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| <p id="owner"><a href="../../../package.html" class="extype" name="org">org</a>.<a href="../../package.html" class="extype" name="org.apache">apache</a>.<a href="../package.html" class="extype" name="org.apache.mxnet">mxnet</a></p> |
| <h1>module</h1><span class="permalink"> |
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| <span class="modifier_kind"> |
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| <div id="types" class="types members"> |
| <h3>Type Members</h3> |
| <ol><li name="org.apache.mxnet.module.BaseModule" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="BaseModuleextendsAnyRef"></a> |
| <a id="BaseModule:BaseModule"></a> |
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| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">class</span> |
| </span> |
| <span class="symbol"> |
| <a href="BaseModule.html"><span class="name">BaseModule</span></a><span class="result"> extends <span class="extype" name="scala.AnyRef">AnyRef</span></span> |
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| <p class="shortcomment cmt">The base class of a modules.</p><div class="fullcomment"><div class="comment cmt"><p>The base class of a modules. A module represents a computation component. The design |
| purpose of a module is that it abstract a computation "machine", that one can run forward, |
| backward, update parameters, etc. We aim to make the APIs easy to use, especially in the |
| case when we need to use imperative API to work with multiple modules (e.g. stochastic |
| depth network).</p><p>A module has several states:</p><p>- Initial state. Memory is not allocated yet, not ready for computation yet. |
| - Binded. Shapes for inputs, outputs, and parameters are all known, memory allocated, |
| ready for computation. |
| - Parameter initialized. For modules with parameters, doing computation before initializing |
| the parameters might result in undefined outputs. |
| - Optimizer installed. An optimizer can be installed to a module. After this, the parameters |
| of the module can be updated according to the optimizer after gradients are computed |
| (forward-backward).</p><p> In order for a module to interactive with others, a module should be able to report the |
| following information in its raw stage (before binded)</p><ul><li><code>data_names</code>: list of string indicating the names of required data.</li><li><code>output_names</code>: list of string indicating the names of required outputs.</li></ul><p> And also the following richer information after binded:</p><ul><li>state information<ul><li><code>binded</code>: <code>bool</code>, indicating whether the memory buffers needed for computation |
| has been allocated.</li><li><code>forTraining</code>: whether the module is binded for training (if binded).</li><li><code>paramsInitialized</code>: <code>bool</code>, indicating whether the parameters of this modules |
| has been initialized.</li><li><code>optimizerInitialized</code>: <code>bool</code>, indicating whether an optimizer is defined |
| and initialized.</li><li><code>inputsNeedGrad</code>: <code>bool</code>, indicating whether gradients with respect to the |
| input data is needed. Might be useful when implementing composition of modules.</li></ul></li><li>input/output information<ul><li><code>dataShapes</code>: a list of <code>(name, shape)</code>. In theory, since the memory is allocated, |
| we could directly provide the data arrays. But in the case of data parallelization, |
| the data arrays might not be of the same shape as viewed from the external world.</li><li><code>labelShapes</code>: a list of <code>(name, shape)</code>. This might be <code>[]</code> if the module does |
| not need labels (e.g. it does not contains a loss function at the top), or a module |
| is not binded for training.</li><li><code>outputShapes</code>: a list of <code>(name, shape)</code> for outputs of the module.</li></ul></li><li>parameters (for modules with parameters)<ul><li><code>getParams()</code>: return a tuple <code>(argParams, auxParams)</code>. Each of those |
| is a dictionary of name to <code>NDArray</code> mapping. Those <code>NDArray</code> always lives on |
| CPU. The actual parameters used for computing might live on other devices (GPUs), |
| this function will retrieve (a copy of) the latest parameters. Therefore, modifying</li><li><code>setParams(argParams, auxParams)</code>: assign parameters to the devices |
| doing the computation.</li><li><code>initParams(...)</code>: a more flexible interface to assign or initialize the parameters.</li></ul></li><li>setup<ul><li><code>bind()</code>: prepare environment for computation.</li><li><code>initOptimizer()</code>: install optimizer for parameter updating.</li></ul></li><li>computation<ul><li><code>forward(dataBatch)</code>: forward operation.</li><li><code>backward(outGrads=None)</code>: backward operation.</li><li><code>update()</code>: update parameters according to installed optimizer.</li><li><code>getOutputs()</code>: get outputs of the previous forward operation.</li><li><code>getInputGrads()</code>: get the gradients with respect to the inputs computed |
| in the previous backward operation.</li><li><code>updateMetric(metric, labels)</code>: update performance metric for the previous forward |
| computed results.</li></ul></li><li>other properties (mostly for backward compatibility)<ul><li><code>symbol</code>: the underlying symbolic graph for this module (if any) |
| This property is not necessarily constant. For example, for <code>BucketingModule</code>, |
| this property is simply the *current* symbol being used. For other modules, |
| this value might not be well defined.</li></ul></li></ul><p> When those intermediate-level API are implemented properly, the following |
| high-level API will be automatically available for a module:</p><ul><li><code>fit</code>: train the module parameters on a data set</li><li><code>predict</code>: run prediction on a data set and collect outputs</li><li><code>score</code>: run prediction on a data set and evaluate performance |
| </li></ul></div></div> |
| </li><li name="org.apache.mxnet.module.BucketingModule" visbl="pub" data-isabs="false" fullComment="no" group="Ungrouped"> |
| <a id="BucketingModuleextendsBaseModule"></a> |
| <a id="BucketingModule:BucketingModule"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier"></span> |
| <span class="kind">class</span> |
| </span> |
| <span class="symbol"> |
| <a href="BucketingModule.html"><span class="name">BucketingModule</span></a><span class="result"> extends <a href="BaseModule.html" class="extype" name="org.apache.mxnet.module.BaseModule">BaseModule</a></span> |
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| </span> |
| <p class="shortcomment cmt">This module helps to deal efficiently with varying-length inputs.</p> |
| </li><li name="org.apache.mxnet.module.DataParallelExecutorGroup" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="DataParallelExecutorGroupextendsAnyRef"></a> |
| <a id="DataParallelExecutorGroup:DataParallelExecutorGroup"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier"></span> |
| <span class="kind">class</span> |
| </span> |
| <span class="symbol"> |
| <a href="DataParallelExecutorGroup.html"><span class="name">DataParallelExecutorGroup</span></a><span class="result"> extends <span class="extype" name="scala.AnyRef">AnyRef</span></span> |
| </span> |
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| <p class="shortcomment cmt">DataParallelExecutorGroup is a group of executors that lives on a group of devices.</p><div class="fullcomment"><div class="comment cmt"><p>DataParallelExecutorGroup is a group of executors that lives on a group of devices. |
| This is a helper class used to implement data parallelism. Each mini-batch will |
| be split and run on the devices.</p></div></div> |
| </li><li name="org.apache.mxnet.module.FitParams" visbl="pub" data-isabs="false" fullComment="no" group="Ungrouped"> |
| <a id="FitParamsextendsAnyRef"></a> |
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| <h4 class="signature"> |
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| <a href="FitParams.html"><span class="name">FitParams</span></a><span class="result"> extends <span class="extype" name="scala.AnyRef">AnyRef</span></span> |
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| </li><li name="org.apache.mxnet.module.Module" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="ModuleextendsBaseModule"></a> |
| <a id="Module:Module"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier"></span> |
| <span class="kind">class</span> |
| </span> |
| <span class="symbol"> |
| <a href="Module.html"><span class="name">Module</span></a><span class="result"> extends <a href="BaseModule.html" class="extype" name="org.apache.mxnet.module.BaseModule">BaseModule</a></span> |
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| <p class="shortcomment cmt">Module is a basic module that wrap a <code>Symbol</code>.</p><div class="fullcomment"><div class="comment cmt"><p>Module is a basic module that wrap a <code>Symbol</code>. It is functionally the same |
| as the <code>FeedForward</code> model, except under the module API.</p></div></div> |
| </li><li name="org.apache.mxnet.module.SequentialModule" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="SequentialModuleextendsBaseModule"></a> |
| <a id="SequentialModule:SequentialModule"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier"></span> |
| <span class="kind">class</span> |
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| <span class="symbol"> |
| <a href="SequentialModule.html"><span class="name">SequentialModule</span></a><span class="result"> extends <a href="BaseModule.html" class="extype" name="org.apache.mxnet.module.BaseModule">BaseModule</a></span> |
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| <p class="shortcomment cmt">A SequentialModule is a container module that can chain multiple modules together.</p><div class="fullcomment"><div class="comment cmt"><p>A SequentialModule is a container module that can chain multiple modules together. |
| Note building a computation graph with this kind of imperative container is less |
| flexible and less efficient than the symbolic graph. |
| So this should be only used as a handy utility. |
| </p></div></div> |
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