#367 fix: Request correct hydra-core

Merged
Ghost merged 1 commits into Deci-AI:master from deci-ai:hotfix/ALG-000_hydra-req
  
    
        
          
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  <section id="super-gradients-training-utils-optimizers-package">
<h1>super_gradients.training.utils.optimizers package<a class="headerlink" href="#super-gradients-training-utils-optimizers-package" title="Permalink to this headline"></a></h1>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</section>
<section id="module-super_gradients.training.utils.optimizers.rmsprop_tf">
<span id="super-gradients-training-utils-optimizers-rmsprop-tf-module"></span><h2>super_gradients.training.utils.optimizers.rmsprop_tf module<a class="headerlink" href="#module-super_gradients.training.utils.optimizers.rmsprop_tf" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="super_gradients.training.utils.optimizers.rmsprop_tf.RMSpropTF">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.optimizers.rmsprop_tf.</span></span><span class="sig-name descname"><span class="pre">RMSpropTF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_in_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/optimizers/rmsprop_tf.html#RMSpropTF"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.optimizers.rmsprop_tf.RMSpropTF" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.optimizer.Optimizer</span></code></p>
<p>Implements RMSprop algorithm (TensorFlow style epsilon)
NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt
and a few other modifications to closer match Tensorflow for matching hyper-params.
Noteworthy changes include:
1. Epsilon applied inside square-root
2. square_avg initialized to ones
3. LR scaling of update accumulated in momentum buffer
Proposed by G. Hinton in his
<a class="reference external" href="http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf">course</a>.
The centered version first appears in <a class="reference external" href="https://arxiv.org/pdf/1308.0850v5.pdf">Generating Sequences
With Recurrent Neural Networks</a>.</p>
<dl class="py method">
<dt class="sig sig-object py" id="super_gradients.training.utils.optimizers.rmsprop_tf.RMSpropTF.step">
<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super_gradients/training/utils/optimizers/rmsprop_tf.html#RMSpropTF.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#super_gradients.training.utils.optimizers.rmsprop_tf.RMSpropTF.step" title="Permalink to this definition"></a></dt>
<dd><p>Performs a single optimization step.
:param closure: A closure that reevaluates the model</p>
<blockquote>
<div><p>and returns the loss.</p>
</div></blockquote>
<dl class="field-list simple">
</dl>
</dd></dl>
</dd></dl>
</section>
<section id="module-super_gradients.training.utils.optimizers">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-super_gradients.training.utils.optimizers" title="Permalink to this headline"></a></h2>
</section>
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Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#367 fix: Request correct hydra-core

Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git
