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---
title: Optimizer
keywords: fastai
sidebar: home_sidebar
summary: "Define the general fastai optimizer and the variants"
description: "Define the general fastai optimizer and the variants"
nb_path: "nbs/12_optimizer.ipynb"
---
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">add_docs</span><span class="p">(</span><span class="n">_BaseOptimizer</span><span class="p">,</span>
<span class="n">all_params</span><span class="o">=</span><span class="s2">"List of param_groups, parameters, and hypers"</span><span class="p">,</span>
<span class="n">freeze_to</span><span class="o">=</span><span class="s2">"Freeze parameter groups up to `n`"</span><span class="p">,</span>
<span class="n">freeze</span><span class="o">=</span><span class="s2">"Freeze up to last parameter group"</span><span class="p">,</span>
<span class="n">set_freeze</span><span class="o">=</span><span class="s2">"Set `rg` for parameter group `n` only"</span><span class="p">,</span>
<span class="n">unfreeze</span><span class="o">=</span><span class="s2">"Unfreeze the entire model"</span><span class="p">,</span>
<span class="n">set_hypers</span><span class="o">=</span><span class="s2">"`set_hyper` for all `kwargs`"</span><span class="p">,</span>
<span class="n">set_hyper</span><span class="o">=</span><span class="s2">"Set the value(s) in `v` for hyper-parameter `k`"</span><span class="p">)</span>
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<h2 id="Optimizer" class="doc_header"><code>class</code> <code>Optimizer</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L64" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>Optimizer</code>(<strong><code>params</code></strong>, <strong><code>cbs</code></strong>, <strong><code>train_bn</code></strong>=<em><code>True</code></em>, <strong>**<code>defaults</code></strong>) :: <code>_BaseOptimizer</code></p>
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<p>Base optimizer class for the fastai library, updating <code>params</code> with <code>cbs</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">add_docs</span><span class="p">(</span><span class="n">Optimizer</span><span class="p">,</span>
<span class="n">zero_grad</span><span class="o">=</span><span class="s2">"Standard PyTorch API: Zero all the grad attributes of the parameters"</span><span class="p">,</span>
<span class="n">step</span><span class="o">=</span><span class="s2">"Standard PyTorch API: Update the stats and execute the steppers in on all parameters that have a grad"</span><span class="p">,</span>
<span class="n">state_dict</span><span class="o">=</span><span class="s2">"Return the state of the optimizer in a dictionary"</span><span class="p">,</span>
<span class="n">load_state_dict</span><span class="o">=</span><span class="s2">"Load the content of `sd`"</span><span class="p">,</span>
<span class="n">clear_state</span><span class="o">=</span><span class="s2">"Reset the state of the optimizer"</span><span class="p">)</span>
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<h3 id="Initializing-an-Optimizer">Initializing an Optimizer<a class="anchor-link" href="#Initializing-an-Optimizer"> </a></h3>
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<p><a href="/torch_core.html#params"><code>params</code></a> will be used to create the <code>param_groups</code> of the optimizer. If it's a collection (or a generator) of parameters, it will be a <a href="https://fastcore.fast.ai/foundation#L"><code>L</code></a> containing one <a href="https://fastcore.fast.ai/foundation#L"><code>L</code></a> with all the parameters. To define multiple parameter groups <a href="/torch_core.html#params"><code>params</code></a> should be passed as a collection (or a generator) of <a href="https://fastcore.fast.ai/foundation#L"><code>L</code></a>s.
{% include note.html content='In PyTorch, <code>model.parameters()</code> returns a generator with all the parameters, that you can directly pass to <code>Optimizer</code>.' %}</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="mi">1</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="n">noop</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">param_lists</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</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="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">noop</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">param_lists</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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="n">noop</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">param_lists</span><span class="p">,</span> <span class="p">[[</span><span class="mi">1</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="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(([</span><span class="n">o</span><span class="p">,</span><span class="n">o</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">)),</span> <span class="n">noop</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">param_lists</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]])</span>
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<p><code>cbs</code> is a list of functions that will be composed when applying the step. For instance, you can compose a function making the SGD step, with another one applying weight decay. Additionally, each <code>cb</code> can have a <a href="https://fastcore.fast.ai/foundation#defaults"><code>defaults</code></a> attribute that contains hyper-parameters and their default value. Those are all gathered at initialization, and new values can be passed to override those defaults with the <a href="https://fastcore.fast.ai/foundation#defaults"><code>defaults</code></a> kwargs. The steppers will be called by <a href="/optimizer.html#Optimizer.step"><code>Optimizer.step</code></a> (which is the standard PyTorch name), and gradients can be cleared with <a href="/optimizer.html#Optimizer.zero_grad"><code>Optimizer.zero_grad</code></a> (also a standard PyTorch name).</p>
<p>Once the defaults have all been pulled off, they are copied as many times as there are <code>param_groups</code> and stored in <code>hypers</code>. To apply different hyper-parameters to different groups (differential learning rates, or no weight decay for certain layers for instance), you will need to adjust those values after the init.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">tst_arg</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">return</span> <span class="n">p</span>
<span class="n">tst_arg</span><span class="o">.</span><span class="n">defaults</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tst_arg2</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">lr2</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">return</span> <span class="n">p</span>
<span class="n">tst_arg2</span><span class="o">.</span><span class="n">defaults</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">lr2</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tst_arg3</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">return</span> <span class="n">p</span>
<span class="n">tst_arg3</span><span class="o">.</span><span class="n">defaults</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">mom</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tst_arg4</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">return</span> <span class="n">p</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="mi">1</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="p">[</span><span class="n">tst_arg</span><span class="p">,</span><span class="n">tst_arg2</span><span class="p">,</span> <span class="n">tst_arg3</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr2'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">,</span> <span class="s1">'mom'</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">,</span> <span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="mi">1</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="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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="n">tst_arg</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">}])</span>
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<p>For each hyper-parameter, you can pass a slice or a collection to set them, if there are multiple parameter groups. A slice will be converted to a log-uniform collection from its beginning to its end, or if it only has an end <code>e</code>, to a collection of as many values as there are parameter groups that are <code>...,e/10,e/10,e</code>.</p>
<p>Setting an hyper-parameter with a collection that has a different number of elements than the optimizer has parameter groups will raise an error.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.2</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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">4</span><span class="p">]],</span> <span class="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mf">1e-2</span><span class="p">))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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">4</span><span class="p">]],</span> <span class="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mf">1e-4</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-4</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">},</span> <span class="p">{</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">param_groups</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'params'</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-4</span><span class="p">},</span> <span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">},</span> <span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="s1">'lr'</span><span class="p">:</span> <span class="mf">1e-2</span><span class="p">}])</span>
<span class="n">test_fail</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="n">Optimizer</span><span class="p">([[</span><span class="mi">1</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">4</span><span class="p">]],</span> <span class="n">tst_arg</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.2</span><span class="p">])))</span>
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<h3 id="Basic-steppers">Basic steppers<a class="anchor-link" href="#Basic-steppers"> </a></h3>
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<p>To be able to give examples of optimizer steps, we will need some steppers, like the following:</p>
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<h4 id="sgd_step" class="doc_header"><code>sgd_step</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L101" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>sgd_step</code>(<strong><code>p</code></strong>, <strong><code>lr</code></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">tst_param</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">grad</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="s2">"Create a tensor with `val` and a gradient of `grad` for testing"</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="n">val</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="n">res</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">([</span><span class="n">val</span><span class="o">/</span><span class="mi">10</span> <span class="k">if</span> <span class="n">grad</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">grad</span><span class="p">])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
<span class="k">return</span> <span class="n">res</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">sgd_step</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.9</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.1</span><span class="p">]))</span>
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<h4 id="weight_decay" class="doc_header"><code>weight_decay</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L105" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>weight_decay</code>(<strong><code>p</code></strong>, <strong><code>lr</code></strong>, <strong><code>wd</code></strong>, <strong><code>do_wd</code></strong>=<em><code>True</code></em>, <strong>**<code>kwargs</code></strong>)</p>
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<p>Weight decay as decaying <code>p</code> with <code>lr*wd</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">weight_decay</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.9</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.1</span><span class="p">]))</span>
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<h4 id="l2_reg" class="doc_header"><code>l2_reg</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L112" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>l2_reg</code>(<strong><code>p</code></strong>, <strong><code>lr</code></strong>, <strong><code>wd</code></strong>, <strong><code>do_wd</code></strong>=<em><code>True</code></em>, <strong>**<code>kwargs</code></strong>)</p>
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<p>L2 regularization as adding <code>wd*p</code> to <code>p.grad</code></p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">l2_reg</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">]))</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.2</span><span class="p">]))</span>
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<p>{% include warning.html content='Weight decay and L2 regularization is the same thing for basic SGD, but for more complex optimizers, they are very different.' %}</p>
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<h3 id="Making-the-step">Making the step<a class="anchor-link" href="#Making-the-step"> </a></h3>
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<h4 id="Optimizer.step" class="doc_header"><code>Optimizer.step</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L81" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.step</code>()</p>
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<p>This method will loop over all param groups, then all parameters for which <code>grad</code> is not None and call each function in <code>stepper</code>, passing it the parameter <code>p</code> with the hyper-parameters in the corresponding dict in <code>hypers</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">L</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tst_params</span><span class="p">():</span> <span class="k">return</span> <span class="n">r</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">tst_param</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">tst_params</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">test_close</span><span class="p">([</span><span class="n">p</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">],</span> <span class="n">r</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">mul</span><span class="p">(</span><span class="mf">0.99</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="n">tst_params</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="p">[</span><span class="n">weight_decay</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">test_close</span><span class="p">([</span><span class="n">p</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">],</span> <span class="n">r</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">mul</span><span class="p">(</span><span class="mf">0.98</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="n">tst_params</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">params</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">test_close</span><span class="p">([</span><span class="n">p</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">,</span> <span class="mf">1.98</span><span class="p">,</span> <span class="mf">3.</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="n">tst_params</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="n">params</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">:]],</span> <span class="n">sgd_step</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'lr'</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.01</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">test_close</span><span class="p">([</span><span class="n">p</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">,</span> <span class="mf">1.98</span><span class="p">,</span> <span class="mf">2.97</span><span class="p">])</span>
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<h4 id="Optimizer.zero_grad" class="doc_header"><code>Optimizer.zero_grad</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L76" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.zero_grad</code>()</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="n">tst_params</span><span class="p">()</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="p">[</span><span class="n">weight_decay</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">wd</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="p">[</span><span class="n">test_eq</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">,</span> <span class="n">tensor</span><span class="p">([</span><span class="mf">0.</span><span class="p">]))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">];</span>
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<p>Some of the <a href="/optimizer.html#Optimizer"><code>Optimizer</code></a> <code>cbs</code> can be functions updating the state associated with a parameter. That state can then be used by any stepper. The best example is a momentum calculation.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">tst_stat</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sum'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">p</span><span class="p">))</span> <span class="o">+</span> <span class="n">p</span><span class="o">.</span><span class="n">data</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">'sum'</span><span class="p">:</span> <span class="n">s</span><span class="p">}</span>
<span class="n">tst_stat</span><span class="o">.</span><span class="n">defaults</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mom'</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">}</span>
<span class="c1">#Test Optimizer init</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="mi">1</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="n">tst_stat</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'mom'</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">}])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">([</span><span class="mi">1</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="n">tst_stat</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mf">0.99</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">hypers</span><span class="p">,</span> <span class="p">[{</span><span class="s1">'mom'</span><span class="p">:</span> <span class="mf">0.99</span><span class="p">}])</span>
<span class="c1">#Test stat</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">tst_stat</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">assert</span> <span class="s1">'sum'</span> <span class="ow">in</span> <span class="n">state</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">state</span><span class="p">[</span><span class="s1">'sum'</span><span class="p">])</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">tst_stat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'sum'</span><span class="p">],</span> <span class="mi">2</span><span class="o">*</span><span class="n">x</span><span class="p">)</span>
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<h2 id="Statistics">Statistics<a class="anchor-link" href="#Statistics"> </a></h2>
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<h4 id="average_grad" class="doc_header"><code>average_grad</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L119" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>average_grad</code>(<strong><code>p</code></strong>, <strong><code>mom</code></strong>, <strong><code>dampening</code></strong>=<em><code>False</code></em>, <strong><code>grad_avg</code></strong>=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
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<p>Keeps track of the avg grads of <code>p</code> in <code>state</code> with <code>mom</code>.</p>
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<p><code>dampening=False</code> gives the classical formula for momentum in SGD:</p>
<pre><code>new_val = old_val * mom + grad</code></pre>
<p>whereas <code>dampening=True</code> makes it an exponential moving average:</p>
<pre><code>new_val = old_val * mom + grad * (1-mom)</code></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">([</span><span class="mi">1</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="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">])</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'grad_avg'</span><span class="p">],</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'grad_avg'</span><span class="p">],</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span> <span class="o">*</span> <span class="mf">1.9</span><span class="p">)</span>
<span class="c1">#Test dampening</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'grad_avg'</span><span class="p">],</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">mom</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_close</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'grad_avg'</span><span class="p">],</span> <span class="p">(</span><span class="mf">0.1</span><span class="o">*</span><span class="mf">0.9</span><span class="o">+</span><span class="mf">0.1</span><span class="p">)</span><span class="o">*</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
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<h4 id="average_sqr_grad" class="doc_header"><code>average_sqr_grad</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L129" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>average_sqr_grad</code>(<strong><code>p</code></strong>, <strong><code>sqr_mom</code></strong>, <strong><code>dampening</code></strong>=<em><code>True</code></em>, <strong><code>sqr_avg</code></strong>=<em><code>None</code></em>, <strong>**<code>kwargs</code></strong>)</p>
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<p><code>dampening=False</code> gives the classical formula for momentum in SGD:</p>
<pre><code>new_val = old_val * mom + grad**2</code></pre>
<p>whereas <code>dampening=True</code> makes it an exponential moving average:</p>
<pre><code>new_val = old_val * mom + (grad**2) * (1-mom)</code></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">([</span><span class="mi">1</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="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">])</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_sqr_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">sqr_mom</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'sqr_avg'</span><span class="p">],</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_sqr_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">sqr_mom</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'sqr_avg'</span><span class="p">],</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.99</span><span class="p">)</span>
<span class="c1">#Test dampening</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_sqr_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">sqr_mom</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_close</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'sqr_avg'</span><span class="p">],</span> <span class="mf">0.01</span><span class="o">*</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">average_sqr_grad</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">sqr_mom</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="o">**</span><span class="n">state</span><span class="p">)</span>
<span class="n">test_close</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s1">'sqr_avg'</span><span class="p">],</span> <span class="p">(</span><span class="mf">0.01</span><span class="o">*</span><span class="mf">0.99</span><span class="o">+</span><span class="mf">0.01</span><span class="p">)</span><span class="o">*</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
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<h3 id="Freezing-part-of-the-model">Freezing part of the model<a class="anchor-link" href="#Freezing-part-of-the-model"> </a></h3>
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<h4 id="Optimizer.freeze" class="doc_header"><code>Optimizer.freeze</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L26" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.freeze</code>()</p>
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<h4 id="Optimizer.freeze_to" class="doc_header"><code>Optimizer.freeze_to</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L19" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.freeze_to</code>(<strong><code>n</code></strong>)</p>
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<h4 id="Optimizer.unfreeze" class="doc_header"><code>Optimizer.unfreeze</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L33" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.unfreeze</code>()</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">tst_params</span><span class="p">(),</span> <span class="n">tst_params</span><span class="p">(),</span> <span class="n">tst_params</span><span class="p">()]</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">freeze_to</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">req_grad</span> <span class="o">=</span> <span class="n">Self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">}:</span> <span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">True</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="c1">#Unfreezing</span>
<span class="n">opt</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span> <span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">True</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="c1">#TODO: test warning</span>
<span class="c1"># opt.freeze_to(3)</span>
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<p>Parameters such as batchnorm weights/bias can be marked to always be in training mode, just put <code>force_train=true</code> in their state.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">tst_params</span><span class="p">(),</span> <span class="n">tst_params</span><span class="p">(),</span> <span class="n">tst_params</span><span class="p">()]</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">sgd_step</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">])[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]]:</span> <span class="n">opt</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'force_train'</span><span class="p">:</span> <span class="kc">True</span><span class="p">}</span>
<span class="n">opt</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">L</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">req_grad</span><span class="p">),</span> <span class="p">[</span><span class="kc">True</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">)</span>
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<h3 id="Serializing">Serializing<a class="anchor-link" href="#Serializing"> </a></h3>
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<h4 id="Optimizer.state_dict" class="doc_header"><code>Optimizer.state_dict</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L90" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.state_dict</code>()</p>
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<h4 id="Optimizer.load_state_dict" class="doc_header"><code>Optimizer.load_state_dict</code><a href="https://github.com/fastai/fastai/tree/master/fastai/optimizer.py#L94" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>Optimizer.load_state_dict</code>(<strong><code>sd</code></strong>)</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">tst_param</span><span class="p">([</span><span class="mi">1</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="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">])</span>
<span class="n">opt</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">average_grad</span><span class="p">)</span>
<span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">test_eq</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="n">p</span><span class="p">][</span><span class="s1">'grad_avg'</span><span class="p">],</span> <span class="n">tensor</span><span class="p">([[</span><span class="mf">4.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]))</span>