/googlenet/README.html">name: GoogleNet on ImageNet SINGA version: 1.0.1 SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744 @@ -291,36 +255,6 @@ license: unrestricted https://github.com <li class="toctree-l2"><a class="reference internal" href="imagenet/googlenet/README.html#details">Details</a></li> </ul> </li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/inception/README.html">name: Inception V4 on ImageNet -SINGA version: 1.1.1 -SINGA commit: -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz -parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 -license: https://github.com/tensorflow/models/tree/master/slim</a></li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/inception/README.html#image-classification-using-inception-v4">Image Classification using Inception V4</a><ul> -<li class="toctree-l2"><a class="reference internal" href="imagenet/inception/README.html#instructions">Instructions</a></li> -<li class="toctree-l2"><a class="reference internal" href="imagenet/inception/README.html#details">Details</a></li> -</ul> -</li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/resnet/README.html">name: Resnets on ImageNet -SINGA version: 1.1 -SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz -license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a><ul> -<li class="toctree-l2"><a class="reference internal" href="imagenet/resnet/README.html#instructions">Instructions</a></li> -<li class="toctree-l2"><a class="reference internal" href="imagenet/resnet/README.html#details">Details</a></li> -</ul> -</li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/vgg/README.html">name: VGG models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li> -<li class="toctree-l1"><a class="reference internal" href="imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a><ul> -<li class="toctree-l2"><a class="reference internal" href="imagenet/vgg/README.html#instructions">Instructions</a></li> -<li class="toctree-l2"><a class="reference internal" href="imagenet/vgg/README.html#details">Details</a></li> -</ul> -</li> </ul> </div> </div>
Modified: incubator/singa/site/trunk/docs/model_zoo/mnist/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/model_zoo/mnist/README.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/model_zoo/mnist/README.html (original) +++ incubator/singa/site/trunk/docs/model_zoo/mnist/README.html Sat Jun 29 14:42:24 2019 @@ -36,8 +36,6 @@ <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> <link rel="index" title="Index" href="../../../genindex.html" /> <link rel="search" title="Search" href="../../../search.html" /> - <link rel="next" title="Train AlexNet over ImageNet" href="../imagenet/alexnet/README.html" /> - <link rel="prev" title="Train Char-RNN over plain text" href="../char-rnn/README.html" /> <link href="../../../_static/style.css" rel="stylesheet" type="text/css"> <!--link href="../../../_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"--> <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css"; integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous"> @@ -100,66 +98,8 @@ - <ul class="current"> -<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Documentation</a><ul class="current"> -<li class="toctree-l2"><a class="reference internal" href="../../installation.html">Installation</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../software_stack.html">Software Stack</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../device.html">Device</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../tensor.html">Tensor</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../layer.html">Layer</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../net.html">FeedForward Net</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../initializer.html">Initializer</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../loss.html">Loss</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../metric.html">Metric</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../optimizer.html">Optimizer</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../autograd.html">Autograd in Singa</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../data.html">Data</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../image_tool.html">Image Tool</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../snapshot.html">Snapshot</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../converter.html">Caffe Converter</a></li> -<li class="toctree-l2"><a class="reference internal" href="../../utils.html">Utils</a></li> -<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Model Zoo</a><ul class="current"> -<li class="toctree-l3"><a class="reference internal" href="../cifar10/README.html">Train CNN over Cifar-10</a></li> -<li class="toctree-l3"><a class="reference internal" href="../char-rnn/README.html">Train Char-RNN over plain text</a></li> -<li class="toctree-l3 current"><a class="current reference internal" href="#">Train a RBM model against MNIST dataset</a><ul> -<li class="toctree-l4"><a class="reference internal" href="#running-instructions">Running instructions</a></li> -</ul> -</li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/alexnet/README.html">Train AlexNet over ImageNet</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html">name: DenseNet models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/densenet/README.html#image-classification-using-densenet">Image Classification using DenseNet</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html">name: GoogleNet on ImageNet -SINGA version: 1.0.1 -SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744 -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz -parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d -license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/googlenet/README.html#image-classification-using-googlenet">Image Classification using GoogleNet</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html">name: Inception V4 on ImageNet -SINGA version: 1.1.1 -SINGA commit: -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz -parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 -license: https://github.com/tensorflow/models/tree/master/slim</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/inception/README.html#image-classification-using-inception-v4">Image Classification using Inception V4</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html">name: Resnets on ImageNet -SINGA version: 1.1 -SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f -parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz -license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/resnet/README.html#image-classification-using-residual-networks">Image Classification using Residual Networks</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html">name: VGG models on ImageNet -SINGA version: 1.1.1 -SINGA commit: -license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py</a></li> -<li class="toctree-l3"><a class="reference internal" href="../imagenet/vgg/README.html#image-classification-using-vgg">Image Classification using VGG</a></li> -</ul> -</li> -</ul> -</li> + <ul> +<li class="toctree-l1"><a class="reference internal" href="../../index.html">Documentation</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../downloads.html">Download SINGA</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../security.html">Security</a></li> </ul> @@ -223,10 +163,6 @@ license: https://github.com/pytorch/visi <li><a href="../../../index.html">Docs</a> »</li> - <li><a href="../../index.html">Documentation</a> »</li> - - <li><a href="../index.html">Model Zoo</a> »</li> - <li>Train a RBM model against MNIST dataset</li> @@ -244,24 +180,7 @@ license: https://github.com/pytorch/visi <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> <div itemprop="articleBody"> - <!-- - Licensed to the Apache Software Foundation (ASF) under one - or more contributor license agreements. See the NOTICE file - distributed with this work for additional information - regarding copyright ownership. The ASF licenses this file - to you under the Apache License, Version 2.0 (the - "License"); you may not use this file except in compliance - with the License. You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, - software distributed under the License is distributed on an - "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - KIND, either express or implied. See the License for the - specific language governing permissions and limitations - under the License. ---><div class="section" id="train-a-rbm-model-against-mnist-dataset"> + <div class="section" id="train-a-rbm-model-against-mnist-dataset"> <h1>Train a RBM model against MNIST dataset<a class="headerlink" href="#train-a-rbm-model-against-mnist-dataset" title="Permalink to this headline">¶</a></h1> <p>This example is to train an RBM model using the MNIST dataset. The RBM model and its hyper-parameters are set following @@ -269,9 +188,8 @@ MNIST dataset. The RBM model and its hyp <div class="section" id="running-instructions"> <h2>Running instructions<a class="headerlink" href="#running-instructions" title="Permalink to this headline">¶</a></h2> <ol> -<li><p class="first">Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz";>MNIST dataset</a></p> -</li> -<li><p class="first">Start the training</p> +<li><p>Download the pre-processed <a class="reference external" href="https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz";>MNIST dataset</a></p></li> +<li><p>Start the training</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">mnist</span><span class="o">.</span><span class="n">pkl</span><span class="o">.</span><span class="n">gz</span> </pre></div> </div> @@ -291,15 +209,6 @@ the program with an additional argument< </div> <footer> - <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> - - <a href="../imagenet/alexnet/README.html" class="btn btn-neutral float-right" title="Train AlexNet over ImageNet" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> - - - <a href="../char-rnn/README.html" class="btn btn-neutral float-left" title="Train Char-RNN over plain text" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> - - </div> - <hr/> Modified: incubator/singa/site/trunk/docs/net.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/net.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/net.html (original) +++ incubator/singa/site/trunk/docs/net.html Sat Jun 29 14:42:24 2019 @@ -104,6 +104,7 @@ <li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current"> <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li> +<li class="toctree-l2"><a class="reference internal" href="benchmark.html">Benchmark for Distributed training</a></li> <li class="toctree-l2"><a class="reference internal" href="device.html">Device</a></li> <li class="toctree-l2"><a class="reference internal" href="tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="layer.html">Layer</a></li> @@ -203,8 +204,267 @@ <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> <div itemprop="articleBody"> - <div class="section" id="feedforward-net"> -<h1>FeedForward Net<a class="headerlink" href="#feedforward-net" title="Permalink to this headline">¶</a></h1> + <div class="section" id="module-singa.net"> +<span id="feedforward-net"></span><h1>FeedForward Net<a class="headerlink" href="#module-singa.net" title="Permalink to this headline">¶</a></h1> +<p>Nerual net class for constructing the nets using layers and providing access +functions for net info, e.g., parameters.</p> +<p>Example usages:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">net</span> <span class="k">as</span> <span class="n">ffnet</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">metric</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">loss</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">layer</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">device</span> + +<span class="c1"># create net and add layers</span> +<span class="n">net</span> <span class="o">=</span> <span class="n">ffnet</span><span class="o">.</span><span class="n">FeedForwardNet</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">(),</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">())</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="s1">'conv1'</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">input_sample_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">,)))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu1'</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="s1">'pool1'</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="s1">'flat'</span><span class="p">))</span> +<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="s1">'dense'</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> + +<span class="c1"># init parameters</span> +<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">param_values</span><span class="p">():</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">p</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">p</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span> + +<span class="c1"># move net onto gpu</span> +<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu</span><span class="p">()</span> +<span class="n">net</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">dev</span><span class="p">)</span> + +<span class="c1"># training (skipped)</span> + +<span class="c1"># do prediction after training</span> +<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">dev</span><span class="p">)</span> +<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">y</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> +<span class="nb">print</span> <span class="n">tensor</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.net.FeedForwardNet"> +<em class="property">class </em><code class="descclassname">singa.net.</code><code class="descname">FeedForwardNet</code><span class="sig-paren">(</span><em>loss=None</em>, <em>metric=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<dl class="method"> +<dt id="singa.net.FeedForwardNet.to_device"> +<code class="descname">to_device</code><span class="sig-paren">(</span><em>dev</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.to_device" title="Permalink to this definition">¶</a></dt> +<dd><p>Move the net onto the given device, including +all parameters and intermediate data.</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.add"> +<code class="descname">add</code><span class="sig-paren">(</span><em>lyr</em>, <em>src=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.add" title="Permalink to this definition">¶</a></dt> +<dd><p>Append a layer into the layer list.</p> +<p>This function will get the sample shape from the src layers to setup the +newly added layer. For the first layer, it is setup outside. The calling +function should ensure the correctness of the layer order. If src is +None, the last layer is the src layer. If there are multiple src layers, +the src is a list of the src layers.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>lyr</strong> (<a class="reference internal" href="layer.html#singa.layer.Layer" title="singa.layer.Layer"><em>Layer</em></a>) â the layer to be added</p></li> +<li><p><strong>src</strong> (<a class="reference internal" href="layer.html#singa.layer.Layer" title="singa.layer.Layer"><em>Layer</em></a>) â the source layer of lyr</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_values"> +<code class="descname">param_values</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_values" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list of tensors for all parameters</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_specs"> +<code class="descname">param_specs</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_specs" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list of ParamSpec for all parameters</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.param_names"> +<code class="descname">param_names</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.param_names" title="Permalink to this definition">¶</a></dt> +<dd><p>Return a list for the names of all params</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.train"> +<code class="descname">train</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.train" title="Permalink to this definition">¶</a></dt> +<dd><p>Run BP for one iteration. +This method is deprecated. It is only kept for backward compatibility. +The name of this method is confusing since it does not update parameters. +Please use backprob() instead. +The back progagation algorithm computes gradients but it does not train.</p> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.backprob"> +<code class="descname">backprob</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.backprob" title="Permalink to this definition">¶</a></dt> +<dd><p>Run BP for one iteration.</p> +<p>Currently only support nets with a single output layer, and a single +loss objective and metric. +For multiple outputs (with multiple loss/metric), please manually +call forward, compute loss/metric and call backward. backward() is also +more memory efficient than this function.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> â input data, a single input Tensor or a dict: layer name -> Tensor</p></li> +<li><p><strong>y</strong> â label data, a single input Tensor.</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>gradients of parameters and the loss and metric values.</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.evaluate"> +<code class="descname">evaluate</code><span class="sig-paren">(</span><em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.evaluate" title="Permalink to this definition">¶</a></dt> +<dd><p>Evaluate the loss and metric of the given data.</p> +<p>Currently only support nets with a single output layer, and a single +loss objective and metric. +TODO(wangwei) consider multiple loss objectives and metrics.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>x</strong> â input data, a single input Tensor or a dict: layer name -> Tensor</p></li> +<li><p><strong>y</strong> â label data, a single input Tensor.</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.predict"> +<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.predict" title="Permalink to this definition">¶</a></dt> +<dd><p>Forward the input data through each layer to get the values of the +output layers.</p> +<p>Currently only support nets with a single output layer</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>x</strong> â input data, a single input Tensor or a dict: layer name -> Tensor</p> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a single output tensor as the prediction result.</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.topo_sort"> +<code class="descname">topo_sort</code><span class="sig-paren">(</span><em>layers</em>, <em>src_of_layer</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.topo_sort" title="Permalink to this definition">¶</a></dt> +<dd><p>Topology sort of layers.</p> +<p>It would try to preserve the orders of the input layers.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>layers</strong> â a list of layers; the layers from the output of the same +layer (e.g., slice layer) should be added by users in correct +order; This function would not change their order.</p></li> +<li><p><strong>src_of_layer</strong> â a dictionary: src layer name -> a list of src layers</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>A list of ordered layer</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.forward"> +<code class="descname">forward</code><span class="sig-paren">(</span><em>flag</em>, <em>x</em>, <em>output=[]</em>, <em>freeze=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.forward" title="Permalink to this definition">¶</a></dt> +<dd><p>Forward the input(s) through every layer.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>flag</strong> â True for training; False for evaluation; could also be +model_pb2.kTrain or model_pb2.kEval, or other values for future +use.</p></li> +<li><p><strong>x</strong> â a single SINGA tensor if there is a single input; otherwise, a +dictionary: layer name-> singa tensor, for each layer accepting +input data. Do not associate a layer with input tensor if it is +connected from another layer. For such case, use a Dummy() layer +to accept the input data and connect the dummy layer to this +layer.</p></li> +<li><p><strong>output</strong> (<em>list</em>) â a list of layer names whose output would be returned +in addition to the default output.</p></li> +<li><p><strong>freeze</strong> (<em>str</em>) â layer name, freeze all layers before this layer; flag +is set to false for these layers.</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p><dl class="simple"> +<dt>if there is only one output layer and output arg is empty, return</dt><dd><p>the result from the single output layer; otherwise, return a +dictionary: layer name -> output tensor(s)</p> +</dd> +</dl> +</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.backward"> +<code class="descname">backward</code><span class="sig-paren">(</span><em>dy</em>, <em>output=[]</em>, <em>freeze=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.backward" title="Permalink to this definition">¶</a></dt> +<dd><p>Run back-propagation after forward-propagation.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>dy</strong> â a single tensor if there is a single loss function; otherwise, +a dictionary maps the name of the layer connecting to the loss +function -> gradient from the loss function. Do not associate a +layer with gradient tensor if it is connecting to another layer. +For such case, connect this layer to a Dummy() layer and use the +dummy layer to accept the gradient.</p></li> +<li><p><strong>output</strong> (<em>list</em>) â a list of layer names whose output gradient would be +returned in addition to the param gradient</p></li> +<li><p><strong>freeze</strong> (<em>str</em>) â layer name, stop backward after this layer.</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>a geneartor iterator that generates +(param_names, param_values, param_grads, layer_grads) after +processing each layer h, where the first three lists are for h +and the last item is a dictionary which maps +layer name -> its output gradient tensor(s). At the end of this +function, the key set includes all layers in the output arg.</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.save"> +<code class="descname">save</code><span class="sig-paren">(</span><em>f</em>, <em>buffer_size=10</em>, <em>use_pickle=False</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.save" title="Permalink to this definition">¶</a></dt> +<dd><p>Save model parameters using io/snapshot.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>f</strong> â file name</p></li> +<li><p><strong>buffer_size</strong> â size (MB) of the IO, default setting is 10MB; Please +make sure it is larger than any single parameter object.</p></li> +<li><p><strong>use_pickle</strong> (<em>Boolean</em>) â if true, it would use pickle for dumping; +otherwise, it would use protobuf for serialization, which uses +less space.</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.net.FeedForwardNet.load"> +<code class="descname">load</code><span class="sig-paren">(</span><em>f</em>, <em>buffer_size=10</em>, <em>use_pickle=False</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.net.FeedForwardNet.load" title="Permalink to this definition">¶</a></dt> +<dd><p>Load model parameters using io/snapshot.</p> +<p>Please refer to the argument description in save().</p> +</dd></dl> + +</dd></dl> + </div> Modified: incubator/singa/site/trunk/docs/neural-net.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/neural-net.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/neural-net.html (original) +++ incubator/singa/site/trunk/docs/neural-net.html Sat Jun 29 14:42:24 2019 @@ -331,7 +331,7 @@ partitioned into two sub-layers.</p> <h3>Partitioning configuration<a class="headerlink" href="#partitioning-configuration" title="Permalink to this headline">¶</a></h3> <p>There are 4 partitioning schemes, whose configurations are give below,</p> <ol> -<li><p class="first">Partitioning each singe layer into sub-layers on batch dimension (see +<li><p>Partitioning each singe layer into sub-layers on batch dimension (see below). It is enabled by configuring the partition dimension of the layer to 0, e.g.,</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> @@ -341,7 +341,7 @@ below). It is enabled by configuring the </pre></div> </div> </li> -<li><p class="first">Partitioning each singe layer into sub-layers on feature dimension (see +<li><p>Partitioning each singe layer into sub-layers on feature dimension (see below). It is enabled by configuring the partition dimension of the layer to 1, e.g.,</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> @@ -351,7 +351,7 @@ below). It is enabled by configuring th </pre></div> </div> </li> -<li><p class="first">Partitioning all layers into different subsets. It is enabled by +<li><p>Partitioning all layers into different subsets. It is enabled by configuring the location ID of a layer, e.g.,</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> <span class="n">layer</span> <span class="p">{</span> @@ -363,7 +363,7 @@ configuring the location ID of a layer, </pre></div> </div> </li> -<li><p class="first">Hybrid partitioning of strategy 1, 2 and 3. The hybrid partitioning is +<li><p>Hybrid partitioning of strategy 1, 2 and 3. The hybrid partitioning is useful for large models. An example application is to implement the <a class="reference external" href="http://arxiv.org/abs/1404.5997";>idea proposed by Alex</a>. Hybrid partitioning is configured like,</p> @@ -394,16 +394,16 @@ are under test and will be released in l <h2>Parameter sharing<a class="headerlink" href="#parameter-sharing" title="Permalink to this headline">¶</a></h2> <p>Parameters can be shared in two cases,</p> <ul class="simple"> -<li>sharing parameters among layers via user configuration. For example, the +<li><p>sharing parameters among layers via user configuration. For example, the visible layer and hidden layer of a RBM shares the weight matrix, which is configured through the <code class="docutils literal notranslate"><span class="pre">share_from</span></code> field as shown in the above RBM configuration. The -configurations must be the same (except name) for shared parameters.</li> -<li>due to neural net partitioning, some <code class="docutils literal notranslate"><span class="pre">Param</span></code> objects are replicated into +configurations must be the same (except name) for shared parameters.</p></li> +<li><p>due to neural net partitioning, some <code class="docutils literal notranslate"><span class="pre">Param</span></code> objects are replicated into different workers, e.g., partitioning one layer on batch dimension. These workers share parameter values. SINGA controls this kind of parameter -sharing automatically, users do not need to do any configuration.</li> -<li>the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers -, thus share <code class="docutils literal notranslate"><span class="pre">Param</span></code> values.</li> +sharing automatically, users do not need to do any configuration.</p></li> +<li><p>the <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers +, thus share <code class="docutils literal notranslate"><span class="pre">Param</span></code> values.</p></li> </ul> <p>If the shared <code class="docutils literal notranslate"><span class="pre">Param</span></code> instances resident in the same process (may in different threads), they use the same chunk of memory space for their values. But they Modified: incubator/singa/site/trunk/docs/optimizer.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/optimizer.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/optimizer.html (original) +++ incubator/singa/site/trunk/docs/optimizer.html Sat Jun 29 14:42:24 2019 @@ -104,6 +104,7 @@ <li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current"> <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li> +<li class="toctree-l2"><a class="reference internal" href="benchmark.html">Benchmark for Distributed training</a></li> <li class="toctree-l2"><a class="reference internal" href="device.html">Device</a></li> <li class="toctree-l2"><a class="reference internal" href="tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="layer.html">Layer</a></li> @@ -203,8 +204,403 @@ <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> <div itemprop="articleBody"> - <div class="section" id="optimizer"> -<h1>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline">¶</a></h1> + <div class="section" id="module-singa.optimizer"> +<span id="optimizer"></span><h1>Optimizer<a class="headerlink" href="#module-singa.optimizer" title="Permalink to this headline">¶</a></h1> +<p>This module includes a set of optimizers for updating model parameters.</p> +<p>Example usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">optimizer</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span> + +<span class="n">sgd</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span> +<span class="n">p</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> +<span class="n">p</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">g</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> +<span class="n">g</span><span class="o">.</span><span class="n">gaussian</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span> + +<span class="n">sgd</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use the global lr=0.1 for epoch 1</span> +<span class="n">sgd</span><span class="o">.</span><span class="n">apply_with_lr</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="s1">'param'</span><span class="p">)</span> <span class="c1"># use lr=0.03 for epoch 2</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.optimizer.Optimizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Optimizer</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>The base python optimizer class.</p> +<p>Typically, an optimizer is used as follows:</p> +<ol class="arabic simple"> +<li><p>construct the optimizer</p></li> +<li><p>(optional) register each parameter with its specs.</p></li> +<li><p>use the optimizer to update parameter values given parameter gradients +and other optional info</p></li> +</ol> +<p>The subclasses should override the apply_with_lr function to do the real +parameter udpate.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>lr</strong> (<em>float</em>) â a constant value for the learning rate</p></li> +<li><p><strong>momentum</strong> (<em>float</em>) â a constant value for the momentum value</p></li> +<li><p><strong>weight_decay</strong> (<em>float</em>) â the coefficent for L2 regularizer, which is +mutually exclusive with âregularizerâ.</p></li> +<li><p><strong>regularizer</strong> â an instance of Regularizer or RegularizerConf; If set, +regularization would be applied in apply_with_lr(). +Users can also do regularization outside.</p></li> +<li><p><strong>constraint</strong> â an instance of Constraint or ConstraintConf; If set, +constraint would be applied inside apply_with_lr(). Users can +also apply constraint outside.</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.Optimizer.register"> +<code class="descname">register</code><span class="sig-paren">(</span><em>name</em>, <em>specs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.register" title="Permalink to this definition">¶</a></dt> +<dd><p>Register the param specs, including creating regularizer and +constraint per param object. Param specific regularizer and constraint +have higher priority than the global ones. If all parameters share the +same setting for learning rate, regularizer and constraint, then there +is no need to call this function.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>name</strong> (<em>str</em>) â parameter name</p></li> +<li><p><strong>specs</strong> (<em>ParamSpec</em>) â protobuf obj, including regularizer and +constraint, multipliers for learning rate and weight decay.</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply_regularizer_constraint"> +<code class="descname">apply_regularizer_constraint</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_regularizer_constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Apply regularization and constraint if available.</p> +<p>If there are both global regularizer (constraint) and param specific +regularizer (constraint), it would use the param specific one.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>value</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter value Tensor</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient Tensor</p></li> +<li><p><strong>name</strong> (<em>string</em>) â to get parameter specific regularizer or constraint</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>the updated gradient Tensor</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.optimizer.Optimizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>grad</em>, <em>value</em>, <em>name=None</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Optimizer.apply" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update assuming the learning rate generator is set.</p> +<p>The subclass optimizer does not need to override this function.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to retrieval parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â training iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.SGD"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">SGD</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>The vallina Stochasitc Gradient Descent algorithm with momentum.</p> +<p>See the base Optimizer for all arguments.</p> +<dl class="method"> +<dt id="singa.optimizer.SGD.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.SGD.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Nesterov"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Nesterov</code><span class="sig-paren">(</span><em>lr=None</em>, <em>momentum=0.9</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>The SGD with Nesterov momentum.</p> +<p>See the base Optimizer for all arguments.</p> +<dl class="method"> +<dt id="singa.optimizer.Nesterov.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Nesterov.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.RMSProp"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">RMSProp</code><span class="sig-paren">(</span><em>rho=0.9</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>RMSProp optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>rho</strong> (<em>float</em>) â float within [0, 1]</p></li> +<li><p><strong>epsilon</strong> (<em>float</em>) â small value for preventing numeric error</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.RMSProp.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.RMSProp.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.AdaGrad"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">AdaGrad</code><span class="sig-paren">(</span><em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>lr_gen=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>AdaGrad optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>epsilon</strong> (<em>float</em>) â small number for preventing numeric error.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.AdaGrad.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.AdaGrad.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Do update of parameters with given learning rate if the grad is not +empty.</p> +<p>The subclass optimizer must override this function. +This function do nothing if the grad is empty.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>epoch</strong> (<em>int</em>) â training epoch ID</p></li> +<li><p><strong>lr</strong> (<em>float</em>) â learning rate</p></li> +<li><p><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â parameter gradient</p></li> +<li><p><strong>value</strong> (<em>Tesnor</em>) â parameter value</p></li> +<li><p><strong>name</strong> (<em>string</em>) â paramter name to index parameter specific +updating rules (including regularizer and constraint)</p></li> +<li><p><strong>step</strong> (<em>int</em>) â iteration ID within one epoch</p></li> +</ul> +</dd> +<dt class="field-even">Returns</dt> +<dd class="field-even"><p>updated parameter value</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Adam"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Adam</code><span class="sig-paren">(</span><em>beta_1=0.9</em>, <em>beta_2=0.999</em>, <em>epsilon=1e-08</em>, <em>lr=None</em>, <em>weight_decay=None</em>, <em>regularizer=None</em>, <em>constraint=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Optimizer" title="singa.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>Adam optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>beta_1</strong> (<em>float</em>) â coefficient of momentum</p></li> +<li><p><strong>beta_2</strong> (<em>float</em>) â coefficient of aggregated squared gradient</p></li> +<li><p><strong>epsilon</strong> (<em>float</em>) â small value for preventing numeric error</p></li> +</ul> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.Adam.apply_with_lr"> +<code class="descname">apply_with_lr</code><span class="sig-paren">(</span><em>epoch</em>, <em>lr</em>, <em>grad</em>, <em>value</em>, <em>name</em>, <em>step</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Adam.apply_with_lr" title="Permalink to this definition">¶</a></dt> +<dd><p>Update one parameter object.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>step</strong> (<em>int</em>) â the accumulated training iterations, not the iteration ID</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Regularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Regularizer</code><a class="headerlink" href="#singa.optimizer.Regularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base Python regularizer for parameter gradients.</p> +<dl class="method"> +<dt id="singa.optimizer.Regularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Regularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.CppRegularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppRegularizer</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>Wrapper for regularizer implemented using C++.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>conf</strong> (<em>RegularizerConf</em>) â protobuf message for the configuration.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.CppRegularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppRegularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.L2Regularizer"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Regularizer</code><span class="sig-paren">(</span><em>coefficient</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Regularizer" title="singa.optimizer.Regularizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>L2 regularization</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>coefficient</strong> (<em>float</em>) â regularization coefficient.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.L2Regularizer.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Regularizer.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.Constraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">Constraint</code><a class="headerlink" href="#singa.optimizer.Constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Base Python constraint class for paramter gradients</p> +<dl class="method"> +<dt id="singa.optimizer.Constraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.Constraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.CppConstraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">CppConstraint</code><span class="sig-paren">(</span><em>conf</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Constraint</span></code></a></p> +<p>Wrapper for constraints implemented using C++.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><p><strong>conf</strong> (<em>ConstraintConf</em>) â protobuf message for the configuration.</p> +</dd> +</dl> +<dl class="method"> +<dt id="singa.optimizer.CppConstraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.CppConstraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="class"> +<dt id="singa.optimizer.L2Constraint"> +<em class="property">class </em><code class="descclassname">singa.optimizer.</code><code class="descname">L2Constraint</code><span class="sig-paren">(</span><em>threshold=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint" title="Permalink to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="#singa.optimizer.Constraint" title="singa.optimizer.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">singa.optimizer.Constraint</span></code></a></p> +<p>Rescale the gradient to make the L2 norm <= a given threshold</p> +<dl class="method"> +<dt id="singa.optimizer.L2Constraint.apply"> +<code class="descname">apply</code><span class="sig-paren">(</span><em>epoch</em>, <em>value</em>, <em>grad</em>, <em>step=-1</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.optimizer.L2Constraint.apply" title="Permalink to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + </div> Modified: incubator/singa/site/trunk/docs/snapshot.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/snapshot.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/snapshot.html (original) +++ incubator/singa/site/trunk/docs/snapshot.html Sat Jun 29 14:42:24 2019 @@ -104,6 +104,7 @@ <li class="toctree-l1 current"><a class="reference internal" href="index.html">Documentation</a><ul class="current"> <li class="toctree-l2"><a class="reference internal" href="installation.html">Installation</a></li> <li class="toctree-l2"><a class="reference internal" href="software_stack.html">Software Stack</a></li> +<li class="toctree-l2"><a class="reference internal" href="benchmark.html">Benchmark for Distributed training</a></li> <li class="toctree-l2"><a class="reference internal" href="device.html">Device</a></li> <li class="toctree-l2"><a class="reference internal" href="tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="layer.html">Layer</a></li> @@ -203,8 +204,51 @@ <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> <div itemprop="articleBody"> - <div class="section" id="snapshot"> -<h1>Snapshot<a class="headerlink" href="#snapshot" title="Permalink to this headline">¶</a></h1> + <div class="section" id="module-singa.snapshot"> +<span id="snapshot"></span><h1>Snapshot<a class="headerlink" href="#module-singa.snapshot" title="Permalink to this headline">¶</a></h1> +<p>This script includes io::snapshot class and its methods.</p> +<p>Example usages:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">snapshot</span> + +<span class="n">sn1</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> +<span class="n">params</span> <span class="o">=</span> <span class="n">sn1</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="c1"># read all params as a dictionary</span> + +<span class="n">sn2</span> <span class="o">=</span> <span class="n">snapshot</span><span class="o">.</span><span class="n">Snapshot</span><span class="p">(</span><span class="s1">'param_new'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> +<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">iteritems</span><span class="p">():</span> + <span class="n">sn2</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.snapshot.Snapshot"> +<em class="property">class </em><code class="descclassname">singa.snapshot.</code><code class="descname">Snapshot</code><span class="sig-paren">(</span><em>f</em>, <em>mode</em>, <em>buffer_size=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot" title="Permalink to this definition">¶</a></dt> +<dd><p>Class and member functions for singa::Snapshot.</p> +<dl class="method"> +<dt id="singa.snapshot.Snapshot.read"> +<code class="descname">read</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.read" title="Permalink to this definition">¶</a></dt> +<dd><p>Call read method to load all (param_name, param_val)</p> +<dl class="field-list simple"> +<dt class="field-odd">Returns</dt> +<dd class="field-odd"><p>a dict of (parameter name, parameter Tensor)</p> +</dd> +</dl> +</dd></dl> + +<dl class="method"> +<dt id="singa.snapshot.Snapshot.write"> +<code class="descname">write</code><span class="sig-paren">(</span><em>param_name</em>, <em>param_val</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.snapshot.Snapshot.write" title="Permalink to this definition">¶</a></dt> +<dd><p>Call Write method to write a parameter</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>param_name</strong> (<em>string</em>) â name of the parameter</p></li> +<li><p><strong>param_val</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â value tensor of the parameter</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +</dd></dl> + </div> Modified: incubator/singa/site/trunk/docs/software_stack.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/docs/software_stack.html?rev=1862313&r1=1862312&r2=1862313&view=diff ============================================================================== --- incubator/singa/site/trunk/docs/software_stack.html (original) +++ incubator/singa/site/trunk/docs/software_stack.html Sat Jun 29 14:42:24 2019 @@ -36,7 +36,7 @@ <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> <link rel="index" title="Index" href="../genindex.html" /> <link rel="search" title="Search" href="../search.html" /> - <link rel="next" title="Device" href="device.html" /> + <link rel="next" title="Benchmark for Distributed training" href="benchmark.html" /> <link rel="prev" title="Installation" href="installation.html" /> <link href="../_static/style.css" rel="stylesheet" type="text/css"> <!--link href="../_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"--> @@ -109,6 +109,7 @@ <li class="toctree-l3"><a class="reference internal" href="#io">IO</a></li> </ul> </li> +<li class="toctree-l2"><a class="reference internal" href="benchmark.html">Benchmark for Distributed training</a></li> <li class="toctree-l2"><a class="reference internal" href="device.html">Device</a></li> <li class="toctree-l2"><a class="reference internal" href="tensor.html">Tensor</a></li> <li class="toctree-l2"><a class="reference internal" href="layer.html">Layer</a></li> @@ -249,19 +250,19 @@ tensor operations on its execution units <p>Depending on the hardware and the programming language, SINGA have implemented the following specific device classes:</p> <ul class="simple"> -<li><strong>CudaGPU</strong> represents an Nvidia GPU card. The execution units are the CUDA streams.</li> -<li><strong>CppCPU</strong> represents a normal CPU. The execution units are the CPU threads.</li> -<li><strong>OpenclGPU</strong> represents normal GPU card from both Nvidia and AMD. +<li><p><strong>CudaGPU</strong> represents an Nvidia GPU card. The execution units are the CUDA streams.</p></li> +<li><p><strong>CppCPU</strong> represents a normal CPU. The execution units are the CPU threads.</p></li> +<li><p><strong>OpenclGPU</strong> represents normal GPU card from both Nvidia and AMD. The execution units are the CommandQueues. Given that OpenCL is compatible with many hardware devices, e.g. FPGA and ARM, the OpenclGPU has the potential to be -extended for other devices.</li> +extended for other devices.</p></li> </ul> <p>Different types of devices use different programming languages to write the kernel functions for tensor operations,</p> <ul class="simple"> -<li>CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for CppCPU</li> -<li>CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for CudaGPU</li> -<li>OpenclMath (tensor_math_opencl.h) implements the tensor operations using OpenCL for OpenclGPU</li> +<li><p>CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for CppCPU</p></li> +<li><p>CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for CudaGPU</p></li> +<li><p>OpenclMath (tensor_math_opencl.h) implements the tensor operations using OpenCL for OpenclGPU</p></li> </ul> <p>In addition, different types of data, such as float32 and float16, could be supported by adding the corresponding tensor functions.</p> @@ -285,31 +286,31 @@ would be described in the <a class="refe <p>On top of the Tensor and Device abstractions, SINGA provides some higher level classes for machine learning modules.</p> <ul class="simple"> -<li><a class="reference external" href="layer.html">Layer</a> and its subclasses are specific for neural networks. Every layer provides +<li><p><a class="reference external" href="layer.html">Layer</a> and its subclasses are specific for neural networks. Every layer provides functions for forward propagating features and backward propagating gradients w.r.t the training loss functions. They wraps the complex layer operations so that users can easily create neural nets -by connecting a set of layers.</li> -<li><a class="reference external" href="initializer.html">Initializer</a> and its subclasses provide variant methods of initializing -model parameters (stored in Tensor instances), following Uniform, Gaussian, etc.</li> -<li><a class="reference external" href="loss.html">Loss</a> and its subclasses defines the training objective loss functions. +by connecting a set of layers.</p></li> +<li><p><a class="reference external" href="initializer.html">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 class="reference external" href="loss.html">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.</li> -<li><a class="reference external" href="metric.html">Metric</a> and its subclasses provide the function to measure the -performance of the model, e.g., the accuracy.</li> -<li><a class="reference external" href="optimizer.html">Optimizer</a> and its subclasses implement the methods for updating -model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</li> +objective loss are implemented. Example loss functions include squared error and cross entropy.</p></li> +<li><p><a class="reference external" href="metric.html">Metric</a> and its subclasses provide the function to measure the +performance of the model, e.g., the accuracy.</p></li> +<li><p><a class="reference external" href="optimizer.html">Optimizer</a> and its subclasses implement the methods for updating +model parameter values using parameter gradients, including SGD, AdaGrad, RMSProp etc.</p></li> </ul> </div> <div class="section" id="io"> <h2>IO<a class="headerlink" href="#io" title="Permalink to this headline">¶</a></h2> <p>The IO module consists of classes for data loading, data preprocessing and message passing.</p> <ul class="simple"> -<li>Reader and its subclasses load string records from disk files</li> -<li>Writer and its subclasses write string records to disk files</li> -<li>Encoder and its subclasses encode Tensor instances into string records</li> -<li>Decoder and its subclasses decodes string records into Tensor instances</li> -<li>Endpoint represents a communication endpoint which provides functions for passing messages to each other.</li> -<li>Message represents communication messages between Endpoint instances. It carries both meta data and payload.</li> +<li><p>Reader and its subclasses load string records from disk files</p></li> +<li><p>Writer and its subclasses write string records to disk files</p></li> +<li><p>Encoder and its subclasses encode Tensor instances into string records</p></li> +<li><p>Decoder and its subclasses decodes string records into Tensor instances</p></li> +<li><p>Endpoint represents a communication endpoint which provides functions for passing messages to each other.</p></li> +<li><p>Message represents communication messages between Endpoint instances. It carries both meta data and payload.</p></li> </ul> </div> </div> @@ -322,7 +323,7 @@ model parameter values using parameter g <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> - <a href="device.html" class="btn btn-neutral float-right" title="Device" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + <a href="benchmark.html" class="btn btn-neutral float-right" title="Benchmark for Distributed training" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> <a href="installation.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
