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| <title>Checkpoint — Korean Speech Recognition 1.0 documentation</title> | ||
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| 1.0 | ||
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| <div class="section" id="module-package.checkpoint"> | ||
| <span id="checkpoint"></span><h1>Checkpoint<a class="headerlink" href="#module-package.checkpoint" title="Permalink to this headline">¶</a></h1> | ||
| <dl class="class"> | ||
| <dt id="package.checkpoint.CheckPoint"> | ||
| <em class="property">class </em><code class="descclassname">package.checkpoint.</code><code class="descname">CheckPoint</code><span class="sig-paren">(</span><em>model=None</em>, <em>epoch=None</em>, <em>train_dataset_list=None</em>, <em>valid_dataset=None</em>, <em>time_step=None</em>, <em>total_time_step=None</em>, <em>hparams=None</em>, <em>queue=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/package/checkpoint.html#CheckPoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#package.checkpoint.CheckPoint" title="Permalink to this definition">¶</a></dt> | ||
| <dd><p>The Checkpoint class manages the saving and loading of a model during training. It allows training to be suspended | ||
| and resumed at a later time (e.g. when running on a cluster using sequential jobs).</p> | ||
| <p>To make a checkpoint, initialize a Checkpoint object with the following args; then call that object’s save() method | ||
| to write parameters to disk.</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>model</strong> (<a class="reference external" href="https://pytorch.org/docs/master/nn.html#torch.nn.Module" title="(in PyTorch vmaster (1.5.0a0+f515d87 ))"><em>torch.nn.Module</em></a>) – seq2seq model being trained</li> | ||
| <li><strong>optimizer</strong> (<em>torch.nn</em>) – stores the state of the optimizer</li> | ||
| <li><strong>epoch</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – current epoch (an epoch is a loop through the full training data)</li> | ||
| <li><strong>time_step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – number of examples seen within the current epoch</li> | ||
| <li><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – mini batch size</li> | ||
| </ul> | ||
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| © Copyright 2020, SooHwan Kim | ||
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