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<h1 id="chain">Chain<a class="headerlink" href="#chain" title="Permanent link">&para;</a></h1>
<p>When we say models are implemented in a declarative way in Refiners, what this means in practice is they are implemented as Chains. <a class="autorefs autorefs-internal" href="../../reference/fluxion/layers/#refiners.fluxion.layers.Chain"><code>Chain</code></a> is a Python class to implement trees of modules. It is a subclass of Refiners' <a class="autorefs autorefs-internal" href="../../reference/fluxion/layers/#refiners.fluxion.layers.Module"><code>Module</code></a>, which is in turn a subclass of PyTorch's <code>Module</code>. All inner nodes of a Chain are subclasses of <code>Chain</code>, and leaf nodes are subclasses of Refiners' <code>Module</code>.</p>
<h2 id="a-first-example">A first example<a class="headerlink" href="#a-first-example" title="Permanent link">&para;</a></h2>
<p>To give you an idea of how it looks, let us take a simple convolution network to classify MNIST as an example. First, let us define a few variables.</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">img_res</span> <span class="o">=</span> <span class="mi">28</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">channels</span> <span class="o">=</span> <span class="mi">128</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">3</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">hidden_layer_in</span> <span class="o">=</span> <span class="p">(((</span><span class="n">img_res</span> <span class="o">-</span> <span class="n">kernel_size</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">channels</span>
</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="n">hidden_layer_out</span> <span class="o">=</span> <span class="mi">200</span>
</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="n">output_size</span> <span class="o">=</span> <span class="mi">10</span>
</span></code></pre></div>
<p>Now, here is the model in PyTorch:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="k">class</span> <span class="nc">BasicModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
</span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">)</span>
</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a> <span class="bp">self</span><span class="o">.</span><span class="n">linear_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_layer_in</span><span class="p">,</span> <span class="n">hidden_layer_out</span><span class="p">)</span>
</span><span id="__span-1-6"><a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a> <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
</span><span id="__span-1-7"><a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a> <span class="bp">self</span><span class="o">.</span><span class="n">linear_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_layer_out</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
</span><span id="__span-1-8"><a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a>
</span><span id="__span-1-9"><a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
</span><span id="__span-1-10"><a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-11"><a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-12"><a id="__codelineno-1-12" name="__codelineno-1-12" href="#__codelineno-1-12"></a> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-13"><a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</span><span id="__span-1-14"><a id="__codelineno-1-14" name="__codelineno-1-14" href="#__codelineno-1-14"></a> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-15"><a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a> <span class="n">x</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-16"><a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span><span id="__span-1-17"><a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a> <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span></code></pre></div>
<p>And here is how we could implement the same model in Refiners:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="k">class</span> <span class="nc">BasicModel</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
</span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">),</span>
</span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a> <span class="n">fl</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
</span><span id="__span-2-6"><a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a> <span class="n">fl</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span>
</span><span id="__span-2-7"><a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
</span><span id="__span-2-8"><a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_layer_in</span><span class="p">,</span> <span class="n">hidden_layer_out</span><span class="p">),</span>
</span><span id="__span-2-9"><a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a> <span class="n">fl</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
</span><span id="__span-2-10"><a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_layer_out</span><span class="p">,</span> <span class="n">output_size</span><span class="p">),</span>
</span><span id="__span-2-11"><a id="__codelineno-2-11" name="__codelineno-2-11" href="#__codelineno-2-11"></a> <span class="n">fl</span><span class="o">.</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">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)),</span>
</span><span id="__span-2-12"><a id="__codelineno-2-12" name="__codelineno-2-12" href="#__codelineno-2-12"></a> <span class="p">)</span>
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We often use the namespace <code>fl</code> which means <code>fluxion</code>, which is the name of the part of Refiners that implements basic layers.</p>
</div>
<p>As of writing, Refiners does not include a <code>Softmax</code> layer by default, but as you can see you can easily call arbitrary code using <a class="autorefs autorefs-internal" href="../../reference/fluxion/layers/#refiners.fluxion.layers.Lambda"><code>fl.Lambda</code></a>. Alternatively, if you just wanted to write <code>Softmax()</code>, you could implement it like this:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="k">class</span> <span class="nc">Softmax</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
</span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Notice the type hints here. All of Refiners' codebase is typed, which makes it a pleasure to use if your downstream code is typed too.</p>
</div>
<h2 id="inspecting-and-manipulating">Inspecting and manipulating<a class="headerlink" href="#inspecting-and-manipulating" title="Permanent link">&para;</a></h2>
<p>Let us instantiate the <code>BasicModel</code> we just defined and inspect its representation in a Python REPL:</p>
<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>&gt;&gt;&gt; m = BasicModel()
</span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>&gt;&gt;&gt; m
</span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a>(CHAIN) BasicModel()
</span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a> ├── Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=float32)
</span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a> ├── ReLU() #1
</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a> ├── MaxPool2d(kernel_size=2, stride=2)
</span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a> ├── Flatten(start_dim=1)
</span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a> ├── Linear(in_features=21632, out_features=200, device=cpu, dtype=float32) #1
</span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a> ├── ReLU() #2
</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a> ├── Linear(in_features=200, out_features=10, device=cpu, dtype=float32) #2
</span><span id="__span-4-11"><a id="__codelineno-4-11" name="__codelineno-4-11" href="#__codelineno-4-11"></a> └── Softmax()
</span></code></pre></div>
<p>The children of a <code>Chain</code> are stored in a dictionary and can be accessed by name or index. When layers of the same type appear in the Chain, distinct suffixed keys are automatically generated.</p>
<div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>&gt;&gt;&gt; m[0]
</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=float32)
</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>&gt;&gt;&gt; m.Conv2d
</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=float32)
</span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a>&gt;&gt;&gt; m[6]
</span><span id="__span-5-6"><a id="__codelineno-5-6" name="__codelineno-5-6" href="#__codelineno-5-6"></a>Linear(in_features=200, out_features=10, device=cpu, dtype=float32)
</span><span id="__span-5-7"><a id="__codelineno-5-7" name="__codelineno-5-7" href="#__codelineno-5-7"></a>&gt;&gt;&gt; m.Linear_2
</span><span id="__span-5-8"><a id="__codelineno-5-8" name="__codelineno-5-8" href="#__codelineno-5-8"></a>Linear(in_features=200, out_features=10, device=cpu, dtype=float32)
</span></code></pre></div>
<p>The Chain class includes several helpers to manipulate the tree. For instance, imagine I want to organize my model by wrapping each layer of the convnet in a subchain. Here is how I could do it:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="k">class</span> <span class="nc">ConvLayer</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a> <span class="k">pass</span>
</span><span id="__span-6-3"><a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a>
</span><span id="__span-6-4"><a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a><span class="k">class</span> <span class="nc">HiddenLayer</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
</span><span id="__span-6-5"><a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a> <span class="k">pass</span>
</span><span id="__span-6-6"><a id="__codelineno-6-6" name="__codelineno-6-6" href="#__codelineno-6-6"></a>
</span><span id="__span-6-7"><a id="__codelineno-6-7" name="__codelineno-6-7" href="#__codelineno-6-7"></a><span class="k">class</span> <span class="nc">OutputLayer</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
</span><span id="__span-6-8"><a id="__codelineno-6-8" name="__codelineno-6-8" href="#__codelineno-6-8"></a> <span class="k">pass</span>
</span><span id="__span-6-9"><a id="__codelineno-6-9" name="__codelineno-6-9" href="#__codelineno-6-9"></a>
</span><span id="__span-6-10"><a id="__codelineno-6-10" name="__codelineno-6-10" href="#__codelineno-6-10"></a><span class="n">m</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ConvLayer</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span>
</span><span id="__span-6-11"><a id="__codelineno-6-11" name="__codelineno-6-11" href="#__codelineno-6-11"></a><span class="n">m</span><span class="o">.</span><span class="n">insert_after_type</span><span class="p">(</span><span class="n">ConvLayer</span><span class="p">,</span> <span class="n">HiddenLayer</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
</span><span id="__span-6-12"><a id="__codelineno-6-12" name="__codelineno-6-12" href="#__codelineno-6-12"></a><span class="n">m</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">OutputLayer</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">m</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span>
</span></code></pre></div>
<p>Did it work? Let's see:</p>
<div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>&gt;&gt;&gt; m
</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>(CHAIN) BasicModel()
</span><span id="__span-7-3"><a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a> ├── (CHAIN) ConvLayer()
</span><span id="__span-7-4"><a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a> │ ├── Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=float32)
</span><span id="__span-7-5"><a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a> │ ├── ReLU()
</span><span id="__span-7-6"><a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a> │ └── MaxPool2d(kernel_size=2, stride=2)
</span><span id="__span-7-7"><a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a> ├── (CHAIN) HiddenLayer()
</span><span id="__span-7-8"><a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a> │ ├── Flatten(start_dim=1)
</span><span id="__span-7-9"><a id="__codelineno-7-9" name="__codelineno-7-9" href="#__codelineno-7-9"></a> │ ├── Linear(in_features=21632, out_features=200, device=cpu, dtype=float32)
</span><span id="__span-7-10"><a id="__codelineno-7-10" name="__codelineno-7-10" href="#__codelineno-7-10"></a> │ └── ReLU()
</span><span id="__span-7-11"><a id="__codelineno-7-11" name="__codelineno-7-11" href="#__codelineno-7-11"></a> └── (CHAIN) OutputLayer()
</span><span id="__span-7-12"><a id="__codelineno-7-12" name="__codelineno-7-12" href="#__codelineno-7-12"></a> ├── Linear(in_features=200, out_features=10, device=cpu, dtype=float32)
</span><span id="__span-7-13"><a id="__codelineno-7-13" name="__codelineno-7-13" href="#__codelineno-7-13"></a> └── Softmax()
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Organizing models like this is actually a good idea, it makes them easier to understand and adapt.</p>
</div>
<h2 id="accessing-and-iterating">Accessing and iterating<a class="headerlink" href="#accessing-and-iterating" title="Permanent link">&para;</a></h2>
<p>There are also many ways to access or iterate nodes even if they are deep in the tree. Most of them are implemented using a powerful iterator named <a class="autorefs autorefs-internal" href="../../reference/fluxion/layers/#refiners.fluxion.layers.Chain.walk"><code>walk</code></a>. However, most of the time, you can use simpler helpers. For instance, to iterate all the modules in the tree that hold weights (the <code>Conv2d</code> and the <code>Linear</code>s), we can just do:</p>
<div class="language-py highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a><span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">WeightedModule</span><span class="p">):</span>
</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a> <span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</span></code></pre></div>
<p>It prints:</p>
<div class="language-text highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a>Conv2d(in_channels=1, out_channels=128, kernel_size=(3, 3), device=cpu, dtype=float32)
</span><span id="__span-9-2"><a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a>Linear(in_features=21632, out_features=200, device=cpu, dtype=float32)
</span><span id="__span-9-3"><a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a>Linear(in_features=200, out_features=10, device=cpu, dtype=float32)
</span></code></pre></div>
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