Normalizations | TensorFlow Addons

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TensorFlow Addons has stopped development, The project will only be providing minimal maintenance releases until May 2024. See the full announcement <a style="color:#d50000;background:none" href="/addons">here</a> or on <a style="color:#d50000;background:none" href="https://github.com/tensorflow/addons/issues/2807">github</a>. </p> </div> </div> </div> <div class="devsite-article-meta nocontent" role="navigation"> <ul class="devsite-breadcrumb-list" aria-label="Breadcrumb"> <li class="devsite-breadcrumb-item "> <a href="https://www.tensorflow.org/" class="devsite-breadcrumb-link gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Breadcrumbs" data-value="1" track-type="globalNav" track-name="breadcrumb" track-metadata-position="1" track-metadata-eventdetail="TensorFlow" > TensorFlow </a> </li> <li class="devsite-breadcrumb-item "> <div class="devsite-breadcrumb-guillemet material-icons" aria-hidden="true"></div> <a href="https://www.tensorflow.org/resources" class="devsite-breadcrumb-link gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Breadcrumbs" data-value="2" track-type="globalNav" track-name="breadcrumb" track-metadata-position="2" track-metadata-eventdetail="" > Resources </a> </li> <li class="devsite-breadcrumb-item "> <div class="devsite-breadcrumb-guillemet material-icons" aria-hidden="true"></div> <a href="https://www.tensorflow.org/addons" class="devsite-breadcrumb-link gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Breadcrumbs" data-value="3" track-type="globalNav" track-name="breadcrumb" track-metadata-position="3" track-metadata-eventdetail="TensorFlow Addons" > Addons </a> </li> <li class="devsite-breadcrumb-item "> <div class="devsite-breadcrumb-guillemet material-icons" aria-hidden="true"></div> <a href="https://www.tensorflow.org/addons/overview" class="devsite-breadcrumb-link gc-analytics-event" data-category="Site-Wide Custom Events" data-label="Breadcrumbs" data-value="4" track-type="globalNav" track-name="breadcrumb" track-metadata-position="4" track-metadata-eventdetail="" > Guide & Tutorials </a> </li> </ul> <devsite-thumb-rating position="header"> </devsite-thumb-rating> </div> <h1 class="devsite-page-title" tabindex="-1"> Normalizations </h1> <devsite-feature-tooltip ack-key="AckCollectionsBookmarkTooltipDismiss" analytics-category="Site-Wide Custom Events" analytics-action-show="Callout Profile displayed" analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> <span slot="popout-heading"> Stay organized with collections </span> <span slot="popout-contents"> Save and categorize content based on your preferences. </span> </devsite-feature-tooltip> <div class="devsite-page-title-meta"><devsite-view-release-notes></devsite-view-release-notes></div> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded > </devsite-toc> <div class="devsite-article-body clearfix "> <p><devsite-mathjax config="TeX-AMS-MML_SVG"></devsite-mathjax> </p>  <div itemscope itemtype="http://developers.google.com/ReferenceObject"> <meta itemprop="name" content="Normalizations" /> <meta itemprop="path" content="Guide & Tutorials" /> <meta itemprop="property" content="tfa.layers.GroupNormalization"/> <meta itemprop="property" content="tfa.layers.InstanceNormalization"/> </div> <table class="tfo-notebook-buttons" align="left"> <td> <a target="_blank" href="https://www.tensorflow.org/addons/tutorials/layers_normalizations"><img src="https://www.tensorflow.org/images/tf_logo_32px.png">View on TensorFlow.org</a> </td> <td> <a target="_blank" href="https://colab.research.google.com/github/tensorflow/addons/blob/master/docs/tutorials/layers_normalizations.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png">Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/tensorflow/addons/blob/master/docs/tutorials/layers_normalizations.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png">View source on GitHub</a> </td> <td> <a href="https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/layers_normalizations.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png">Download notebook</a> </td> </table> <aside class="note"><strong>Note:</strong><span> <code translate="no" dir="ltr">tensorflow-addons</code> is deprecated, use the builtin <a href="https://keras.io/api/layers/normalization_layers/">normalization layers</a> instead.</span></aside> <h2 id="overview" data-text="Overview" tabindex="-1">Overview</h2> <p>This notebook gives a brief introduction into the <a href="https://github.com/tensorflow/addons/blob/master/tensorflow_addons/layers/normalizations.py">normalization layers</a> of TensorFlow. Currently supported layers are:</p> <ul> <li><strong>Group Normalization</strong> (TensorFlow Addons)</li> <li><strong>Instance Normalization</strong> (TensorFlow Addons)</li> <li><strong>Layer Normalization</strong> (TensorFlow Core)</li> </ul> <p>The basic idea behind these layers is to normalize the output of an activation layer to improve the convergence during training. In contrast to <a href="https://keras.io/layers/normalization/">batch normalization</a> these normalizations do not work on batches, instead they normalize the activations of a single sample, making them suitable for recurrent neural networks as well. </p> <p>Typically the normalization is performed by calculating the mean and the standard deviation of a subgroup in your input tensor. It is also possible to apply a scale and an offset factor to this as well.</p> <p>$y_{i} = \frac{\gamma ( x_{i} - \mu )}{\sigma }+ \beta$</p> <p>$ y$ : Output</p> <p>$x$ : Input</p> <p>$\gamma$ : Scale factor</p> <p>$\mu$: mean</p> <p>$\sigma$: standard deviation</p> <p>$\beta$: Offset factor</p> <p>The following image demonstrates the difference between these techniques. Each subplot shows an input tensor, with N as the batch axis, C as the channel axis, and (H, W) as the spatial axes (Height and Width of a picture for example). The pixels in blue are normalized by the same mean and variance, computed by aggregating the values of these pixels.</p> <p><img src="https://github.com/shaohua0116/Group-Normalization-Tensorflow/raw/master/figure/gn.png" alt></p> <p>Source: (<a href="https://arxiv.org/pdf/1803.08494.pdf">https://arxiv.org/pdf/1803.08494.pdf</a>)</p> <p>The weights gamma and beta are trainable in all normalization layers to compensate for the possible lost of representational ability. You can activate these factors by setting the <code translate="no" dir="ltr">center</code> or the <code translate="no" dir="ltr">scale</code> flag to <code translate="no" dir="ltr">True</code>. Of course you can use <code translate="no" dir="ltr">initializers</code>, <code translate="no" dir="ltr">constraints</code> and <code translate="no" dir="ltr">regularizer</code> for <code translate="no" dir="ltr">beta</code> and <code translate="no" dir="ltr">gamma</code> to tune these values during the training process. </p> <h2 id="setup" data-text="Setup" tabindex="-1">Setup</h2> <h3 id="install_tensorflow_20_and_tensorflow-addons" data-text="Install Tensorflow 2.0 and Tensorflow-Addons" tabindex="-1">Install Tensorflow 2.0 and Tensorflow-Addons</h3> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Bash"><code class='devsite-terminal' translate="no" dir="ltr">pip<span class="devsite-syntax-w"> </span>install<span class="devsite-syntax-w"> </span>-U<span class="devsite-syntax-w"> </span>tensorflow-addons</code></pre></devsite-code> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-kn">import</span> <span class="devsite-syntax-nn">tensorflow</span> <span class="devsite-syntax-k">as</span> <span class="devsite-syntax-nn">tf</span> <span class="devsite-syntax-kn">import</span> <span class="devsite-syntax-nn">tensorflow_addons</span> <span class="devsite-syntax-k">as</span> <span class="devsite-syntax-nn">tfa</span> </code></pre></devsite-code> <h3 id="preparing_dataset" data-text="Preparing Dataset" tabindex="-1">Preparing Dataset</h3> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-n">mnist</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">datasets</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">mnist</span> <span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_train</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_train</span><span class="devsite-syntax-p">),(</span><span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">mnist</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">load_data</span><span class="devsite-syntax-p">()</span> <span class="devsite-syntax-n">x_train</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">x_test</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">x_train</span> <span class="devsite-syntax-o">/</span> <span class="devsite-syntax-mf">255.0</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">x_test</span> <span class="devsite-syntax-o">/</span> <span class="devsite-syntax-mf">255.0</span> </code></pre></devsite-code> <h2 id="group_normalization_tutorial" data-text="Group Normalization Tutorial" tabindex="-1">Group Normalization Tutorial</h2> <h3 id="introduction" data-text="Introduction" tabindex="-1">Introduction</h3> <p>Group Normalization(GN) divides the channels of your inputs into smaller sub groups and normalizes these values based on their mean and variance. Since GN works on a single example this technique is batchsize independent. </p> <p>GN experimentally scored closed to batch normalization in image classification tasks. It can be beneficial to use GN instead of Batch Normalization in case your overall batch_size is low, which would lead to bad performance of batch normalization </p> <h3 id="example" data-text="Example" tabindex="-1">Example</h3> <p>Splitting 10 channels after a Conv2D layer into 5 subgroups in a standard "channels last" setting:</p> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-n">model</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">models</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Sequential</span><span class="devsite-syntax-p">([</span> <span class="devsite-syntax-c1"># Reshape into "channels last" setup.</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Reshape</span><span class="devsite-syntax-p">((</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">1</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">input_shape</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">)),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Conv2D</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">filters</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">kernel_size</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">),</span><span class="devsite-syntax-n">data_format</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s2">"channels_last"</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-c1"># Groupnorm Layer</span> <span class="devsite-syntax-n">tfa</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">GroupNormalization</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">groups</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">5</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">axis</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Flatten</span><span class="devsite-syntax-p">(),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">128</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'relu'</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dropout</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mf">0.2</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'softmax'</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">compile</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">optimizer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'adam'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">loss</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'sparse_categorical_crossentropy'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">metrics</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">[</span><span class="devsite-syntax-s1">'accuracy'</span><span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">fit</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">)</span> </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> 313/313 [==============================] - 2s 3ms/step - loss&colon; 0.4725 - accuracy&colon; 0.8565 <keras.callbacks.History at 0x7f3b65b1a6a0> </pre></devsite-code> <h2 id="instance_normalization_tutorial" data-text="Instance Normalization Tutorial" tabindex="-1">Instance Normalization Tutorial</h2> <h3 id="introduction_2" data-text="Introduction" tabindex="-1">Introduction</h3> <p>Instance Normalization is special case of group normalization where the group size is the same size as the channel size (or the axis size).</p> <p>Experimental results show that instance normalization performs well on style transfer when replacing batch normalization. Recently, instance normalization has also been used as a replacement for batch normalization in GANs.</p> <h3 id="example_2" data-text="Example" tabindex="-1">Example</h3> <p>Applying InstanceNormalization after a Conv2D Layer and using a uniformed initialized scale and offset factor.</p> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-n">model</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">models</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Sequential</span><span class="devsite-syntax-p">([</span> <span class="devsite-syntax-c1"># Reshape into "channels last" setup.</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Reshape</span><span class="devsite-syntax-p">((</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">1</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">input_shape</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">)),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Conv2D</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">filters</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">kernel_size</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">),</span><span class="devsite-syntax-n">data_format</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s2">"channels_last"</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-c1"># LayerNorm Layer</span> <span class="devsite-syntax-n">tfa</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">InstanceNormalization</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">axis</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">center</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-kc">True</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">scale</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-kc">True</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">beta_initializer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s2">"random_uniform"</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">gamma_initializer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s2">"random_uniform"</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Flatten</span><span class="devsite-syntax-p">(),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">128</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'relu'</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dropout</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mf">0.2</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'softmax'</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">compile</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">optimizer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'adam'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">loss</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'sparse_categorical_crossentropy'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">metrics</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">[</span><span class="devsite-syntax-s1">'accuracy'</span><span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">fit</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">)</span> </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> 313/313 [==============================] - 1s 2ms/step - loss&colon; 0.5736 - accuracy&colon; 0.8249 <keras.callbacks.History at 0x7f3b3c48ce80> </pre></devsite-code> <h2 id="layer_normalization_tutorial" data-text="Layer Normalization Tutorial" tabindex="-1">Layer Normalization Tutorial</h2> <h3 id="introduction_3" data-text="Introduction" tabindex="-1">Introduction</h3> <p>Layer Normalization is special case of group normalization where the group size is 1. The mean and standard deviation is calculated from all activations of a single sample.</p> <p>Experimental results show that Layer normalization is well suited for Recurrent Neural Networks, since it works batchsize independently.</p> <h3 id="example_3" data-text="Example" tabindex="-1">Example</h3> <p>Applying Layernormalization after a Conv2D Layer and using a scale and offset factor. </p> <div></div><devsite-code><pre class="devsite-click-to-copy" translate="no" dir="ltr" is-upgraded syntax="Python"><code translate="no" dir="ltr"><span class="devsite-syntax-n">model</span> <span class="devsite-syntax-o">=</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">models</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Sequential</span><span class="devsite-syntax-p">([</span> <span class="devsite-syntax-c1"># Reshape into "channels last" setup.</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Reshape</span><span class="devsite-syntax-p">((</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">1</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">input_shape</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">28</span><span class="devsite-syntax-p">)),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Conv2D</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">filters</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">kernel_size</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">,</span><span class="devsite-syntax-mi">3</span><span class="devsite-syntax-p">),</span><span class="devsite-syntax-n">data_format</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s2">"channels_last"</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-c1"># LayerNorm Layer</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">LayerNormalization</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">axis</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-mi">3</span> <span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">center</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-kc">True</span> <span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">scale</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-kc">True</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Flatten</span><span class="devsite-syntax-p">(),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">128</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'relu'</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dropout</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mf">0.2</span><span class="devsite-syntax-p">),</span> <span class="devsite-syntax-n">tf</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">keras</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">layers</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">Dense</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-mi">10</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">activation</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'softmax'</span><span class="devsite-syntax-p">)</span> <span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">compile</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">optimizer</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'adam'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">loss</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-s1">'sparse_categorical_crossentropy'</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">metrics</span><span class="devsite-syntax-o">=</span><span class="devsite-syntax-p">[</span><span class="devsite-syntax-s1">'accuracy'</span><span class="devsite-syntax-p">])</span> <span class="devsite-syntax-n">model</span><span class="devsite-syntax-o">.</span><span class="devsite-syntax-n">fit</span><span class="devsite-syntax-p">(</span><span class="devsite-syntax-n">x_test</span><span class="devsite-syntax-p">,</span> <span class="devsite-syntax-n">y_test</span><span class="devsite-syntax-p">)</span> </code></pre></devsite-code> <div></div><devsite-code><pre class="tfo-notebook-code-cell-output" translate="no" dir="ltr" is-upgraded> 313/313 [==============================] - 1s 3ms/step - loss&colon; 0.4019 - accuracy&colon; 0.8773 <keras.callbacks.History at 0x7f3b3c32eb50> </pre></devsite-code> <h2 id="literature" data-text="Literature" tabindex="-1">Literature</h2> <p><a href="https://arxiv.org/pdf/1607.06450.pdf">Layer norm</a></p> <p><a href="https://arxiv.org/pdf/1607.08022.pdf">Instance norm</a></p> <p><a href="https://arxiv.org/pdf/1803.08494.pdf">Group Norm</a></p> </div> <devsite-thumb-rating position="footer"> </devsite-thumb-rating> <div class="devsite-floating-action-buttons"> </div> </article> <devsite-content-footer class="nocontent"> <p>Except as otherwise noted, the content of this page is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 License</a>, and code samples are licensed under the <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0 License</a>. For details, see the <a href="https://developers.google.com/site-policies">Google Developers Site Policies</a>. 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