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+  <h1>Source code for deeprobust.graph.defense.adv_training</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.nn.modules.module</span> <span class="kn">import</span> <span class="n">Module</span>
+<span class="kn">from</span> <span class="nn">deeprobust.graph</span> <span class="kn">import</span> <span class="n">utils</span>
+<span class="kn">from</span> <span class="nn">deeprobust.graph.defense</span> <span class="kn">import</span> <span class="n">GCN</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">sp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="AdvTraining"><a class="viewcode-back" href="../../../../source/deeprobust.graph.defense.html#deeprobust.graph.defense.adv_training.AdvTraining">[docs]</a><span class="k">class</span> <span class="nc">AdvTraining</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Adversarial training framework for defending against attacks.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    model :</span>
+<span class="sd">        model to protect, e.g, GCN</span>
+<span class="sd">    adversary :</span>
+<span class="sd">        attack model</span>
+<span class="sd">    device : str</span>
+<span class="sd">        &#39;cpu&#39; or &#39;cuda&#39;</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">adversary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
+        <span class="k">if</span> <span class="n">adversary</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">adversary</span> <span class="o">=</span> <span class="n">RND</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">adversary</span> <span class="o">=</span> <span class="n">adversary</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
+
+<div class="viewcode-block" id="AdvTraining.adv_train"><a class="viewcode-back" href="../../../../source/deeprobust.graph.defense.html#deeprobust.graph.defense.adv_training.AdvTraining.adv_train">[docs]</a>    <span class="k">def</span> <span class="nf">adv_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">adj</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">idx_train</span><span class="p">,</span> <span class="n">train_iters</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;Start adversarial training.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        features :</span>
+<span class="sd">            node features</span>
+<span class="sd">        adj :</span>
+<span class="sd">            the adjacency matrix. The format could be torch.tensor or scipy matrix</span>
+<span class="sd">        labels :</span>
+<span class="sd">            node labels</span>
+<span class="sd">        idx_train :</span>
+<span class="sd">            node training indices</span>
+<span class="sd">        idx_val :</span>
+<span class="sd">            node validation indices. If not given (None), GCN training process will not adpot early stopping</span>
+<span class="sd">        train_iters : int</span>
+<span class="sd">            number of training epochs</span>
+<span class="sd">        &quot;&quot;&quot;</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="n">train_iters</span><span class="p">):</span>
+            <span class="n">modified_adj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">adversary</span><span class="o">.</span><span class="n">attack</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">adj</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">modified_adj</span><span class="p">,</span> <span class="n">train_iters</span><span class="p">,</span> <span class="n">initialize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div></div>
+
+
+
+
+</pre></div>
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