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<!-- navigation toc: --> <li><a href="._week43-bs001.html#___sec0" style="font-size: 80%;"><b>Plans for week 43</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs003.html#___sec2" style="font-size: 80%;"><b>Summary on Deep Learning Methods</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs005.html#___sec4" style="font-size: 80%;"><b>Recurrent neural networks: Overarching view</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs010.html#___sec9" style="font-size: 80%;"><b>Predicting New Points With A Trained Recurrent Neural Network</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs025.html#___sec24" style="font-size: 80%;"><b>Getting Results</b></a></li>
<!-- navigation toc: --> <li><a href="._week43-bs026.html#___sec25" style="font-size: 80%;"><b>Interpolating Between MNIST Digits</b></a></li>
<!-- navigation toc: --> <li><a href="._week43-bs027.html#___sec26" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
<!-- navigation toc: --> <li><a href="._week43-bs028.html#___sec27" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
<!-- navigation toc: --> <li><a href="._week43-bs029.html#___sec28" style="font-size: 80%;"><b>More on the covariance</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs031.html#___sec30" style="font-size: 80%;"><b>Simple Example</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs041.html#___sec40" style="font-size: 80%;"><b>The Algorithm before the Theorem</b></a></li>
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<!-- navigation toc: --> <li><a href="._week43-bs048.html#___sec47" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
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<h2 id="___sec20" class="anchor">Other Models </h2>
Let us take a look at our models. <b>Note</b>: double click images for bigger view.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>generator <span style="color: #666666">=</span> generator_model()
plot_model(generator, show_shapes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, rankdir<span style="color: #666666">=</span><span style="color: #BA2121">&#39;LR&#39;</span>)
</pre></div>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>discriminator <span style="color: #666666">=</span> discriminator_model()
plot_model(discriminator, show_shapes<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>, rankdir<span style="color: #666666">=</span><span style="color: #BA2121">&#39;LR&#39;</span>)
</pre></div>
<p>
Next we need a few helper objects we will use in training
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>cross_entropy <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>keras<span style="color: #666666">.</span>losses<span style="color: #666666">.</span>BinaryCrossentropy(from_logits<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>)
generator_optimizer <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>keras<span style="color: #666666">.</span>optimizers<span style="color: #666666">.</span>Adam(<span style="color: #666666">1e-4</span>)
discriminator_optimizer <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>keras<span style="color: #666666">.</span>optimizers<span style="color: #666666">.</span>Adam(<span style="color: #666666">1e-4</span>)
</pre></div>
<p>
The first object, <em>cross_entropy</em> is our loss function and the two others are
our optimizers. Notice we use the same learning rate for both \( g \) and \( d \). This
is because they need to improve their accuracy at approximately equal speeds to
get convergence (not necessarily exactly equal). Now we define our loss
functions
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">generator_loss</span>(fake_output):
loss <span style="color: #666666">=</span> cross_entropy(tf<span style="color: #666666">.</span>ones_like(fake_output), fake_output)
<span style="color: #008000; font-weight: bold">return</span> loss
</pre></div>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">discriminator_loss</span>(real_output, fake_output):
real_loss <span style="color: #666666">=</span> cross_entropy(tf<span style="color: #666666">.</span>ones_like(real_output), real_output)
fake_loss <span style="color: #666666">=</span> cross_entropy(tf<span style="color: #666666">.</span>zeros_liks(fake_output), fake_output)
total_loss <span style="color: #666666">=</span> real_loss <span style="color: #666666">+</span> fake_loss
<span style="color: #008000; font-weight: bold">return</span> total_loss
</pre></div>
<p>
Next we define a kind of seed to help us compare the learning process over
multiple training epochs.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>noise_dimension <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_examples_to_generate <span style="color: #666666">=</span> <span style="color: #666666">16</span>
seed_images <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>random<span style="color: #666666">.</span>normal([n_examples_to_generate, noise_dimension])
</pre></div>
<p>
<p>
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