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<h1>Generative Adversarial Networks experiment with MNIST</h1>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">10</span><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
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<span class="lineno">11</span>
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<span class="lineno">12</span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">13</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
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<span class="lineno">14</span><span class="kn">import</span> <span class="nn">torch.utils.data</span>
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<span class="lineno">15</span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>
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<span class="lineno">16</span>
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<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">tracker</span><span class="p">,</span> <span class="n">monit</span><span class="p">,</span> <span class="n">experiment</span>
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<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span><span class="p">,</span> <span class="n">calculate</span>
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<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml_helpers.datasets.mnist</span> <span class="kn">import</span> <span class="n">MNISTConfigs</span>
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<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_helpers.device</span> <span class="kn">import</span> <span class="n">DeviceConfigs</span>
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<span class="lineno">21</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
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<span class="lineno">22</span><span class="kn">from</span> <span class="nn">labml_helpers.optimizer</span> <span class="kn">import</span> <span class="n">OptimizerConfigs</span>
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<span class="lineno">23</span><span class="kn">from</span> <span class="nn">labml_helpers.train_valid</span> <span class="kn">import</span> <span class="n">TrainValidConfigs</span><span class="p">,</span> <span class="n">hook_model_outputs</span><span class="p">,</span> <span class="n">BatchIndex</span>
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<span class="lineno">24</span><span class="kn">from</span> <span class="nn">labml_nn.gan.original</span> <span class="kn">import</span> <span class="n">DiscriminatorLogitsLoss</span><span class="p">,</span> <span class="n">GeneratorLogitsLoss</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-1'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-1'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">27</span><span class="k">def</span> <span class="nf">weights_init</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
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<span class="lineno">28</span> <span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
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<span class="lineno">29</span> <span class="k">if</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'Linear'</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
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<span class="lineno">30</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
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<span class="lineno">31</span> <span class="k">elif</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'BatchNorm'</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
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<span class="lineno">32</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
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<span class="lineno">33</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-2'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-2'>#</a>
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</div>
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<h3>Simple MLP Generator</h3>
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<p>This has three linear layers of increasing size with <code>LeakyReLU</code> activations.
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The final layer has a $tanh$ activation.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">36</span><span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-3'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-3'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">44</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">45</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
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<span class="lineno">46</span> <span class="n">layer_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">]</span>
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<span class="lineno">47</span> <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="lineno">48</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="mi">100</span>
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<span class="lineno">49</span> <span class="k">for</span> <span class="n">size</span> <span class="ow">in</span> <span class="n">layer_sizes</span><span class="p">:</span>
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<span class="lineno">50</span> <span class="n">layers</span> <span class="o">=</span> <span class="n">layers</span> <span class="o">+</span> <span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)]</span>
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<span class="lineno">51</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="n">size</span>
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<span class="lineno">52</span>
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<span class="lineno">53</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</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="o">*</span><span class="n">layers</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">())</span>
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<span class="lineno">54</span>
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<span class="lineno">55</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-4'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-4'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">57</span> <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>
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<span class="lineno">58</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</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">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-5'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-5'>#</a>
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</div>
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<h3>Simple MLP Discriminator</h3>
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<p>This has three linear layers of decreasing size with <code>LeakyReLU</code> activations.
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The final layer has a single output that gives the logit of whether input
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is real or fake. You can get the probability by calculating the sigmoid of it.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">61</span><span class="k">class</span> <span class="nc">Discriminator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-6'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-6'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">70</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">71</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
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<span class="lineno">72</span> <span class="n">layer_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">]</span>
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<span class="lineno">73</span> <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="lineno">74</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span>
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<span class="lineno">75</span> <span class="k">for</span> <span class="n">size</span> <span class="ow">in</span> <span class="n">layer_sizes</span><span class="p">:</span>
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<span class="lineno">76</span> <span class="n">layers</span> <span class="o">=</span> <span class="n">layers</span> <span class="o">+</span> <span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)]</span>
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<span class="lineno">77</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="n">size</span>
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<span class="lineno">78</span>
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<span class="lineno">79</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</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="o">*</span><span class="n">layers</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
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<span class="lineno">80</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-7'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-7'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">82</span> <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>
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<span class="lineno">83</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</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">shape</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></pre></div>
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</div>
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</div>
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<div class='section' id='section-8'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-8'>#</a>
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</div>
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<h2>Configurations</h2>
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<p>This extends MNIST configurations to get the data loaders and Training and validation loop
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configurations to simplify our implementation.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">86</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">MNISTConfigs</span><span class="p">,</span> <span class="n">TrainValidConfigs</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-9'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-9'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">94</span> <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">DeviceConfigs</span><span class="p">()</span>
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<span class="lineno">95</span> <span class="n">dataset_transforms</span> <span class="o">=</span> <span class="s1">'mnist_gan_transforms'</span>
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<span class="lineno">96</span> <span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span>
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<span class="lineno">97</span>
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<span class="lineno">98</span> <span class="n">is_save_models</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="lineno">99</span> <span class="n">discriminator</span><span class="p">:</span> <span class="n">Module</span> <span class="o">=</span> <span class="s1">'mlp'</span>
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<span class="lineno">100</span> <span class="n">generator</span><span class="p">:</span> <span class="n">Module</span> <span class="o">=</span> <span class="s1">'mlp'</span>
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<span class="lineno">101</span> <span class="n">generator_optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span>
|
|
<span class="lineno">102</span> <span class="n">discriminator_optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span>
|
|
<span class="lineno">103</span> <span class="n">generator_loss</span><span class="p">:</span> <span class="n">GeneratorLogitsLoss</span> <span class="o">=</span> <span class="s1">'original'</span>
|
|
<span class="lineno">104</span> <span class="n">discriminator_loss</span><span class="p">:</span> <span class="n">DiscriminatorLogitsLoss</span> <span class="o">=</span> <span class="s1">'original'</span>
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<span class="lineno">105</span> <span class="n">label_smoothing</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span>
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<span class="lineno">106</span> <span class="n">discriminator_k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-10'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-10'>#</a>
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</div>
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<p>Initializations</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">108</span> <span class="k">def</span> <span class="nf">init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-11'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-11'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">112</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_modules</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="lineno">113</span>
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<span class="lineno">114</span> <span class="n">hook_model_outputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span> <span class="s1">'generator'</span><span class="p">)</span>
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<span class="lineno">115</span> <span class="n">hook_model_outputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">,</span> <span class="s1">'discriminator'</span><span class="p">)</span>
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<span class="lineno">116</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_scalar</span><span class="p">(</span><span class="s2">"loss.generator.*"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
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<span class="lineno">117</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_scalar</span><span class="p">(</span><span class="s2">"loss.discriminator.*"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
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|
<span class="lineno">118</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="s2">"generated"</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-12'>
|
|
<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-12'>#</a>
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</div>
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<p>
|
|
<script type="math/tex; mode=display">z \sim p(z)</script>
|
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</p>
|
|
</div>
|
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">120</span> <span class="k">def</span> <span class="nf">sample_z</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-13'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-13'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">124</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
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</div>
|
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</div>
|
|
<div class='section' id='section-14'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-14'>#</a>
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</div>
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<p>Take a training step</p>
|
|
</div>
|
|
<div class='code'>
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|
<div class="highlight"><pre><span class="lineno">126</span> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">:</span> <span class="n">BatchIndex</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-15'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-15'>#</a>
|
|
</div>
|
|
<p>Set model states</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">132</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">)</span>
|
|
<span class="lineno">133</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-16'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-16'>#</a>
|
|
</div>
|
|
<p>Get MNIST images</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">136</span> <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-17'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-17'>#</a>
|
|
</div>
|
|
<p>Increment step in training mode</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">139</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
|
|
<span class="lineno">140</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add_global_step</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-18'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-18'>#</a>
|
|
</div>
|
|
<p>Train the discriminator</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">143</span> <span class="k">with</span> <span class="n">monit</span><span class="o">.</span><span class="n">section</span><span class="p">(</span><span class="s2">"discriminator"</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-19'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-19'>#</a>
|
|
</div>
|
|
<p>Get discriminator loss</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">145</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_discriminator_loss</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-20'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-20'>#</a>
|
|
</div>
|
|
<p>Train</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">148</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
|
|
<span class="lineno">149</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
|
|
<span class="lineno">150</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
|
|
<span class="lineno">151</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_last</span><span class="p">:</span>
|
|
<span class="lineno">152</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">'discriminator'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">)</span>
|
|
<span class="lineno">153</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-21'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-21'>#</a>
|
|
</div>
|
|
<p>Train the generator once in every <code>discriminator_k</code></p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">156</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_interval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">discriminator_k</span><span class="p">):</span>
|
|
<span class="lineno">157</span> <span class="k">with</span> <span class="n">monit</span><span class="o">.</span><span class="n">section</span><span class="p">(</span><span class="s2">"generator"</span><span class="p">):</span>
|
|
<span class="lineno">158</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_generator_loss</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-22'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-22'>#</a>
|
|
</div>
|
|
<p>Train</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">161</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
|
|
<span class="lineno">162</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
|
|
<span class="lineno">163</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
|
|
<span class="lineno">164</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_last</span><span class="p">:</span>
|
|
<span class="lineno">165</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">'generator'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">)</span>
|
|
<span class="lineno">166</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
|
|
<span class="lineno">167</span>
|
|
<span class="lineno">168</span> <span class="n">tracker</span><span class="o">.</span><span class="n">save</span><span class="p">()</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-23'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-23'>#</a>
|
|
</div>
|
|
<p>Calculate discriminator loss</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">170</span> <span class="k">def</span> <span class="nf">calc_discriminator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-24'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-24'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">174</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">data</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="lineno">175</span> <span class="n">logits_true</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
|
<span class="lineno">176</span> <span class="n">logits_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
|
|
<span class="lineno">177</span> <span class="n">loss_true</span><span class="p">,</span> <span class="n">loss_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">(</span><span class="n">logits_true</span><span class="p">,</span> <span class="n">logits_false</span><span class="p">)</span>
|
|
<span class="lineno">178</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-25'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-25'>#</a>
|
|
</div>
|
|
<p>Log stuff</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">181</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"loss.discriminator.true."</span><span class="p">,</span> <span class="n">loss_true</span><span class="p">)</span>
|
|
<span class="lineno">182</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"loss.discriminator.false."</span><span class="p">,</span> <span class="n">loss_false</span><span class="p">)</span>
|
|
<span class="lineno">183</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"loss.discriminator."</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
|
|
<span class="lineno">184</span>
|
|
<span class="lineno">185</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-26'>
|
|
<div class='docs doc-strings'>
|
|
<div class='section-link'>
|
|
<a href='#section-26'>#</a>
|
|
</div>
|
|
<p>Calculate generator loss</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">187</span> <span class="k">def</span> <span class="nf">calc_generator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-27'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-27'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">191</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
|
|
<span class="lineno">192</span> <span class="n">generated_images</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span>
|
|
<span class="lineno">193</span> <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">generated_images</span><span class="p">)</span>
|
|
<span class="lineno">194</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-28'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
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<a href='#section-28'>#</a>
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</div>
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<p>Log stuff</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">197</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">'generated'</span><span class="p">,</span> <span class="n">generated_images</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">6</span><span class="p">])</span>
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<span class="lineno">198</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">"loss.generator."</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
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<span class="lineno">199</span>
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<span class="lineno">200</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
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</div>
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</div>
|
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<div class='section' id='section-29'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-29'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">205</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">dataset_transforms</span><span class="p">)</span>
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<span class="lineno">206</span><span class="k">def</span> <span class="nf">mnist_gan_transforms</span><span class="p">():</span>
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<span class="lineno">207</span> <span class="k">return</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
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<span class="lineno">208</span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
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<span class="lineno">209</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))</span>
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<span class="lineno">210</span> <span class="p">])</span>
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<span class="lineno">211</span>
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<span class="lineno">212</span>
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<span class="lineno">213</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="p">)</span>
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<span class="lineno">214</span><span class="k">def</span> <span class="nf">_discriminator_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span>
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<span class="lineno">215</span> <span class="n">opt_conf</span> <span class="o">=</span> <span class="n">OptimizerConfigs</span><span class="p">()</span>
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<span class="lineno">216</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="s1">'Adam'</span>
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|
<span class="lineno">217</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">discriminator</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span>
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<span class="lineno">218</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">2.5e-4</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-30'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-30'>#</a>
|
|
</div>
|
|
<p>Setting exponent decay rate for first moment of gradient,
|
|
$\beta_1$ to <code>0.5</code> is important.
|
|
Default of <code>0.9</code> fails.</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">222</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">betas</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">)</span>
|
|
<span class="lineno">223</span> <span class="k">return</span> <span class="n">opt_conf</span></pre></div>
|
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</div>
|
|
</div>
|
|
<div class='section' id='section-31'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-31'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
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|
<div class="highlight"><pre><span class="lineno">226</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="p">)</span>
|
|
<span class="lineno">227</span><span class="k">def</span> <span class="nf">_generator_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span>
|
|
<span class="lineno">228</span> <span class="n">opt_conf</span> <span class="o">=</span> <span class="n">OptimizerConfigs</span><span class="p">()</span>
|
|
<span class="lineno">229</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="s1">'Adam'</span>
|
|
<span class="lineno">230</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">generator</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span>
|
|
<span class="lineno">231</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">2.5e-4</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-32'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-32'>#</a>
|
|
</div>
|
|
<p>Setting exponent decay rate for first moment of gradient,
|
|
$\beta_1$ to <code>0.5</code> is important.
|
|
Default of <code>0.9</code> fails.</p>
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">235</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">betas</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">)</span>
|
|
<span class="lineno">236</span> <span class="k">return</span> <span class="n">opt_conf</span>
|
|
<span class="lineno">237</span>
|
|
<span class="lineno">238</span>
|
|
<span class="lineno">239</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span> <span class="s1">'mlp'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Generator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
|
|
<span class="lineno">240</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator</span><span class="p">,</span> <span class="s1">'mlp'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Discriminator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
|
|
<span class="lineno">241</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">,</span> <span class="s1">'original'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">GeneratorLogitsLoss</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">label_smoothing</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
|
|
<span class="lineno">242</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">,</span> <span class="s1">'original'</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">DiscriminatorLogitsLoss</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">label_smoothing</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span></pre></div>
|
|
</div>
|
|
</div>
|
|
<div class='section' id='section-33'>
|
|
<div class='docs'>
|
|
<div class='section-link'>
|
|
<a href='#section-33'>#</a>
|
|
</div>
|
|
|
|
</div>
|
|
<div class='code'>
|
|
<div class="highlight"><pre><span class="lineno">245</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
|
|
<span class="lineno">246</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span>
|
|
<span class="lineno">247</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'mnist_gan'</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'test'</span><span class="p">)</span>
|
|
<span class="lineno">248</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
|
|
<span class="lineno">249</span> <span class="p">{</span><span class="s1">'label_smoothing'</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">})</span>
|
|
<span class="lineno">250</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
|
|
<span class="lineno">251</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
|
|
<span class="lineno">252</span>
|
|
<span class="lineno">253</span>
|
|
<span class="lineno">254</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
|
<span class="lineno">255</span> <span class="n">main</span><span class="p">()</span></pre></div>
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</div>
|
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</div>
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</div>
|
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</div>
|
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<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.4/MathJax.js?config=TeX-AMS_HTML">
|
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</script>
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<!-- MathJax configuration -->
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<script type="text/x-mathjax-config">
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MathJax.Hub.Config({
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tex2jax: {
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// Center justify equations in code and markdown cells. Elsewhere
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// we use CSS to left justify single line equations in code cells.
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displayAlign: 'center',
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"HTML-CSS": { fonts: ["TeX"] }
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<script>
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modal.appendChild(modalContent)
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var modalImage = document.createElement('img')
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modalContent.appendChild(modalImage)
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var span = document.createElement('span')
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span.classList.add('close')
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span.textContent = 'x'
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modal.appendChild(span)
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img.onclick = function () {
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document.body.appendChild(modal)
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modalImage.src = img.src
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span.onclick = function () {
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