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As the +neural net typically consists of a set of layers, <code class="docutils literal"><span class="pre">NeuralNet</span></code> comprises +a set of unidirectionally connected <a class="reference external" href="layer.html">Layer</a>s. +This page describes how to convert an user’s neural net into +the configuration of <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p> +<p><img src="../_static/images/model-category.png" align="center" width="200px"/> +<span><strong>Figure 1 - Categorization of popular deep learning models.</strong></span></p> +<div class="section" id="net-structure-configuration"> +<span id="net-structure-configuration"></span><h2>Net structure configuration<a class="headerlink" href="#net-structure-configuration" title="Permalink to this headline">¶</a></h2> +<p>Users configure the <code class="docutils literal"><span class="pre">NeuralNet</span></code> by listing all layers of the neural net and +specifying each layer’s source layer names. Popular deep learning models can be +categorized as Figure 1. The subsequent sections give details for each +category.</p> +<div class="section" id="feed-forward-models"> +<span id="feed-forward-models"></span><h3>Feed-forward models<a class="headerlink" href="#feed-forward-models" title="Permalink to this headline">¶</a></h3> +<div align = "left"> +<img src="../_static/images/mlp-net.png" align="center" width="200px"/> +<span><strong>Figure 2 - Net structure of a MLP model.</strong></span> +</div><p>Feed-forward models, e.g., CNN and MLP, can easily get configured as their layer +connections are undirected without circles. The +configuration for the MLP model shown in Figure 1 is as follows,</p> +<div class="highlight-python"><div class="highlight"><pre>net { + layer { + name : 'data" + type : kData + } + layer { + name : 'image" + type : kImage + srclayer: 'data' + } + layer { + name : 'label" + type : kLabel + srclayer: 'data' + } + layer { + name : 'hidden" + type : kHidden + srclayer: 'image' + } + layer { + name : 'softmax" + type : kSoftmaxLoss + srclayer: 'hidden' + srclayer: 'label' + } +} +</pre></div> +</div> +</div> +<div class="section" id="energy-models"> +<span id="energy-models"></span><h3>Energy models<a class="headerlink" href="#energy-models" title="Permalink to this headline">¶</a></h3> +<p><img src="../_static/images/rbm-rnn.png" align="center" width="500px"/> +<span><strong>Figure 3 - Convert connections in RBM and RNN.</strong></span></p> +<p>For energy models including RBM, DBM, +etc., their connections are undirected (i.e., Category B). To represent these models using +<code class="docutils literal"><span class="pre">NeuralNet</span></code>, users can simply replace each connection with two directed +connections, as shown in Figure 3a. In other words, for each pair of connected layers, their source +layer field should include each other’s name. +The full <a class="reference external" href="rbm.html">RBM example</a> has +detailed neural net configuration for a RBM model, which looks like</p> +<div class="highlight-python"><div class="highlight"><pre>net { + layer { + name : "vis" + type : kVisLayer + param { + name : "w1" + } + srclayer: "hid" + } + layer { + name : "hid" + type : kHidLayer + param { + name : "w2" + share_from: "w1" + } + srclayer: "vis" + } +} +</pre></div> +</div> +</div> +<div class="section" id="rnn-models"> +<span id="rnn-models"></span><h3>RNN models<a class="headerlink" href="#rnn-models" title="Permalink to this headline">¶</a></h3> +<p>For recurrent neural networks (RNN), users can remove the recurrent connections +by unrolling the recurrent layer. For example, in Figure 3b, the original +layer is unrolled into a new layer with 4 internal layers. In this way, the +model is like a normal feed-forward model, thus can be configured similarly. +The <a class="reference external" href="rnn.html">RNN example</a> has a full neural net +configuration for a RNN model.</p> +</div> +</div> +<div class="section" id="configuration-for-multiple-nets"> +<span id="configuration-for-multiple-nets"></span><h2>Configuration for multiple nets<a class="headerlink" href="#configuration-for-multiple-nets" title="Permalink to this headline">¶</a></h2> +<p>Typically, a training job includes three neural nets for +training, validation and test phase respectively. The three neural nets share most +layers except the data layer, loss layer or output layer, etc.. To avoid +redundant configurations for the shared layers, users can uses the <code class="docutils literal"><span class="pre">exclude</span></code> +filed to filter a layer in the neural net, e.g., the following layer will be +filtered when creating the testing <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p> +<div class="highlight-python"><div class="highlight"><pre>layer { + ... + exclude : kTest # filter this layer for creating test net +} +</pre></div> +</div> +</div> +<div class="section" id="neural-net-partitioning"> +<span id="neural-net-partitioning"></span><h2>Neural net partitioning<a class="headerlink" href="#neural-net-partitioning" title="Permalink to this headline">¶</a></h2> +<p>A neural net can be partitioned in different ways to distribute the training +over multiple workers.</p> +<div class="section" id="batch-and-feature-dimension"> +<span id="batch-and-feature-dimension"></span><h3>Batch and feature dimension<a class="headerlink" href="#batch-and-feature-dimension" title="Permalink to this headline">¶</a></h3> +<p><img src="../_static/images/partition_fc.png" align="center" width="400px"/> +<span><strong>Figure 4 - Partitioning of a fully connected layer.</strong></span></p> +<p>Every layer’s feature blob is considered a matrix whose rows are feature +vectors. Thus, one layer can be split on two dimensions. Partitioning on +dimension 0 (also called batch dimension) slices the feature matrix by rows. +For instance, if the mini-batch size is 256 and the layer is partitioned into 2 +sub-layers, each sub-layer would have 128 feature vectors in its feature blob. +Partitioning on this dimension has no effect on the parameters, as every +<a class="reference external" href="param.html">Param</a> object is replicated in the sub-layers. Partitioning on dimension +1 (also called feature dimension) slices the feature matrix by columns. For +example, suppose the original feature vector has 50 units, after partitioning +into 2 sub-layers, each sub-layer would have 25 units. This partitioning may +result in <a class="reference external" href="param.html">Param</a> object being split, as shown in +Figure 4. Both the bias vector and weight matrix are +partitioned into two sub-layers.</p> +</div> +<div class="section" id="partitioning-configuration"> +<span id="partitioning-configuration"></span><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 +below). It is enabled by configuring the partition dimension of the layer to +0, e.g.,</p> +<div class="highlight-python"><div class="highlight"><pre> # with other fields omitted + layer { + partition_dim: 0 + } +</pre></div> +</div> +</li> +<li><p class="first">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-python"><div class="highlight"><pre> # with other fields omitted + layer { + partition_dim: 1 + } +</pre></div> +</div> +</li> +<li><p class="first">Partitioning all layers into different subsets. It is enabled by +configuring the location ID of a layer, e.g.,</p> +<div class="highlight-python"><div class="highlight"><pre> # with other fields omitted + layer { + location: 1 + } + layer { + location: 0 + } +</pre></div> +</div> +</li> +</ol> +<ol> +<li><p class="first">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> +<div class="highlight-python"><div class="highlight"><pre> # with other fields omitted + layer { + location: 1 + } + layer { + location: 0 + } + layer { + partition_dim: 0 + location: 0 + } + layer { + partition_dim: 1 + location: 0 + } +</pre></div> +</div> +</li> +</ol> +<p>Currently SINGA supports strategy-2 well. Other partitioning strategies are +are under test and will be released in later version.</p> +</div> +</div> +<div class="section" id="parameter-sharing"> +<span id="parameter-sharing"></span><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 +visible layer and hidden layer of a RBM shares the weight matrix, which is configured through +the <code class="docutils literal"><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"><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"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers +, thus share <code class="docutils literal"><span class="pre">Param</span></code> values.</li> +</ul> +<p>If the shared <code class="docutils literal"><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 +would have different memory spaces for their gradients. In fact, their +gradients will be averaged by the stub or server.</p> +</div> +<div class="section" id="advanced-user-guide"> +<span id="advanced-user-guide"></span><h2>Advanced user guide<a class="headerlink" href="#advanced-user-guide" title="Permalink to this headline">¶</a></h2> +<div class="section" id="creation"> +<span id="creation"></span><h3>Creation<a class="headerlink" href="#creation" title="Permalink to this headline">¶</a></h3> +<div class="highlight-python"><div class="highlight"><pre>static NeuralNet* NeuralNet::Create(const NetProto& np, Phase phase, int num); +</pre></div> +</div> +<p>The above function creates a <code class="docutils literal"><span class="pre">NeuralNet</span></code> for a given phase, and returns a +pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance. The phase is in {kTrain, +kValidation, kTest}. <code class="docutils literal"><span class="pre">num</span></code> is used for net partitioning which indicates the +number of partitions. Typically, a training job includes three neural nets for +training, validation and test phase respectively. The three neural nets share most +layers except the data layer, loss layer or output layer, etc.. The <code class="docutils literal"><span class="pre">Create</span></code> +function takes in the full net configuration including layers for training, +validation and test. It removes layers for phases other than the specified +phase based on the <code class="docutils literal"><span class="pre">exclude</span></code> field in +<a class="reference external" href="layer.html">layer configuration</a>:</p> +<div class="highlight-python"><div class="highlight"><pre>layer { + ... + exclude : kTest # filter this layer for creating test net +} +</pre></div> +</div> +<p>The filtered net configuration is passed to the constructor of <code class="docutils literal"><span class="pre">NeuralNet</span></code>:</p> +<div class="highlight-python"><div class="highlight"><pre>NeuralNet::NeuralNet(NetProto netproto, int npartitions); +</pre></div> +</div> +<p>The constructor creates a graph representing the net structure firstly in</p> +<div class="highlight-python"><div class="highlight"><pre>Graph* NeuralNet::CreateGraph(const NetProto& netproto, int npartitions); +</pre></div> +</div> +<p>Next, it creates a layer for each node and connects layers if their nodes are +connected.</p> +<div class="highlight-python"><div class="highlight"><pre>void NeuralNet::CreateNetFromGraph(Graph* graph, int npartitions); +</pre></div> +</div> +<p>Since the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance may be shared among multiple workers, the +<code class="docutils literal"><span class="pre">Create</span></code> function returns a pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance .</p> +</div> +<div class="section" id="parameter-sharing"> +<span id="id1"></span><h3>Parameter sharing<a class="headerlink" href="#parameter-sharing" title="Permalink to this headline">¶</a></h3> +<p><code class="docutils literal"><span class="pre">Param</span></code> sharing +is enabled by first sharing the Param configuration (in <code class="docutils literal"><span class="pre">NeuralNet::Create</span></code>) +to create two similar (e.g., the same shape) Param objects, and then calling +(in <code class="docutils literal"><span class="pre">NeuralNet::CreateNetFromGraph</span></code>),</p> +<div class="highlight-python"><div class="highlight"><pre>void Param::ShareFrom(const Param& from); +</pre></div> +</div> +<p>It is also possible to share <code class="docutils literal"><span class="pre">Param</span></code>s of two nets, e.g., sharing parameters of +the training net and the test net,</p> +<div class="highlight-python"><div class="highlight"><pre>void NeuralNet:ShareParamsFrom(NeuralNet* other); +</pre></div> +</div> +<p>It will call <code class="docutils literal"><span class="pre">Param::ShareFrom</span></code> for each Param object.</p> +</div> +<div class="section" id="access-functions"> +<span id="access-functions"></span><h3>Access functions<a class="headerlink" href="#access-functions" title="Permalink to this headline">¶</a></h3> +<p><code class="docutils literal"><span class="pre">NeuralNet</span></code> provides a couple of access function to get the layers and params +of the net:</p> +<div class="highlight-python"><div class="highlight"><pre>const std::vector<Layer*>& layers() const; +const std::vector<Param*>& params() const ; +Layer* name2layer(string name) const; +Param* paramid2param(int id) const; +</pre></div> +</div> +</div> +<div class="section" id="partitioning"> +<span id="partitioning"></span><h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h3> +<div class="section" id="implementation"> +<span id="implementation"></span><h4>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline">¶</a></h4> +<p>SINGA partitions the neural net in <code class="docutils literal"><span class="pre">CreateGraph</span></code> function, which creates one +node for each (partitioned) layer. For example, if one layer’s partition +dimension is 0 or 1, then it creates <code class="docutils literal"><span class="pre">npartition</span></code> nodes for it; if the +partition dimension is -1, a single node is created, i.e., no partitioning. +Each node is assigned a partition (or location) ID. If the original layer is +configured with a location ID, then the ID is assigned to each newly created node. +These nodes are connected according to the connections of the original layers. +Some connection layers will be added automatically. +For instance, if two connected sub-layers are located at two +different workers, then a pair of bridge layers is inserted to transfer the +feature (and gradient) blob between them. When two layers are partitioned on +different dimensions, a concatenation layer which concatenates feature rows (or +columns) and a slice layer which slices feature rows (or columns) would be +inserted. These connection layers help making the network communication and +synchronization transparent to the users.</p> +</div> +<div class="section" id="dispatching-partitions-to-workers"> +<span id="dispatching-partitions-to-workers"></span><h4>Dispatching partitions to workers<a class="headerlink" href="#dispatching-partitions-to-workers" title="Permalink to this headline">¶</a></h4> +<p>Each (partitioned) layer is assigned a location ID, based on which it is dispatched to one +worker. Particularly, the pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance is passed +to every worker within the same group, but each worker only computes over the +layers that have the same partition (or location) ID as the worker’s ID. When +every worker computes the gradients of the entire model parameters +(strategy-2), we refer to this process as data parallelism. When different +workers compute the gradients of different parameters (strategy-3 or +strategy-1), we call this process model parallelism. The hybrid partitioning +leads to hybrid parallelism where some workers compute the gradients of the +same subset of model parameters while other workers compute on different model +parameters. For example, to implement the hybrid parallelism in for the +<a class="reference external" href="http://arxiv.org/abs/1404.5997";>DCNN model</a>, we set <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">0</span></code> for +lower layers and <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">1</span></code> for higher layers.</p> +</div> +</div> +</div> +</div> + + + </div> + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2017 The Apache Software Foundation. All rights reserved. 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Added: incubator/singa/site/trunk/v1.1.0/docs/notebook/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/v1.1.0/docs/notebook/README.html?rev=1782721&view=auto ============================================================================== --- incubator/singa/site/trunk/v1.1.0/docs/notebook/README.html (added) +++ incubator/singa/site/trunk/v1.1.0/docs/notebook/README.html Mon Feb 13 05:13:19 2017 @@ -0,0 +1,240 @@ + + + +<!DOCTYPE html> +<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]--> +<head> + <meta charset="utf-8"> + + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + + <title><no title> — incubator-singa 1.1.0 documentation</title> + + + + + + + + + + + + + + + + <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" /> + + + + + + <link rel="top" title="incubator-singa 1.1.0 documentation" href="../../index.html"/> + <link href="../../_static/style.css" rel="stylesheet" type="text/css"> + + + + <script src="../../_static/js/modernizr.min.js"></script> + +</head> + +<body class="wy-body-for-nav" role="document"> + + <div class="wy-grid-for-nav"> + + + <nav data-toggle="wy-nav-shift" class="wy-nav-side"> + <div class="wy-side-scroll"> + <div class="wy-side-nav-search"> + + + + <a href="../../index.html" class="icon icon-home"> incubator-singa + + + + + <img src="../../_static/singa.png" class="logo" /> + + </a> + + + + + <div class="version"> + latest + </div> + + + + +<div role="search"> + <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get"> + <input type="text" name="q" placeholder="Search docs" /> + <input type="hidden" name="check_keywords" value="yes" /> + <input type="hidden" name="area" value="default" /> + </form> +</div> + + + </div> + + <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation"> + + + + <ul> +<li class="toctree-l1"><a class="reference internal" href="../../downloads.html">Download SINGA</a></li> +<li class="toctree-l1"><a class="reference internal" href="../index.html">Documentation</a></li> +</ul> +<p class="caption"><span class="caption-text">Development</span></p> +<ul> +<li class="toctree-l1"><a class="reference internal" href="../../develop/schedule.html">Development Schedule</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../develop/how-contribute.html">How to Contribute to SINGA</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../develop/contribute-code.html">How to Contribute Code</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../develop/contribute-docs.html">How to Contribute to Documentation</a></li> +</ul> +<p class="caption"><span class="caption-text">Community</span></p> +<ul> +<li class="toctree-l1"><a class="reference internal" href="../../community/source-repository.html">Source Repository</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../community/mail-lists.html">Project Mailing Lists</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../community/issue-tracking.html">Issue Tracking</a></li> +<li class="toctree-l1"><a class="reference internal" href="../../community/team-list.html">The SINGA Team</a></li> +</ul> + + + + </div> + </div> + </nav> + + <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"> + + + <nav class="wy-nav-top" role="navigation" aria-label="top navigation"> + <i data-toggle="wy-nav-top" class="fa fa-bars"></i> + <a href="../../index.html">incubator-singa</a> + </nav> + + + + <div class="wy-nav-content"> + <div class="rst-content"> + + + + + + +<div role="navigation" aria-label="breadcrumbs navigation"> + <ul class="wy-breadcrumbs"> + <li><a href="../../index.html">Docs</a> »</li> + + <li><no title></li> + <li class="wy-breadcrumbs-aside"> + + + + </li> + </ul> + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <p>These are some examples in IPython notebooks.</p> +<p>You can open them in <a class="reference external" href="http://nbviewer.jupyter.org/github/apache/incubator-singa/blob/master/doc/en/docs/notebook/index.ipynb";>notebook viewer</a>.</p> + + + </div> + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2017 The Apache Software Foundation. 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href="../community/team-list.html">The SINGA Team</a></li> +</ul> + + + + </div> + </div> + </nav> + + <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"> + + + <nav class="wy-nav-top" role="navigation" aria-label="top navigation"> + <i data-toggle="wy-nav-top" class="fa fa-bars"></i> + <a href="../index.html">incubator-singa</a> + </nav> + + + + <div class="wy-nav-content"> + <div class="rst-content"> + + + + + + +<div role="navigation" aria-label="breadcrumbs navigation"> + <ul class="wy-breadcrumbs"> + <li><a href="../index.html">Docs</a> »</li> + + <li><a href="index.html">Documentation</a> »</li> + + <li>Optimizer</li> + <li class="wy-breadcrumbs-aside"> + + + + </li> + </ul> + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <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-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">optimizer</span> +<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">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"><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>construct the optimizer</li> +<li>(optional) register each parameter with its specs.</li> +<li>use the optimizer to update parameter values given parameter gradients +and other optional info</li> +</ol> +<p>The subclasses should override the apply_with_lr function to do the real +parameter udpate.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> +<li><strong>lr</strong> (<em>float</em>) – a constant value for the learning rate</li> +<li><strong>momentum</strong> (<em>float</em>) – a constant value for the momentum value</li> +<li><strong>weight_decay</strong> (<em>float</em>) – the coefficent for L2 regularizer, which is +mutually exclusive with ‘regularizer’.</li> +<li><strong>regularizer</strong> – an instance of Regularizer or RegularizerConf; If set, +regularization would be applied in apply_with_lr(). +Users can also do regularization outside.</li> +<li><strong>constraint</strong> – an instance of Constraint or ConstraintConf; If set, +constraint would be applied inside apply_with_lr(). Users can +also apply constraint outside.</li> +</ul> +</td> +</tr> +</tbody> +</table> +<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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> +<li><strong>name</strong> (<em>str</em>) – parameter name</li> +<li><strong>specs</strong> (<em>ParamSpec</em>) – protobuf obj, including regularizer and +constraint, multipliers for learning rate and weight decay.</li> +</ul> +</td> +</tr> +</tbody> +</table> +</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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> +<li><strong>epoch</strong> (<em>int</em>) – training epoch ID</li> +<li><strong>value</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter value Tensor</li> +<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient Tensor</li> +<li><strong>name</strong> (<em>string</em>) – to get parameter specific regularizer or constraint</li> +<li><strong>step</strong> (<em>int</em>) – iteration ID within one epoch</li> +</ul> +</td> +</tr> +<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the updated gradient Tensor</p> +</td> +</tr> +</tbody> +</table> +</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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> +<li><strong>epoch</strong> (<em>int</em>) – training epoch ID</li> +<li><strong>lr</strong> (<em>float</em>) – learning rate</li> +<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient</li> +<li><strong>value</strong> (<em>Tesnor</em>) – parameter value</li> +<li><strong>name</strong> (<em>string</em>) – paramter name to index parameter specific +updating rules (including regularizer and constraint)</li> +<li><strong>step</strong> (<em>int</em>) – iteration ID within one epoch</li> +</ul> +</td> +</tr> +<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">updated parameter value</p> +</td> +</tr> +</tbody> +</table> +</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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple"> +<li><strong>epoch</strong> (<em>int</em>) – training epoch ID</li> +<li><strong>grad</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – parameter gradient</li> +<li><strong>value</strong> (<em>Tesnor</em>) – parameter value</li> +<li><strong>name</strong> (<em>string</em>) – paramter name to retrieval parameter specific +updating rules (including regularizer and constraint)</li> +<li><strong>step</strong> (<em>int</em>) – training iteration ID within one epoch</li> +</ul> +</td> +</tr> +<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">updated parameter value</p> +</td> +</tr> +</tbody> +</table> +</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"><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></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"><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></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"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>RMSProp optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> +<li><strong>rho</strong> (<em>float</em>) – float within [0, 1]</li> +<li><strong>epsilon</strong> (<em>float</em>) – small value for preventing numeric error</li> +</ul> +</td> +</tr> +</tbody> +</table> +<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></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"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>AdaGrad optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>epsilon</strong> (<em>float</em>) – small number for preventing numeric error.</td> +</tr> +</tbody> +</table> +<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></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"><span class="pre">singa.optimizer.Optimizer</span></code></a></p> +<p>Adam optimizer.</p> +<p>See the base Optimizer for all constructor args.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> +<li><strong>beta_1</strong> (<em>float</em>) – coefficient of momentum</li> +<li><strong>beta_2</strong> (<em>float</em>) – coefficient of aggregated squared gradient</li> +<li><strong>epsilon</strong> (<em>float</em>) – small value for preventing numeric error</li> +</ul> +</td> +</tr> +</tbody> +</table> +<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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>step</strong> (<em>int</em>) – the accumulated training iterations, not the iteration ID</td> +</tr> +</tbody> +</table> +</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"><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"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>Wrapper for regularizer implemented using C++.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>conf</strong> (<em>RegularizerConf</em>) – protobuf message for the configuration.</td> +</tr> +</tbody> +</table> +<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"><span class="pre">singa.optimizer.Regularizer</span></code></a></p> +<p>L2 regularization</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>coefficient</strong> (<em>float</em>) – regularization coefficient.</td> +</tr> +</tbody> +</table> +<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"><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"><span class="pre">singa.optimizer.Constraint</span></code></a></p> +<p>Wrapper for constraints implemented using C++.</p> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>conf</strong> (<em>ConstraintConf</em>) – protobuf message for the configuration.</td> +</tr> +</tbody> +</table> +<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"><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> + + + </div> + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="data.html" class="btn btn-neutral float-right" title="Data" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="metric.html" class="btn btn-neutral" title="Metric" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2017 The Apache Software Foundation. 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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-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">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="bp">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="bp">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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">a dict of (parameter name, parameter Tensor)</td> +</tr> +</tbody> +</table> +</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> +<table class="docutils field-list" frame="void" rules="none"> +<col class="field-name" /> +<col class="field-body" /> +<tbody valign="top"> +<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple"> +<li><strong>param_name</strong> (<em>string</em>) – name of the parameter</li> +<li><strong>param_val</strong> (<a class="reference internal" href="tensor.html#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) – value tensor of the parameter</li> +</ul> +</td> +</tr> +</tbody> +</table> +</dd></dl> + +</dd></dl> + +</div> + + + </div> + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="converter.html" class="btn btn-neutral float-right" title="Caffe Converter" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="image_tool.html" class="btn btn-neutral" title="Image Tool" 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