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stepLR.html
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---
title: StepLRScheduler
keywords: fastai
sidebar: home_sidebar
nb_path: "nbs/07c_stepLR.ipynb"
---
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<p>In this tutorial we are going to be looking at the <code>StepLRScheduler</code> in the <code>timm</code> library.</p>
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<p>The schedule looks something like:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">timm.scheduler.step_lr</span> <span class="kn">import</span> <span class="n">StepLRScheduler</span>
<span class="kn">from</span> <span class="nn">nbdev.showdoc</span> <span class="kn">import</span> <span class="n">show_doc</span>
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<h2 id="StepLRScheduler" class="doc_header"><code>class</code> <code>StepLRScheduler</code><a href="timm/scheduler/step_lr.py#L13" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>StepLRScheduler</code>(<strong><code>optimizer</code></strong>:<code>Optimizer</code>, <strong><code>decay_t</code></strong>:<code>float</code>, <strong><code>decay_rate</code></strong>:<code>float</code>=<em><code>1.0</code></em>, <strong><code>warmup_t</code></strong>=<em><code>0</code></em>, <strong><code>warmup_lr_init</code></strong>=<em><code>0</code></em>, <strong><code>t_in_epochs</code></strong>=<em><code>True</code></em>, <strong><code>noise_range_t</code></strong>=<em><code>None</code></em>, <strong><code>noise_pct</code></strong>=<em><code>0.67</code></em>, <strong><code>noise_std</code></strong>=<em><code>1.0</code></em>, <strong><code>noise_seed</code></strong>=<em><code>42</code></em>, <strong><code>initialize</code></strong>=<em><code>True</code></em>) :: <code>Scheduler</code></p>
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<p>The <code>StepLRScheduler</code> as shown above accepts an <code>optimizer</code> and also some hyperparams which we will look into in detail below. We will first see how we can train models using the <code>StepLRScheduler</code> by first using <code>timm</code> training docs and then look at how we can use this scheduler as standalone scheduler for our custom training scripts.</p>
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<h2 id="Using-StepLRScheduler-scheduler-with-timm-training-script">Using <code>StepLRScheduler</code> scheduler with <code>timm</code> training script<a class="anchor-link" href="#Using-StepLRScheduler-scheduler-with-timm-training-script"> </a></h2>
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<p>To train models using the <code>StepLRScheduler</code> we simply update the training script args passed by passing in <code>--sched step</code> parameter alongside the necessary hyperparams. In this section we will also look at how each of the hyperparams update the <code>cosine</code> scheduler.</p>
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<p>The training command to use <code>cosine</code> scheduler looks something like:</p>
<div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="o">../</span><span class="n">imagenette2</span><span class="o">-</span><span class="mi">320</span><span class="o">/</span> <span class="o">--</span><span class="n">sched</span> <span class="n">step</span>
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<h2 id="Args">Args<a class="anchor-link" href="#Args"> </a></h2>
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<p>This way we start to use the <code>StepLRScheduler</code> with all the defaults. Let's now look at the associated hyperparams and how that updates the annealing schedule.</p>
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<h3 id="optimizer"><code>optimizer</code><a class="anchor-link" href="#optimizer"> </a></h3>
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<p>This is the <code>optimizer</code> that will be used for the training process.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">timm</span> <span class="kn">import</span> <span class="n">create_model</span>
<span class="kn">from</span> <span class="nn">timm.optim</span> <span class="kn">import</span> <span class="n">create_optimizer</span>
<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">SimpleNamespace</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">create_model</span><span class="p">(</span><span class="s1">'resnet34'</span><span class="p">)</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">SimpleNamespace</span><span class="p">()</span>
<span class="n">args</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">args</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-4</span>
<span class="n">args</span><span class="o">.</span><span class="n">opt</span> <span class="o">=</span> <span class="s1">'adam'</span>
<span class="n">args</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">create_optimizer</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
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<p>This <code>optimizer</code> object created using <code>create_optimizer</code> is what get's passed to the <code>optimizer</code> argument.</p>
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<h3 id="decay_t"><code>decay_t</code><a class="anchor-link" href="#decay_t"> </a></h3>
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<p>The number of epochs after which to decay the learning rate where the new learning rate value equals <code>lr * decay_rate</code>.</p>
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<h3 id="decay_rate"><code>decay_rate</code><a class="anchor-link" href="#decay_rate"> </a></h3>
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<p>When <code>decay_rate</code> > 0 and <1., at every restart the learning rate is decayed by new learning rate which equals <code>lr * decay_rate</code>. So if <code>decay_rate=0.5</code>, then in that case, the new learning rate becomes half the initial <code>lr</code>.</p>
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<span class="k">def</span> <span class="nf">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">):</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epoch</span><span class="p">):</span>
<span class="n">lr_per_epoch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scheduler</span><span class="o">.</span><span class="n">get_epoch_values</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
<span class="k">return</span> <span class="n">lr_per_epoch</span>
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<h4 id="decay_rate=1.-or-no-decay"><code>decay_rate=1.</code> or no decay<a class="anchor-link" href="#decay_rate=1.-or-no-decay"> </a></h4>
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<p>By setting <code>decay_t</code> = 5 and <code>decay_rate</code> = 1., we are telling the schedule to reduce the learning rate by decay_rate where new lr <code>lr * decay_rate</code> every 5 epochs.</p>
<p>But since, <code>decay_rate=1.</code>, the new learning rate equals the old learning rate hence, we get a constant line.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">StepLRScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">decay_t</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="n">decay_rate</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="n">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">i</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="n">num_epoch</span><span class="p">)],</span> <span class="n">lr_per_epoch</span><span class="p">);</span>
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<h4 id="decay_rate=0.5"><code>decay_rate=0.5</code><a class="anchor-link" href="#decay_rate=0.5"> </a></h4>
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<p>By setting <code>decay_t</code> = 5 and <code>decay_rate</code> = 0.5, we are telling the schedule to reduce the learning rate by decay_rate where new lr <code>lr * decay_rate</code> every 5 epochs, that is halve the learning rate after every 5 epochs.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">StepLRScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">decay_t</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="n">decay_rate</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="n">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">i</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="n">num_epoch</span><span class="p">)],</span> <span class="n">lr_per_epoch</span><span class="p">);</span>
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0PATSOVTU59O7BW0s3Ai8D1SZlNktYCTwP9wIqIOHGuGmy/rKWpwIWTmtm+1z0NM6tOEWP7rpnOzs7o6enJuhq59rG7fkJzscD9y7uyroqZ5YSkxyKic/B2PxFuzJk6wYsxmVkqDg2jva2Vna8fof/EyayrYmY559Cw089qHDiadVXMLOccGnb6tltPhptZFQ4N82JMZpaaQ8OYNaUFcGiYWXUODaO5WGBGyetqmFl1Dg0DykNU2x0aZlaFQ8OA8mS4h6fMrBqHhgEwp20CO/f5WQ0zG5lDw4ByT+PEyeBlr6thZiNwaBjg227NLB2HhgFejMnM0nFoGAAzp7QgeTEmMxuZQ8OA5FmNSS3uaZjZiBwadkr5tlv3NMxseA4NO6W9rZXte93TMLPhOTTslPa2Cby8389qmNnwHBp2ypyp5Wc1du7zsxpmNjSHhp3iZzXMrBqHhp1y+lkNT4ab2dAcGnbKzMmtybMa7mmY2dAcGnbK+OI43lrysxpmNrxi1hWwfJnTNoEHHu/j20/tqOl1Pv2hDlZcfXFNr2Fm555Dw37J5z7yDtY/s6um1/j+M7t48ImXHBpmdcihYb9k4bypLJw3tabXmDyhiS9/dzOvHDzKBROba3otMzu3PKdh513X/GkA/HTr3oxrYmaj5dCw8+7S2ZN5y/gCG7a+mnVVzGyUHBp23jUVxtE5d6pDw6wOOTQsE13zp7Fl90FeOXg066qY2Sg4NCwTXfPLk+3ubZjVl1ShIWmRpM2SeiWtHGK/JN2R7H9S0uXVykqaKmmdpC3JZ1vFvtuS4zdL+kiybYKkb0t6VtImSbe/uaZbljyvYVafqoaGpAJwJ7AYWADcIGnBoMMWAx3Jn+XA3SnKrgTWR0QHsD75TbJ/KXAJsAi4KzkPwFcj4leB9wJXSlp8No227BUL43jfvKls8B1UZnUlTU9jIdAbEVsj4hiwBlgy6JglwH1RtgGYImlmlbJLgNXJ99XAdRXb10TE0YjYBvQCCyPiUET8ACA518+A9tE32fKia/40encfZM8Bz2uY1Ys0oTEb2F7xuy/ZluaYkcrOiIidAMnnhWmvJ2kK8FHKPZQzSFouqUdSz549e0Zqm2Xo1PMa2zxEZVYv0oSGhtgWKY9JU3ZU15NUBO4H7oiIrUOdICJWRURnRHROnz69yuUsK++aVWJic5FHfuHQMKsXaUKjD5hT8bsdGPw2u+GOGansrmQIi+Rzd8rrrQK2RMTXU9TdcqxYGMf75rZ5MtysjqQJjUeBDknzJI2nPEndPeiYbuDG5C6qLmBfMuQ0UtluYFnyfRnwYMX2pZKaJc2jPLm+EUDSF4DJwO+PvqmWR13zp/GLPW+w+4CXmDWrB1VDIyL6gVuBh4FngLURsUnSLZJuSQ57CNhKedL6L4FPjVQ2KXM78GFJW4APJ79J9q8Fnga+C6yIiBOS2oE/pHwX1s8kPSHp37zZ/wEsW34PlVl9UUS1KYb61tnZGT09PVlXw4bRf+Ik7/3jdXz0sll86bcvzbo6ZpaQ9FhEdA7e7ifCLVOnn9fwvIZZPXBoWOa65k9l65432L3f8xpmeefQsMwNzGts2OZ5DbO8c2hY5i6ZNZlJfl7DrC44NCxzhXFi4byp/NTzGma559CwXOiaP42tr7zBLs9rmOWaQ8Ny4dS8hnsbZrlWzLoCZgALZpWY1FLke5t2Me+Ct9T0WvOnT2Ris//RNzsb/jfHcqEwTvza26bx7ad28u2ndtb0Wh+YP437l3fV9BpmY5VDw3LjS799KZ/onFP9wDdhw9ZX+ct/3MYjv3iVD7xtWk2vZTYWOTQsN6ZNbOaad86o6TWuvPgCHnxiB3/+/efomt+FNNSb+M1sOJ4It4bS0lTgU1e9jY3b9vKIJ93NRs2hYQ1n6cKLmFFq5uvrtjDWX9hpdq45NKzhlHsbF7Px+b1+Ct1slBwa1pD+5fvm8NZSC3/+/efc2zAbBYeGNaSWpgKfuvptPPr8a/w/9zbMUnNoWMM61dtY596GWVoODWtYzcUCK65+Gz0vvMZPet3bMEvDoWEN7RPvm8PMyS183XMbZqk4NKyhNRcLfOrqi+l54TV+3PtK1tUxyz0/EW4N7xOd7dz9g14+ff/jzJjUUtNr/bOOC7jt2ndSGOcn0a0+OTSs4TUXC3zpY5eyZuP2ml7njWP9fPPH23jt0HG+/PF3OzisLjk0zICr3nEhV73jwppf5xvf31J+NoTgKx9/j4PD6o5Dw+w8+uyvdyDB19Y9BwFfud7BYfXFoWF2nn3mmg4E/Nm65wAHh9UXh4ZZBj59TbnH8dXvPUcAX3VwWJ1waJhl5NYPdSCJrzy8mZdeO8zsttaaXm/+BW/hxg/MZfKEpppex8Y2h4ZZhlZcfTHNxXH89YYXeHn/kZpdJwgeePwlVv1oKzddOZff++A8pkwYX7Pr2dilsf4UbGdnZ/T09GRdDbPMPbNzP3es38J3fv4yE5uL3HTlXG52eNgwJD0WEZ1nbHdomDWWZ3bu5y/+YQsPPVUOj9+6bBYTm2s76NA2YTzvnz+VS2dPpqngF1HUg+FCI9U/KZIWAd8ACsA3I+L2QfuV7L8WOAT8bkT8bKSykqYC/xOYCzwPfCIiXkv23QbcDJwAPhMRDyfbrwC+BbQCDwGfjbGeembn2DtnlrjrX13Bsy/v5y/W9/Lg4y9xssb/Fh0+fgKACeMLXPErbXTNn0bX/KlcOnsK44sOkXpStachqQA8B3wY6AMeBW6IiKcrjrkW+DTl0Hg/8I2IeP9IZSV9GdgbEbdLWgm0RcTnJS0A7gcWArOA7wNvj4gTkjYCnwU2UA6NOyLiOyPV3z0Ns+y9evAoG7ftZcPWV9mwdS+bdx04b9dubSowu62V9lN/JtDe1spbSy2Mq/Eda8VxYlJLE6WWIqXWprrqZb2ZnsZCoDcitiYnWgMsAZ6uOGYJcF/yt/4NkqZImkm5FzFc2SXAVUn51cAPgc8n29dExFFgm6ReYKGk54FSRDySnOs+4DpgxNAws+xNm9jM4ktnsvjSmcDpEHn25QPUeqjgjaP9vPTaYfpeP8QT21/n9UPHa3zF4bU2FSi1FpnYXGScan+L9d9/5oM0Fwvn9JxpQmM2UPlSnj7KvYlqx8yuUnZGROwEiIidkgbe4TCbck9i8LmOJ98Hbz+DpOXAcoCLLrpohKaZWRYGh8j5dDAJkV37j3CyxqPb/SeCA0ePs/9wP/sPH2f/kfL3g0f7iZrHJYhzH0xpQmOoqw5u7XDHpCmb9nqpzxURq4BVUB6eqnI9M2sgE5uLvOOtk3jHWydlXZW6lGaArQ+YU/G7HdiR8piRyu5KhrBIPnenOFd7lXqYmVkNpQmNR4EOSfMkjQeWAt2DjukGblRZF7AvGXoaqWw3sCz5vgx4sGL7UknNkuYBHcDG5HwHJHUld2vdWFHGzMzOg6rDUxHRL+lW4GHKt83eGxGbJN2S7L+H8p1M1wK9lG+5vWmkssmpbwfWSroZeBG4PimzSdJaypPl/cCKiDiRlPkkp2+5/Q6eBDczO6/8cJ+ZmZ1huFtu6+emYTMzy5xDw8zMUnNomJlZag4NMzNLbcxPhEvaA7xwlsUvAF45h9WpF253Y3G7G0vadv9KREwfvHHMh8abIalnqLsHxjq3u7G43Y3lzbbbw1NmZpaaQ8PMzFJzaIxsVdYVyIjb3Vjc7sbyptrtOQ0zM0vNPQ0zM0vNoWFmZqk5NIYgaZGkzZJ6k/XLxyxJ90raLennFdumSlonaUvy2ZZlHWtB0hxJP5D0jKRNkj6bbB/TbZfUImmjpH9K2v1HyfYx3W4ASQVJj0v6++T3mG8zgKTnJT0l6QlJPcm2s267Q2MQSQXgTmAxsAC4QdKCbGtVU98CFg3athJYHxEdwPrk91jTD/z7iHgn0AWsSP5/HuttPwp8KCLeA1wGLErWwBnr7Qb4LPBMxe9GaPOAqyPisornM8667Q6NMy0EeiNia0QcA9YASzKuU81ExI+AvYM2LwFWJ99XA9edzzqdDxGxMyJ+lnw/QPk/JrMZ422PsoPJz6bkTzDG2y2pHfgXwDcrNo/pNldx1m13aJxpNrC94ndfsq2RzEhWSiT5vDDj+tSUpLnAe4Gf0gBtT4ZpnqC8xPK6iGiEdn8d+APgZMW2sd7mAQF8T9JjkpYn28667VVX7mtAGmKb70seoyRNBP4X8PsRsb+8kvDYlqyEeZmkKcADkt6VcZVqStJvArsj4jFJV2VcnSxcGRE7JF0IrJP07Js5mXsaZ+oD5lT8bgd2ZFSXrOySNBMg+dydcX1qQlIT5cD4HxHxv5PNDdF2gIh4Hfgh5TmtsdzuK4HfkvQ85eHmD0n6a8Z2m0+JiB3J527gAcpD8GfddofGmR4FOiTNkzQeWAp0Z1yn860bWJZ8XwY8mGFdakLlLsVfAc9ExNcqdo3ptkuanvQwkNQK/DrwLGO43RFxW0S0R8Rcyv8+/0NE/GvGcJsHSHqLpEkD34HfAH7Om2i7nwgfgqRrKY+BFoB7I+KL2daodiTdD1xF+XXJu4D/BPwfYC1wEfAicH1EDJ4sr2uSPgj8I/AUp8e5/wPleY0x23ZJ76Y88Vmg/JfGtRHxx5KmMYbbPSAZnvpcRPxmI7RZ0nzKvQsoT0f8TUR88c203aFhZmapeXjKzMxSc2iYmVlqDg0zM0vNoWFmZqk5NMzMLDWHhpmZpebQMDOz1P4/tMcbsPg1kiAAAAAASUVORK5CYII=
"
>
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{% endraw %}
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<h3 id="warmup_t"><code>warmup_t</code><a class="anchor-link" href="#warmup_t"> </a></h3>
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<p>Defines the number of warmup epochs.</p>
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<h3 id="warmup_lr_init"><code>warmup_lr_init</code><a class="anchor-link" href="#warmup_lr_init"> </a></h3>
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<p>The initial learning rate during warmup.</p>
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{% raw %}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">StepLRScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">decay_t</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">warmup_t</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">warmup_lr_init</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">decay_rate</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="n">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">i</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="n">num_epoch</span><span class="p">)],</span> <span class="n">lr_per_epoch</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"With warmup"</span><span class="p">);</span>
<span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">StepLRScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">decay_t</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">decay_rate</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="n">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">i</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="n">num_epoch</span><span class="p">)],</span> <span class="n">lr_per_epoch</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Without warmup"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
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
"
>
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{% endraw %}
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<p>As we can see by setting up <code>warmup_t</code> and <code>warmup_lr_init</code>, the <code>cosine</code> scheduler first starts with a value of <code>warmup_lr_init</code>, then gradually progresses up to the <code>initial_lr</code> set in the optimizer which is 1e-4. It takes <code>warmup_t</code> number of epochs to go from <code>warmup_lr_init</code> to <code>initial_lr</code>.</p>
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<h3 id="noise_range_t"><code>noise_range_t</code><a class="anchor-link" href="#noise_range_t"> </a></h3>
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<p>The upper and lower limit of noise.</p>
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<h3 id="noise_pct"><code>noise_pct</code><a class="anchor-link" href="#noise_pct"> </a></h3>
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<p>Percentage of noise to add.</p>
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<h3 id="noise_std"><code>noise_std</code><a class="anchor-link" href="#noise_std"> </a></h3>
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<p>Noise standard deviation.</p>
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<h3 id="noise_seed"><code>noise_seed</code><a class="anchor-link" href="#noise_seed"> </a></h3>
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<p>Seed to use to add random noise.</p>
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<h3 id="t_in_epochs"><code>t_in_epochs</code><a class="anchor-link" href="#t_in_epochs"> </a></h3>
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<p>If set to False, the learning rates returned for epoch <code>t</code> are <code>None</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_epoch</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">StepLRScheduler</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">decay_t</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">t_in_epochs</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">lr_per_epoch</span> <span class="o">=</span> <span class="n">get_lr_per_epoch</span><span class="p">(</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">num_epoch</span><span class="p">)</span>
<span class="n">lr_per_epoch</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<pre>[None, None, None, None, None]</pre>
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<h3 id="initialize"><code>initialize</code><a class="anchor-link" href="#initialize"> </a></h3>
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<p>If True, then inside each param group of the <code>optimizer</code> a new field is set called <code>initial_{field_name}</code> where <code>field_name</code> refers to the field in param group that we are scheduling. Typically <code>field_name='lr'</code>.</p>
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<p>{% include note.html content='we leave it up to the reader to try and experiment how the LR annealing scheduling get’s updated by adding in noise hyperparams. From here on, this should be a fairly simple exercise. :)' %}</p>
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