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  <div class="section" id="cnn-rnn">

<h1>CNN+RNN<a class="headerlink" href="#cnn-rnn" title="Permalink to this headline"></a></h1>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DeepPurpose</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">CNN_RNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">)</span>

</pre></div>

</div>

<p>CNN_RNN means a GRU/LSTM on top of a CNN on <a class="reference external" href="https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system">SMILES</a>.</p>

<p><strong>constructor</strong> create CNN_RNN</p>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoding</span><span class="p">,</span> <span class="o">**</span><span class="n">config</span><span class="p">)</span>

</pre></div>

</div>

<ul class="simple">

<li><p><strong>encoding</strong> (string, “drug” or “protein”) - specify input type, “drug” or “protein”.</p></li>

<li><dl class="simple">

<dt><strong>config</strong> (kwargs, keyword arguments) - specify the parameter of transformer. The keys include</dt><dd><ul>

<li><p>cnn_drug_filters (list, each element is int) - specify the size of filter when encoding drug, e.g., cnn_drug_filters = [32,64,96].</p></li>

<li><p>cnn_drug_kernels (list, each element is int) - specify the size of kernel when encoding drug, e.g., cnn_drug_kernels = [4,6,8].</p></li>

<li><p>rnn_drug_hid_dim (int) - specify the hidden dimension of RNN when encoding drug, e.g., rnn_drug_hid_dim = 64.</p></li>

<li><p>rnn_drug_n_layers (int) - specify number of layer in RNN when encoding drug, .e.g, rnn_drug_n_layers = 2.</p></li>

<li><p>rnn_drug_bidirectional (bool) - specify if RNN is bidirectional when encoding drug, .e.g, rnn_drug_bidirectional = True.</p></li>

<li><p>hidden_dim_drug (int) - specify the hidden dimension when encoding drug, e.g., hidden_dim_drug = 256.</p></li>

<li><p>cnn_target_filters (list, each element is int) - specify the size of filter when encoding protein, e.g, cnn_target_filters = [32,64,96].</p></li>

<li><p>cnn_target_kernels (list, each element is int) - specify the size of kernel when encoding protein, e.g, cnn_target_kernels = [4,8,12].</p></li>

<li><p>hidden_dim_protein (int) - specify the hidden dimension when encoding protein, e.g., hidden_dim_protein = 256.</p></li>

<li><p>rnn_target_hid_dim (int) - specify hidden dimension of RNN when encoding protein, e.g., rnn_target_hid_dim = 64.</p></li>

<li><p>rnn_target_n_layers (int) - specify the number of layer in RNN when encoding protein, e.g., rnn_target_n_layers = 2.</p></li>

<li><p>rnn_target_bidirectional (bool) - specify if RNN is bidirectional when encoding protein, e.g., rnn_target_bidirectional = True</p></li>

</ul>

</dd>

</dl>

</li>

</ul>

<p><strong>Calling functions</strong> implement the feedforward procedure of CNN_RNN.</p>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>

</pre></div>

</div>

<ul class="simple">

<li><p><strong>v</strong> (torch.Tensor) - input feature of CNN_RNN.</p></li>

</ul>

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