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<div class="section" id="pytorch-tutorial">
<h1>PyTorch Tutorial<a class="headerlink" href="#pytorch-tutorial" title="Permalink to this headline">¶</a></h1>
<div class="section" id="simple-usage-example">
<h2>Simple usage example<a class="headerlink" href="#simple-usage-example" title="Permalink to this headline">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">utils</span>
<span class="kn">import</span> <span class="nn">dataloader</span>
<span class="kn">from</span> <span class="nn">gnn_wrapper</span> <span class="kn">import</span> <span class="n">GNNWrapper</span><span class="p">,</span> <span class="n">SemiSupGNNWrapper</span>
<span class="c1"># define GNN configuration</span>
<span class="n">cfg</span> <span class="o">=</span> <span class="n">GNNWrapper</span><span class="o">.</span><span class="n">Config</span><span class="p">()</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">use_cuda</span> <span class="o">=</span> <span class="n">use_cuda</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">state_transition_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,]</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">output_function_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">]</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">state_dim</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">convergence_threshold</span> <span class="o">=</span> <span class="mf">0.01</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">graph_based</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">task_type</span> <span class="o">=</span> <span class="s2">"semisupervised"</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">lrw</span> <span class="o">=</span> <span class="mf">0.001</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">SemiSupGNNWrapper</span><span class="p">(</span><span class="n">cfg</span><span class="p">)</span>
<span class="c1"># Provide your own functions to generate input data</span>
<span class="n">E</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">mask_train</span><span class="p">,</span> <span class="n">mask_test</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">old_load_karate</span><span class="p">()</span>
<span class="n">dset</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">from_EN_to_GNN</span><span class="p">(</span><span class="n">E</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">aggregation_type</span><span class="o">=</span><span class="s2">"sum"</span><span class="p">,</span> <span class="n">sparse_matrix</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># generate the dataset</span>
<span class="n">model</span><span class="p">(</span><span class="n">dset</span><span class="p">)</span> <span class="c1"># dataset initalization into the GNN</span>
<span class="c1">#Training</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">train_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="simple-toy-example-for-input-formatting">
<span id="simple-pytorch"></span><h2>Simple toy example for input formatting<a class="headerlink" href="#simple-toy-example-for-input-formatting" title="Permalink to this headline">¶</a></h2>
<p>Input composed by two graphs:</p>
<blockquote>
<div><a class="reference internal image-reference" href="_images/g1.png"><img alt="_images/g1.png" src="_images/g1.png" style="width: 49%;" /></a>
<a class="reference internal image-reference" href="_images/g2.png"><img alt="_images/g2.png" src="_images/g2.png" style="width: 49%;" /></a>
</div></blockquote>
<p>This graphs can be described in the <a class="reference internal" href="#enmatrices-pytorch"><span class="std std-ref">EN Input</span></a> format.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">E</span></code> matrix describing the first graph ( <code class="docutils literal notranslate"><span class="pre">[[id_p,</span> <span class="pre">id_c,</span> <span class="pre">graph_id],...]</span></code>):</p>
<a class="reference internal image-reference" href="_images/g1.png"><img alt="_images/g1.png" class="align-right" src="_images/g1.png" style="width: 45%;" /></a>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">E</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span><span class="p">]</span>
<span class="go"> [0 2 0]</span>
<span class="go"> [0 4 0]</span>
<span class="go"> [1 0 0]</span>
<span class="go"> [1 2 0]</span>
<span class="go"> [1 3 0]</span>
<span class="go"> [2 0 0]</span>
<span class="go"> [2 1 0]</span>
<span class="go"> [2 3 0]</span>
<span class="go"> [2 4 0]</span>
<span class="go"> [3 1 0]</span>
<span class="go"> [3 2 0]</span>
<span class="go"> [4 0 0]</span>
<span class="go"> [4 2 0]]</span>
</pre></div>
</div>
<p>Note the last column, denoting the id (<code class="docutils literal notranslate"><span class="pre">0</span></code>) of the graph to which the arc belongs.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">E</span></code> matrix describing the second graph:</p>
<a class="reference internal image-reference" href="_images/g2.png"><img alt="_images/g2.png" class="align-right" src="_images/g2.png" style="width: 45%;" /></a>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">E</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span><span class="p">]</span>
<span class="go"> [0 3 1]</span>
<span class="go"> [1 2 1]</span>
<span class="go"> [1 3 1]</span>
<span class="go"> [2 3 1]</span>
<span class="go"> [2 0 1]</span>
<span class="go"> [3 0 1]</span>
<span class="go"> [2 1 1]</span>
<span class="go"> [3 1 1]</span>
<span class="go"> [3 2 1]]</span>
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
<div class="line"><br /></div>
</div>
<p>Note the last column, denoting the id (<code class="docutils literal notranslate"><span class="pre">1</span></code>) of the graph to which the arc belongs .</p>
<p>The global <code class="docutils literal notranslate"><span class="pre">E_tot</span></code> matrix (with incremental node ids):</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">E_tot</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">0</span><span class="p">]</span>
<span class="go"> [0 2 0]</span>
<span class="go"> [0 4 0]</span>
<span class="go"> [1 0 0]</span>
<span class="go"> [1 2 0]</span>
<span class="go"> [1 3 0]</span>
<span class="go"> [2 0 0]</span>
<span class="go"> [2 1 0]</span>
<span class="go"> [2 3 0]</span>
<span class="go"> [2 4 0]</span>
<span class="go"> [3 1 0]</span>
<span class="go"> [3 2 0]</span>
<span class="go"> [4 0 0]</span>
<span class="go"> [4 2 0]</span>
<span class="go"> [5 7 1]</span>
<span class="go"> [5 8 1]</span>
<span class="go"> [6 7 1]</span>
<span class="go"> [6 8 1]</span>
<span class="go"> [7 5 1]</span>
<span class="go"> [7 6 1]</span>
<span class="go"> [7 8 1]</span>
<span class="go"> [8 5 1]</span>
<span class="go"> [8 6 1]</span>
<span class="go"> [8 7 1]]</span>
</pre></div>
</div>
<p>The global <code class="docutils literal notranslate"><span class="pre">N</span></code> matrix ( in this simple case, each node has a different one-hot feature) (<code class="docutils literal notranslate"><span class="pre">[[node_features,</span> <span class="pre">graph_id],...</span> <span class="pre">]</span></code>) :</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">N</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="go"> [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]</span>
<span class="go"> [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]</span>
<span class="go"> [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]</span>
<span class="go"> [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]</span>
<span class="go"> [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
<span class="go"> [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]</span>
<span class="go"> [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]</span>
<span class="go"> [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">from_EN_to_GNN()</span></code> util takes care of formatting the inputs:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># random labels</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">N_tot</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="c1"># set input and output dim, the maximum number of iterations, the number of epochs and the optimizer</span>
<span class="n">cfg</span> <span class="o">=</span> <span class="n">GNNWrapper</span><span class="o">.</span><span class="n">Config</span><span class="p">()</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">use_cuda</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">prepare_device</span><span class="p">(</span><span class="n">n_gpu_use</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">gpu_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">tensorboard</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">state_transition_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,]</span> <span class="c1"># hidden dims of the state transition function</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">output_function_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="c1"># hidden dims of the output function</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">state_dim</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">convergence_threshold</span> <span class="o">=</span> <span class="mf">0.01</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">graph_based</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">log_interval</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">task_type</span> <span class="o">=</span> <span class="s2">"multiclass"</span>
<span class="n">cfg</span><span class="o">.</span><span class="n">lrw</span> <span class="o">=</span> <span class="mf">0.001</span>
<span class="c1"># model creation</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">GNNWrapper</span><span class="p">(</span><span class="n">cfg</span><span class="p">)</span>
<span class="c1"># dataset creation</span>
<span class="n">dset</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">from_EN_to_GNN</span><span class="p">(</span><span class="n">E</span><span class="p">,</span> <span class="n">N_tot</span><span class="p">,</span> <span class="n">targets</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">aggregation_type</span><span class="o">=</span><span class="s2">"sum"</span><span class="p">,</span> <span class="n">sparse_matrix</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># generate the dataset</span>
<span class="n">model</span><span class="p">(</span><span class="n">dset</span><span class="p">)</span> <span class="c1"># dataset initalization into the GNN</span>
<span class="c1">#Training</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">cfg</span><span class="o">.</span><span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">train_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
<span class="k">if</span> <span class="n">epoch</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">test_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="description">
<h2>Description<a class="headerlink" href="#description" title="Permalink to this headline">¶</a></h2>
<p>This guide is an introduction to the PyTorch GNN package.</p>
<p>The implementation consists of several modules:</p>
<ul class="simple">
<li><p><strong>pygnn.py</strong> contains the main core of the GNN</p></li>
<li><p><strong>gnn_wrapper.py</strong> a wrapper (for supervised and semisupervised tasks) handling the GNN</p></li>
<li><p><strong>net.py</strong> contains the implementation of several <em>state</em> and <em>output</em> networks</p></li>
<li><p><strong>dataloader.py</strong> contains the data input handling and utils - EN input format utilities, DGL examples</p></li>
</ul>
</div>
<div class="section" id="model-definition">
<h2>Model definition<a class="headerlink" href="#model-definition" title="Permalink to this headline">¶</a></h2>
<div class="section" id="input-data">
<span id="inp-data"></span><h3>Input data<a class="headerlink" href="#input-data" title="Permalink to this headline">¶</a></h3>
<p>As described in <a class="reference internal" href="GNN/software.html#matrix-based"><span class="std std-ref">Matrix-based implementation</span></a>, the computations are based on the arcs in the input graphs.
Hence, inputs to the model must be specified as an ordered edge list.</p>
<p>In particular, for each edge, this structure (<code class="docutils literal notranslate"><span class="pre">inp</span></code>) must contain:</p>
<ul class="simple">
<li><p>the <strong>id</strong> of the child node (used to gather its state)</p></li>
<li><p>the father and child <strong>node labels</strong></p></li>
<li><p>the <strong>edge label</strong> (if available)</p></li>
</ul>
<blockquote>
<div><a class="reference internal image-reference" href="_images/input.svg"><img alt="_images/input.svg" class="align-center" src="_images/input.svg" width="70%" /></a>
</div></blockquote>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>We provide a novel utility to compose this kind of input, given a description of the graph dataset in an E-N format. See section <a class="reference internal" href="#enmatrices-pytorch"><span class="std std-ref">EN Input</span></a>.</p>
</div>
<div class="section" id="arcnode">
<h4>ArcNode<a class="headerlink" href="#arcnode" title="Permalink to this headline">¶</a></h4>
<p>In order to aggregate the state per node, a matrix multiplication with an edge–node matrix is performed. The matrix encodes which arcs affect a certain node (see <a class="reference internal" href="GNN/software.html#matrix-based"><span class="std std-ref">Matrix-based implementation</span></a>).
This matrix (<code class="docutils literal notranslate"><span class="pre">arcnode</span></code>) is sparse, to save memory.</p>
<blockquote>
<div><a class="reference internal image-reference" href="_images/arcnode.svg"><img alt="_images/arcnode.svg" class="align-center" src="_images/arcnode.svg" width="50%" /></a>
</div></blockquote>
</div>
<div class="section" id="en-input">
<span id="enmatrices-pytorch"></span><h4>EN Input<a class="headerlink" href="#en-input" title="Permalink to this headline">¶</a></h4>
<p>To simplify the input creation, we provide an utility in the <code class="docutils literal notranslate"><span class="pre">utils.py</span></code> file, the <code class="docutils literal notranslate"><span class="pre">from_EN_to_GNN(E,</span> <span class="pre">N)</span></code> function.</p>
<p>The user can describe the dataset in the EN format:</p>
<blockquote>
<div><dl class="field-list simple">
<dt class="field-odd">E</dt>
<dd class="field-odd"><ul class="simple">
<li><p>numpy array of edges : [[id_p, id_c, graph_id],…]. One row for each arc in the dataset. First column must contain the ids of father nodes, the second column ids of child nodes. The third column contains an id that identifies the graph (to which the node belongs) in the dataset.</p></li>
</ul>
</dd>
<dt class="field-even">N</dt>
<dd class="field-even"><ul class="simple">
<li><p>numpy array of nodes features - [[node_features, graph_id],… ]. One row for each and every node. A set of columns containing the nodes features. The last column is an id that identifies the graph (to which the node belongs) in the dataset.</p></li>
</ul>
</dd>
</dl>
</div></blockquote>
<p>The <code class="docutils literal notranslate"><span class="pre">from_EN_to_GNN</span></code> util takes this two array as input (<code class="docutils literal notranslate"><span class="pre">E</span></code> <code class="docutils literal notranslate"><span class="pre">N</span></code> ) and returns the formatted input for the GNN model, <code class="docutils literal notranslate"><span class="pre">inp</span></code>, <code class="docutils literal notranslate"><span class="pre">arcnode</span></code>, <code class="docutils literal notranslate"><span class="pre">graphnode</span></code>.</p>
<p>See <a class="reference internal" href="#simple-pytorch"><span class="std std-ref">Simple toy example for input formatting</span></a> for a practical example.</p>
</div>
</div>
</div>
<div class="section" id="state-and-output-function-definition">
<h2>State and output function definition<a class="headerlink" href="#state-and-output-function-definition" title="Permalink to this headline">¶</a></h2>
<p>It is possible to define the structure of these functions using the configurator:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">state_transition_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">output_function_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
</pre></div>
</div>
<p>The state dimension, convergence threshold and maximum number of iterations are defined via:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">state_dim</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">max_iterations</span> <span class="o">=</span> <span class="mi">50</span>
<span class="gp">>>> </span><span class="n">cfg</span><span class="o">.</span><span class="n">convergence_threshold</span> <span class="o">=</span> <span class="mf">0.01</span>
</pre></div>
</div>
<p>Afterwards, the GNN model can be defined using:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">GNNWrapper</span><span class="p">(</span><span class="n">cfg</span><span class="p">)</span>
</pre></div>
</div>
<p>In this way, the model building is complete.</p>
<p>The dataset is generated and given as input to the GNN via:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dset</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">from_EN_to_GNN</span><span class="p">(</span><span class="n">E</span><span class="p">,</span> <span class="n">N_tot</span><span class="p">,</span> <span class="n">targets</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">aggregation_type</span><span class="o">=</span><span class="s2">"sum"</span><span class="p">,</span> <span class="n">sparse_matrix</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># generate the dataset</span>
<span class="gp">>>> </span><span class="n">model</span><span class="p">(</span><span class="n">dset</span><span class="p">)</span> <span class="c1"># dataset initalization into the GNN</span>
</pre></div>
</div>
<p>The GNN model handles the dataset. To have more control on the data, check the script <code class="docutils literal notranslate"><span class="pre">gnn_wrapper.py</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In case of a <em>graph-based</em> task, you can specify it trough the config:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span> <span class="n">cfg</span><span class="o">.</span><span class="n">graph_based</span> <span class="o">=</span> <span class="kc">True</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="training">
<h2>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">Train</span></code> method runs one epoch of the training procedure:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span> <span class="n">model</span><span class="o">.</span><span class="n">train_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
</pre></div>
</div>
<p>It returns the loss value <code class="docutils literal notranslate"><span class="pre">loss</span></code> and the number of iteration of the current step convergence procedure <code class="docutils literal notranslate"><span class="pre">it</span></code>.</p>
</div>
<div class="section" id="validation">
<h2>Validation<a class="headerlink" href="#validation" title="Permalink to this headline">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">valid_step</span></code> method performs the validation (i.e., using the validation set already available in the data) of the model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">valid_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="test">
<h2>Test<a class="headerlink" href="#test" title="Permalink to this headline">¶</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">test_step</span></code> method performs the test phase (i.e., using the test set already available in the data) of the model:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">test_step</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
</pre></div>
</div>
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