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---
title: layers
keywords: fastai
sidebar: home_sidebar
summary: "Provides essential functions to building and modifying `Model` architectures."
---
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<h2 id="Model-Layers">Model Layers<a class="anchor-link" href="#Model-Layers">¶</a></h2>
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<p>This module contains many layer classes that we might be interested in using in our models. These layers complement the default <a href="https://pytorch.org/docs/stable/nn.html">Pytorch layers</a> which we can also use as predefined layers.</p>
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<h2 id="Custom-fastai-modules">Custom fastai modules<a class="anchor-link" href="#Custom-fastai-modules">¶</a></h2>
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<h3 id="AdaptiveConcatPool2d" class="doc_header"><code>class</code> <code>AdaptiveConcatPool2d</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L176" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#AdaptiveConcatPool2d-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>AdaptiveConcatPool2d</code>(<strong><code>sz</code></strong>:<code>Optional</code>[<code>int</code>]=<strong><em><code>None</code></em></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="AdaptiveConcatPool2d-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#AdaptiveConcatPool2d-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>AdaptiveConcatPool2d</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Layer that concats <code>AdaptiveAvgPool2d</code> and <code>AdaptiveMaxPool2d</code>.</p>
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<p>The output will be <code>2*sz</code>, or just 2 if <code>sz</code> is None.</p>
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<p>The <a href="/layers.html#AdaptiveConcatPool2d"><code>AdaptiveConcatPool2d</code></a> object uses adaptive average pooling and adaptive max pooling and concatenates them both. We use this because it provides the model with the information of both methods and improves performance. This technique is called <code>adaptive</code> because it allows us to decide on what output dimensions we want, instead of choosing the input's dimensions to fit a desired output size.</p>
<p>Let's try training with Adaptive Average Pooling first, then with Adaptive Max Pooling and finally with the concatenation of them both to see how they fare in performance.</p>
<p>We will first define a <a href="/layers.html#simple_cnn"><code>simple_cnn</code></a> using <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.AdaptiveMaxPool2d">Adaptive Max Pooling</a> by changing the source code a bit.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">MNIST_SAMPLE</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">simple_cnn_max</span><span class="p">(</span><span class="n">actns</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">strides</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">:</span>
<span class="s2">"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"</span>
<span class="n">nl</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">actns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span>
<span class="n">kernel_szs</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">kernel_szs</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">strides</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">strides</span> <span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">conv_layer</span><span class="p">(</span><span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">strides</span><span class="p">))]</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveMaxPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">Flatten</span><span class="p">()))</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn_max</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</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">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">])</span>
<span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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Total time: 00:02 <p><table style='width:300px; margin-bottom:10px'>
<tr>
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.102758</th>
<th>0.064676</th>
<th>0.984298</th>
</tr>
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<p>Now let's try with <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.AdaptiveAvgPool2d">Adaptive Average Pooling</a> now.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">simple_cnn_avg</span><span class="p">(</span><span class="n">actns</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">strides</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">:</span>
<span class="s2">"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"</span>
<span class="n">nl</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">actns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span>
<span class="n">kernel_szs</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">kernel_szs</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">strides</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">strides</span> <span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">conv_layer</span><span class="p">(</span><span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">strides</span><span class="p">))]</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">Flatten</span><span class="p">()))</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn_avg</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</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">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">])</span>
<span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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Total time: 00:02 <p><table style='width:300px; margin-bottom:10px'>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.241485</th>
<th>0.201116</th>
<th>0.973994</th>
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<p>Finally we will try with the concatenation of them both <a href="/layers.html#AdaptiveConcatPool2d"><code>AdaptiveConcatPool2d</code></a>. We will see that, in fact, it increases our accuracy and decreases our loss considerably!</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">simple_cnn</span><span class="p">(</span><span class="n">actns</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">strides</span><span class="p">:</span><span class="n">Collection</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">:</span>
<span class="s2">"CNN with `conv2d_relu` layers defined by `actns`, `kernel_szs` and `strides`"</span>
<span class="n">nl</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">actns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span>
<span class="n">kernel_szs</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">kernel_szs</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">strides</span> <span class="o">=</span> <span class="n">ifnone</span><span class="p">(</span><span class="n">strides</span> <span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">nl</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">conv_layer</span><span class="p">(</span><span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">actns</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> <span class="n">kernel_szs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="n">strides</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">strides</span><span class="p">))]</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">AdaptiveConcatPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">Flatten</span><span class="p">()))</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">simple_cnn</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">2</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">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">])</span>
<span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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Total time: 00:02 <p><table style='width:300px; margin-bottom:10px'>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
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<th>1</th>
<th>0.203015</th>
<th>0.122094</th>
<th>0.988224</th>
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<h3 id="Lambda" class="doc_header"><code>class</code> <code>Lambda</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L10" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Lambda-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>Lambda</code>(<strong><code>func</code></strong>:<code>LambdaFunc</code>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="Lambda-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Lambda-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Lambda</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Create a layer that simply calls <code>func</code> with <code>x</code></p>
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<p>This is very useful to use functions as layers in our networks inside a <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential">Sequential</a> object. So, for example, say we want to apply a <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.functional.log_softmax">log_softmax loss</a> and we need to change the shape of our output batches to be able to use this loss. We can add a layer that applies the necessary change in shape by calling:</p>
<p><code>Lambda(lambda x: x.view(x.size(0),-1))</code></p>
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<p>Let's see an example of how the shape of our output can change when we add this layer.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">for</span> <span class="n">xb</span><span class="p">,</span> <span class="n">yb</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">xb</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="k">break</span>
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<pre>torch.Size([64, 10, 1, 1])
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
<span class="n">Lambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">for</span> <span class="n">xb</span><span class="p">,</span> <span class="n">yb</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">xb</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="k">break</span>
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<pre>torch.Size([64, 10])
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<h2 id="Flatten" class="doc_header"><code>class</code> <code>Flatten</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L25" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Flatten-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Flatten</code>(<strong><code>full</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="Flatten-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Flatten-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Flatten</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Flatten <code>x</code> to a single dimension, often used at the end of a model. <code>full</code> for rank-1 tensor</p>
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<p>The function we build above is actually implemented in our library as <a href="/layers.html#Flatten"><code>Flatten</code></a>. We can see that it returns the same size when we run it.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
<span class="n">Flatten</span><span class="p">(),</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">for</span> <span class="n">xb</span><span class="p">,</span> <span class="n">yb</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">xb</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="k">break</span>
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<pre>torch.Size([64, 10])
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<h4 id="PoolFlatten" class="doc_header"><code>PoolFlatten</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L30" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#PoolFlatten-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>PoolFlatten</code>() → <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential"><code>Sequential</code></a></p>
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<div class="collapse" id="PoolFlatten-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#PoolFlatten-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>PoolFlatten</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Apply <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.AdaptiveAvgPool2d"><code>nn.AdaptiveAvgPool2d</code></a> to <code>x</code> and then flatten the result.</p>
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<p>We can combine these two final layers (<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.AdaptiveAvgPool2d">AdaptiveAvgPool2d</a> and <a href="/layers.html#Flatten"><code>Flatten</code></a>) by using <a href="/layers.html#PoolFlatten"><code>PoolFlatten</code></a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">PoolFlatten</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="k">for</span> <span class="n">xb</span><span class="p">,</span> <span class="n">yb</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">train_dl</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">xb</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="k">break</span>
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<pre>torch.Size([64, 10])
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<p>Another use we give to the Lambda function is to resize batches with <a href="/layers.html#ResizeBatch"><code>ResizeBatch</code></a> when we have a layer that expects a different input than what comes from the previous one.</p>
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<h2 id="ResizeBatch" class="doc_header"><code>class</code> <code>ResizeBatch</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L20" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#ResizeBatch-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>ResizeBatch</code>(<strong>*<code>size</code></strong>:<code>int</code>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="ResizeBatch-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#ResizeBatch-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>ResizeBatch</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Reshape <code>x</code> to <code>size</code>, keeping batch dim the same size</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">]])[</span><span class="kc">None</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<pre>tensor([[[ 1., -1.],
[ 1., -1.]]])
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">out</span> <span class="o">=</span> <span class="n">ResizeBatch</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="p">(</span><span class="n">a</span><span class="p">))</span>
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<pre>tensor([[ 1., -1., 1., -1.]])
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<h3 id="Debugger" class="doc_header"><code>class</code> <code>Debugger</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L186" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Debugger-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>Debugger</code>() :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="Debugger-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Debugger-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>Debugger</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>A module to debug inside a model.</p>
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<p>The debugger module allows us to peek inside a network while its training and see in detail what is going on. We can see inputs, outputs and sizes at any point in the network.</p>
<p>For instance, if you run the following:</p>
<div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">Debugger</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
<span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">cuda</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">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">accuracy</span><span class="p">])</span>
<span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<p>... you'll see something like this:</p>
<pre><code>/home/ubuntu/fastai/fastai/layers.py(74)forward()
72 def forward(self,x:Tensor) -> Tensor:
73 set_trace()
---> 74 return x
75
76 class StdUpsample(nn.Module):
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<h3 id="PixelShuffle_ICNR" class="doc_header"><code>class</code> <code>PixelShuffle_ICNR</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L202" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#PixelShuffle_ICNR-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>PixelShuffle_ICNR</code>(<strong><code>ni</code></strong>:<code>int</code>, <strong><code>nf</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>scale</code></strong>:<code>int</code>=<strong><em><code>2</code></em></strong>, <strong><code>blur</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>norm_type</code></strong>=<strong><em><code><NormType.Weight: 3></code></em></strong>, <strong><code>leaky</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="PixelShuffle_ICNR-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#PixelShuffle_ICNR-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>PixelShuffle_ICNR</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Upsample by <code>scale</code> from <code>ni</code> filters to <code>nf</code> (default <code>ni</code>), using <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.PixelShuffle"><code>nn.PixelShuffle</code></a>, <a href="/layers.html#icnr"><code>icnr</code></a> init, and <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.weight_norm"><code>weight_norm</code></a>.</p>
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<h3 id="MergeLayer" class="doc_header"><code>class</code> <code>MergeLayer</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L147" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#MergeLayer-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>MergeLayer</code>(<strong><code>dense</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="MergeLayer-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#MergeLayer-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>MergeLayer</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Merge a shortcut with the result of the module by adding them or concatenating thme if <code>dense=True</code>.</p>
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<h3 id="PartialLayer" class="doc_header"><code>class</code> <code>PartialLayer</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L170" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#PartialLayer-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>PartialLayer</code>(<strong><code>func</code></strong>, <strong>**<code>kwargs</code></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="PartialLayer-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#PartialLayer-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>PartialLayer</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Layer that applies <code>partial(func, **kwargs)</code>.</p>
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<h3 id="SigmoidRange" class="doc_header"><code>class</code> <code>SigmoidRange</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L165" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#SigmoidRange-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>SigmoidRange</code>(<strong><code>low</code></strong>, <strong><code>high</code></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="SigmoidRange-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#SigmoidRange-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>SigmoidRange</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Sigmoid module with range <code>(low,x_max)</code></p>
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<h3 id="SequentialEx" class="doc_header"><code>class</code> <code>SequentialEx</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L128" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#SequentialEx-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>SequentialEx</code>(<strong>*<code>layers</code></strong>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="SequentialEx-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#SequentialEx-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>SequentialEx</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Like <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Sequential"><code>nn.Sequential</code></a>, but with ModuleList semantics, and can access module input</p>
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<h3 id="SelfAttention" class="doc_header"><code>class</code> <code>SelfAttention</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L81" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#SelfAttention-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>SelfAttention</code>(<strong><code>n_channels</code></strong>:<code>int</code>) :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="SelfAttention-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#SelfAttention-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>SelfAttention</code>:</p><ul><li><code>pytest -sv tests/test_torch_core.py::test_keep_parameter</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_torch_core.py#L269" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Self attention layer for nd.</p>
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<h3 id="BatchNorm1dFlat" class="doc_header"><code>class</code> <code>BatchNorm1dFlat</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L286" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#BatchNorm1dFlat-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>BatchNorm1dFlat</code>(<strong><code>num_features</code></strong>, <strong><code>eps</code></strong>=<strong><em><code>1e-05</code></em></strong>, <strong><code>momentum</code></strong>=<strong><em><code>0.1</code></em></strong>, <strong><code>affine</code></strong>=<strong><em><code>True</code></em></strong>, <strong><code>track_running_stats</code></strong>=<strong><em><code>True</code></em></strong>) :: <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.BatchNorm1d"><code>BatchNorm1d</code></a></p>
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<div class="collapse" id="BatchNorm1dFlat-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#BatchNorm1dFlat-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>BatchNorm1dFlat</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p><a href="https://pytorch.org/docs/stable/nn.html#torch.nn.BatchNorm1d"><code>nn.BatchNorm1d</code></a>, but first flattens leading dimensions</p>
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<h2 id="Loss-functions">Loss functions<a class="anchor-link" href="#Loss-functions">¶</a></h2>
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<h3 id="FlattenedLoss" class="doc_header"><code>class</code> <code>FlattenedLoss</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L220" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#FlattenedLoss-pytest" style="float:right; padding-right:10px">[test]</a></h3><blockquote><p><code>FlattenedLoss</code>(<strong><code>func</code></strong>, <strong>*<code>args</code></strong>, <strong><code>axis</code></strong>:<code>int</code>=<strong><em><code>-1</code></em></strong>, <strong><code>floatify</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>is_2d</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="FlattenedLoss-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#FlattenedLoss-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>FlattenedLoss</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Same as <code>func</code>, but flattens input and target.</p>
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<p>Create an instance of <code>func</code> with <code>args</code> and <code>kwargs</code>. When passing an output and target, it</p>
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<li>puts <code>axis</code> first in output and target with a transpose</li>
<li>casts the target to <code>float</code> is <code>floatify=True</code></li>
<li>squeezes the <code>output</code> to two dimensions if <code>is_2d</code>, otherwise one dimension, squeezes the target to one dimension</li>
<li>applied the instance of <code>func</code>.</li>
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<h4 id="BCEFlat" class="doc_header"><code>BCEFlat</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L247" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#BCEFlat-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>BCEFlat</code>(<strong>*<code>args</code></strong>, <strong><code>axis</code></strong>:<code>int</code>=<strong><em><code>-1</code></em></strong>, <strong><code>floatify</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="BCEFlat-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#BCEFlat-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>BCEFlat</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Same as <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.BCELoss"><code>nn.BCELoss</code></a>, but flattens input and target.</p>
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<h4 id="BCEWithLogitsFlat" class="doc_header"><code>BCEWithLogitsFlat</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L243" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#BCEWithLogitsFlat-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>BCEWithLogitsFlat</code>(<strong>*<code>args</code></strong>, <strong><code>axis</code></strong>:<code>int</code>=<strong><em><code>-1</code></em></strong>, <strong><code>floatify</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="BCEWithLogitsFlat-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#BCEWithLogitsFlat-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>BCEWithLogitsFlat</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Same as <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.BCEWithLogitsLoss"><code>nn.BCEWithLogitsLoss</code></a>, but flattens input and target.</p>
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<h4 id="CrossEntropyFlat" class="doc_header"><code>CrossEntropyFlat</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L239" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#CrossEntropyFlat-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>CrossEntropyFlat</code>(<strong>*<code>args</code></strong>, <strong><code>axis</code></strong>:<code>int</code>=<strong><em><code>-1</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="CrossEntropyFlat-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#CrossEntropyFlat-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>CrossEntropyFlat</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Same as <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.CrossEntropyLoss"><code>nn.CrossEntropyLoss</code></a>, but flattens input and target.</p>
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<h4 id="MSELossFlat" class="doc_header"><code>MSELossFlat</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L251" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#MSELossFlat-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>MSELossFlat</code>(<strong>*<code>args</code></strong>, <strong><code>axis</code></strong>:<code>int</code>=<strong><em><code>-1</code></em></strong>, <strong><code>floatify</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="MSELossFlat-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#MSELossFlat-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>MSELossFlat</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Same as <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.MSELoss"><code>nn.MSELoss</code></a>, but flattens input and target.</p>
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<h2 id="NoopLoss" class="doc_header"><code>class</code> <code>NoopLoss</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L255" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#NoopLoss-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>NoopLoss</code>() :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="NoopLoss-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#NoopLoss-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>NoopLoss</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>Just returns the mean of the <code>output</code>.</p>
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<h2 id="WassersteinLoss" class="doc_header"><code>class</code> <code>WassersteinLoss</code><a href="https://github.com/fastai/fastai/blob/master/fastai/layers.py#L259" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#WassersteinLoss-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>WassersteinLoss</code>() :: <a href="/core.html#PrePostInitMeta"><code>PrePostInitMeta</code></a> :: <a href="/torch_core.html#Module"><code>Module</code></a></p>
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<div class="collapse" id="WassersteinLoss-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#WassersteinLoss-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>No tests found for <code>WassersteinLoss</code>. To contribute a test please refer to <a href="/dev/test.html">this guide</a> and <a href="https://forums.fast.ai/t/improving-expanding-functional-tests/32929">this discussion</a>.</p></div></div><p>For WGAN.</p>