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| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l6 current"><a class="current reference internal" href="#">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../../../index.html">Python Tutorials</a><ul class="current"> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../blocks/naming.html">Parameter and Block Naming</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../loss/loss.html">Loss functions</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../text/gnmt.html">Google Neural Machine Translation</a></li> |
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| <li class="toctree-l5 current"><a class="reference internal" href="index.html">Learning Rates</a><ul class="current"> |
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| <li class="toctree-l6"><a class="reference internal" href="learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../normalization/index.html">Normalization Blocks</a></li> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
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| <li class="toctree-l6"><a class="reference internal" href="../../../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Learning-Rate-Finder"> |
| <h1>Learning Rate Finder<a class="headerlink" href="#Learning-Rate-Finder" title="Permalink to this headline">¶</a></h1> |
| <p>Setting the learning rate for stochastic gradient descent (SGD) is crucially important when training neural network because it controls both the speed of convergence and the ultimate performance of the network. Set the learning too low and you could be twiddling your thumbs for quite some time as the parameters update very slowly. Set it too high and the updates will skip over optimal solutions, or worse the optimizer might not converge at all!</p> |
| <p>Leslie Smith from the U.S. Naval Research Laboratory presented a method for finding a good learning rate in a paper called <a class="reference external" href="https://arxiv.org/abs/1506.01186">“Cyclical Learning Rates for Training Neural Networks”</a>. We implement this method in MXNet (with the Gluon API) and create a ‘Learning Rate Finder’ which you can use while training your own networks. We take a look at the central idea of the paper, cyclical learning rate schedules, in the <a class="reference internal" href="learning_rate_schedules_advanced.html"><span class="doc">‘Advanced Learning Rate |
| Schedules’</span></a> tutorial.</p> |
| <div class="section" id="Simple-Idea"> |
| <h2>Simple Idea<a class="headerlink" href="#Simple-Idea" title="Permalink to this headline">¶</a></h2> |
| <p>Given an initialized network, a defined loss and a training dataset we take the following steps:</p> |
| <ol class="arabic simple"> |
| <li><p>Train one batch at a time (a.k.a. an iteration)</p></li> |
| <li><p>Start with a very small learning rate (e.g. 0.000001) and slowly increase it every iteration</p></li> |
| <li><p>Record the training loss and continue until we see the training loss diverge</p></li> |
| </ol> |
| <p>We then analyse the results by plotting a graph of the learning rate against the training loss as seen below (taking note of the log scales).</p> |
| <p>As expected, for very small learning rates we don’t see much change in the loss as the parameter updates are negligible. At a learning rate of 0.001, we start to see the loss fall. Setting the initial learning rate here is reasonable, but we still have the potential to learn faster. We observe a drop in the loss up until 0.1 where the loss appears to diverge. We want to set the initial learning rate as high as possible before the loss becomes unstable, so we choose a learning rate of 0.05.</p> |
| </div> |
| <div class="section" id="Epoch-to-Iteration"> |
| <h2>Epoch to Iteration<a class="headerlink" href="#Epoch-to-Iteration" title="Permalink to this headline">¶</a></h2> |
| <p>Usually, our unit of work is an epoch (a full pass through the dataset) and the learning rate would typically be held constant throughout the epoch. With the Learning Rate Finder (and cyclical learning rate schedules) we are required to vary the learning rate every iteration. As such we structure our training code so that a single iteration can be run with a given learning rate. You can implement Learner as you wish. Just initialize the network, define the loss and trainer in <code class="docutils literal notranslate"><span class="pre">__init__</span></code> and |
| keep your training logic for a single batch in <code class="docutils literal notranslate"><span class="pre">iteration</span></code>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| |
| <span class="c1"># Set seed for reproducibility</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span> |
| |
| <span class="k">class</span> <span class="nc">Learner</span><span class="p">():</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param net: network (mx.gluon.Block)</span> |
| <span class="sd"> :param data_loader: training data loader (mx.gluon.data.DataLoader)</span> |
| <span class="sd"> :param device: device (mx.gpu or mx.cpu)</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span> <span class="o">=</span> <span class="n">data_loader</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> |
| <span class="c1"># So we don't need to be in `for batch in data_loader` scope</span> |
| <span class="c1"># and can call for next batch in `iteration`</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">data_loader_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</span><span class="p">(),</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">trainer</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="s1">'sgd'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">.001</span><span class="p">})</span> |
| |
| <span class="k">def</span> <span class="nf">iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">take_step</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param lr: learning rate to use for iteration (float)</span> |
| <span class="sd"> :param take_step: take trainer step to update weights (boolean)</span> |
| <span class="sd"> :return: iteration loss (float)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># Update learning rate if different this iteration</span> |
| <span class="k">if</span> <span class="n">lr</span> <span class="ow">and</span> <span class="p">(</span><span class="n">lr</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">set_learning_rate</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span> |
| <span class="c1"># Get next batch, and move device (e.g. to GPU if set)</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_loader_iter</span><span class="p">)</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="c1"># Standard forward and backward pass</span> |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="c1"># Update parameters</span> |
| <span class="k">if</span> <span class="n">take_step</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="c1"># Set and return loss.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">iteration_loss</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">iteration_loss</span> |
| |
| <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># Close open iterator and associated workers</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">data_loader_iter</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We also adjust our <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> so that it continuously provides batches of data and doesn’t stop after a single epoch. We can then call <code class="docutils literal notranslate"><span class="pre">iteration</span></code> as many times as required for the loss to diverge as part of the Learning Rate Finder process. We implement a custom <code class="docutils literal notranslate"><span class="pre">BatchSampler</span></code> for this, that keeps returning random indices of samples to be included in the next batch. We use the CIFAR-10 dataset for image classification to test our Learning Rate Finder.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="c1"># Switches HWC to CHW, and converts to `float32`</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="c1"># Channel-wise, using pre-computed means and stds</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.4914</span><span class="p">,</span> <span class="mf">0.4822</span><span class="p">,</span> <span class="mf">0.4465</span><span class="p">],</span> |
| <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.2023</span><span class="p">,</span> <span class="mf">0.1994</span><span class="p">,</span> <span class="mf">0.2010</span><span class="p">])</span> |
| <span class="p">])</span> |
| |
| <span class="n">dataset</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">transform</span><span class="p">)</span> |
| |
| <span class="k">class</span> <span class="nc">ContinuousBatchSampler</span><span class="p">():</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sampler</span> <span class="o">=</span> <span class="n">sampler</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> |
| |
| <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">while</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sampler</span><span class="p">:</span> |
| <span class="n">batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_size</span><span class="p">:</span> |
| <span class="k">yield</span> <span class="n">batch</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="n">sampler</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">RandomSampler</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span> |
| <span class="n">batch_sampler</span> <span class="o">=</span> <span class="n">ContinuousBatchSampler</span><span class="p">(</span><span class="n">sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span> |
| <span class="n">data_loader</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_sampler</span><span class="o">=</span><span class="n">batch_sampler</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [04:48:41] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| </div> |
| <div class="section" id="Implementation"> |
| <h2>Implementation<a class="headerlink" href="#Implementation" title="Permalink to this headline">¶</a></h2> |
| <p>With preparation complete, we’re ready to write our Learning Rate Finder that wraps the <code class="docutils literal notranslate"><span class="pre">Learner</span></code> we defined above. We implement a <code class="docutils literal notranslate"><span class="pre">find</span></code> method for the procedure, and <code class="docutils literal notranslate"><span class="pre">plot</span></code> for the visualization. Starting with a very low learning rate as defined by <code class="docutils literal notranslate"><span class="pre">lr_start</span></code> we train one iteration at a time and keep multiplying the learning rate by <code class="docutils literal notranslate"><span class="pre">lr_multiplier</span></code>. We analyse the loss and continue until it diverges according to <code class="docutils literal notranslate"><span class="pre">LRFinderStoppingCriteria</span></code> (which is defined later on). You may also |
| notice that we save the parameters and state of the optimizer before the process and restore afterwards. This is so the Learning Rate Finder process doesn’t impact the state of the model, and can be used at any point during training.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> |
| |
| <span class="k">class</span> <span class="nc">LRFinder</span><span class="p">():</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">learner</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param learner: able to take single iteration with given learning rate and return loss</span> |
| <span class="sd"> and save and load parameters of the network (Learner)</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span> <span class="o">=</span> <span class="n">learner</span> |
| |
| <span class="k">def</span> <span class="nf">find</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr_start</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">lr_multiplier</span><span class="o">=</span><span class="mf">1.1</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param lr_start: learning rate to start search (float)</span> |
| <span class="sd"> :param lr_multiplier: factor the learning rate is multiplied by at each step of search (float)</span> |
| <span class="sd"> :param smoothing: amount of smoothing applied to loss for stopping criteria (float)</span> |
| <span class="sd"> :return: learning rate and loss pairs (list of (float, float) tuples)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># Used to initialize weights; pass data, but don't take step.</span> |
| <span class="c1"># Would expect for new model with lazy weight initialization</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">iteration</span><span class="p">(</span><span class="n">take_step</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="c1"># Used to initialize trainer (if no step has been taken)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="c1"># Store params and optimizer state for restore after lr_finder procedure</span> |
| <span class="c1"># Useful for applying the method partway through training, not just for initialization of lr.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s2">"lr_finder.params"</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">save_states</span><span class="p">(</span><span class="s2">"lr_finder.state"</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="n">lr_start</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">results</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># List of (lr, loss) tuples</span> |
| <span class="n">stopping_criteria</span> <span class="o">=</span> <span class="n">LRFinderStoppingCriteria</span><span class="p">(</span><span class="n">smoothing</span><span class="p">)</span> |
| <span class="k">while</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="c1"># Run iteration, and block until loss is calculated.</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">iteration</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">lr</span><span class="p">,</span> <span class="n">loss</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">stopping_criteria</span><span class="p">(</span><span class="n">loss</span><span class="p">):</span> |
| <span class="k">break</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">lr_multiplier</span> |
| <span class="c1"># Restore params (as finder changed them)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s2">"lr_finder.params"</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">load_states</span><span class="p">(</span><span class="s2">"lr_finder.state"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">results</span> |
| |
| <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">lrs</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">]</span> |
| <span class="n">losses</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">results</span><span class="p">]</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">lrs</span><span class="p">,</span> <span class="n">losses</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Learning Rate"</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Loss"</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">xscale</span><span class="p">(</span><span class="s1">'log'</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s1">'log'</span><span class="p">)</span> |
| <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="n">lrs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">lrs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]])</span> |
| <span class="n">y_lower</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.8</span> |
| <span class="n">y_upper</span> <span class="o">=</span> <span class="n">losses</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">4</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">y_lower</span><span class="p">,</span> <span class="n">y_upper</span><span class="p">])</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>You can define the <code class="docutils literal notranslate"><span class="pre">LRFinderStoppingCriteria</span></code> as you wish, but empirical testing suggests using a smoothed average gives a more consistent stopping rule (see <code class="docutils literal notranslate"><span class="pre">smoothing</span></code>). We stop when the smoothed average of the loss exceeds twice the initial loss, assuming there have been a minimum number of iterations (see <code class="docutils literal notranslate"><span class="pre">min_iter</span></code>).</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">class</span> <span class="nc">LRFinderStoppingCriteria</span><span class="p">():</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">min_iter</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param smoothing: applied to running mean which is used for thresholding (float)</span> |
| <span class="sd"> :param min_iter: minimum number of iterations before early stopping can occur (int)</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">smoothing</span> <span class="o">=</span> <span class="n">smoothing</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">min_iter</span> <span class="o">=</span> <span class="n">min_iter</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">first_loss</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> :param loss: from single iteration (float)</span> |
| <span class="sd"> :return: indicator to stop (boolean)</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">first_loss</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">first_loss</span> <span class="o">=</span> <span class="n">loss</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">=</span> <span class="n">loss</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">smoothing</span><span class="p">)</span> <span class="o">*</span> <span class="n">loss</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">smoothing</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">first_loss</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_iter</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Usage"> |
| <h2>Usage<a class="headerlink" href="#Usage" title="Permalink to this headline">¶</a></h2> |
| <p>Using a Pre-activation ResNet-18 from the Gluon model zoo, we instantiate our Learner and fire up our Learning Rate Finder!</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> <span class="k">if</span> <span class="n">mx</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="k">else</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v2</span><span class="p">(</span><span class="n">classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="n">learner</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">net</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">data_loader</span><span class="o">=</span><span class="n">data_loader</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">lr_finder</span> <span class="o">=</span> <span class="n">LRFinder</span><span class="p">(</span><span class="n">learner</span><span class="p">)</span> |
| <span class="n">lr_finder</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">lr_start</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">)</span> |
| <span class="n">lr_finder</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [04:48:44] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU |
| [04:48:46] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| [04:48:46] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <img alt="../../../../../_images/tutorials_packages_gluon_training_learning_rates_learning_rate_finder_9_1.png" src="../../../../../_images/tutorials_packages_gluon_training_learning_rates_learning_rate_finder_9_1.png" /> |
| </div> |
| </div> |
| <p><img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/lr_finder/finder_plot.png" /></p> |
| <p>As discussed before, we should select a learning rate where the loss is falling (i.e. from 0.001 to 0.05) but before the loss starts to diverge (i.e. 0.1). We prefer higher learning rates where possible, so we select an initial learning rate of 0.05. Just as a test, we will run 500 epochs using this learning rate and evaluate the loss on the final batch. As we’re working with a single batch of 128 samples, the variance of the loss estimates will be reasonably high, but it will give us a general |
| idea. We save the initialized parameters for a later comparison with other learning rates.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">learner</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s2">"net.params"</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.05</span> |
| |
| <span class="k">for</span> <span class="n">iter_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">):</span> |
| <span class="n">learner</span><span class="o">.</span><span class="n">iteration</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">((</span><span class="n">iter_idx</span> <span class="o">%</span> <span class="mi">100</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Iteration: </span><span class="si">{}</span><span class="s2">, Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iter_idx</span><span class="p">,</span> <span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Final Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Iteration: 0, Loss: 2.8763 |
| Iteration: 100, Loss: 1.4716 |
| Iteration: 200, Loss: 1.5103 |
| Final Loss: 1.1372 |
| </pre></div></div> |
| </div> |
| <p>Iteration: 0, Loss: 2.785</p> |
| <p>Iteration: 100, Loss: 1.6653</p> |
| <p>Iteration: 200, Loss: 1.4891</p> |
| <p>Final Loss: 1.1812</p> |
| <p>We see a sizable drop in the loss from approx. 2.7 to 1.2.</p> |
| <p>And now we have a baseline, let’s see what happens when we train with a learning rate that’s higher than advisable at 0.5.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v2</span><span class="p">(</span><span class="n">classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="n">learner</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">net</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">data_loader</span><span class="o">=</span><span class="n">data_loader</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">learner</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s2">"net.params"</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.5</span> |
| |
| <span class="k">for</span> <span class="n">iter_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">):</span> |
| <span class="n">learner</span><span class="o">.</span><span class="n">iteration</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">((</span><span class="n">iter_idx</span> <span class="o">%</span> <span class="mi">100</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Iteration: </span><span class="si">{}</span><span class="s2">, Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iter_idx</span><span class="p">,</span> <span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Final Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Iteration: 0, Loss: 2.6405 |
| Iteration: 100, Loss: 1.9491 |
| Iteration: 200, Loss: 1.69 |
| Final Loss: 1.6182 |
| </pre></div></div> |
| </div> |
| <p>Iteration: 0, Loss: 2.6469</p> |
| <p>Iteration: 100, Loss: 1.9666</p> |
| <p>Iteration: 200, Loss: 1.6919</p> |
| <p>Final Loss: 1.366</p> |
| <p>We still observe a fall in the loss but aren’t able to reach as low as before.</p> |
| <p>And lastly, we see how the model trains with a more conservative learning rate of 0.005.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">vision</span><span class="o">.</span><span class="n">resnet18_v2</span><span class="p">(</span><span class="n">classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| <span class="n">learner</span> <span class="o">=</span> <span class="n">Learner</span><span class="p">(</span><span class="n">net</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">data_loader</span><span class="o">=</span><span class="n">data_loader</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">learner</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s2">"net.params"</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.005</span> |
| |
| <span class="k">for</span> <span class="n">iter_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">300</span><span class="p">):</span> |
| <span class="n">learner</span><span class="o">.</span><span class="n">iteration</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">((</span><span class="n">iter_idx</span> <span class="o">%</span> <span class="mi">100</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Iteration: </span><span class="si">{}</span><span class="s2">, Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">iter_idx</span><span class="p">,</span> <span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Final Loss: </span><span class="si">{:.5g}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="n">iteration_loss</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Iteration: 0, Loss: 2.5701 |
| Iteration: 100, Loss: 1.5993 |
| Iteration: 200, Loss: 1.6313 |
| Final Loss: 1.6397 |
| </pre></div></div> |
| </div> |
| <p>Iteration: 0, Loss: 2.605</p> |
| <p>Iteration: 100, Loss: 1.8621</p> |
| <p>Iteration: 200, Loss: 1.6316</p> |
| <p>Final Loss: 1.2919</p> |
| <p>Although we get quite similar results to when we set the learning rate at 0.05 (because we’re still in the region of falling loss on the Learning Rate Finder plot), we can still optimize our network faster using a slightly higher rate.</p> |
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| <div class="section" id="Wrap-Up"> |
| <h2>Wrap Up<a class="headerlink" href="#Wrap-Up" title="Permalink to this headline">¶</a></h2> |
| <p>Give Learning Rate Finder a try on your current projects, and experiment with the different learning rate schedules found in the <a class="reference internal" href="learning_rate_schedules.html"><span class="doc">basic learning rate tutorial</span></a> and the <a class="reference internal" href="learning_rate_schedules_advanced.html"><span class="doc">advanced learning rate tutorial</span></a>.</p> |
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