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<h1><a href="https://nn.labml.ai/gan/cycle_gan/index.html">Cycle GAN</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of the paper
<a href="https://arxiv.org/abs/1703.10593">Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks</a>.</p>
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<h1><a href="https://nn.labml.ai/gan/dcgan/index.html">Deep Convolutional Generative Adversarial Networks - DCGAN</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of paper
<a href="https://arxiv.org/abs/1511.06434">Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks</a>.</p>
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<title>None</title> <title>Generative Adversarial Networks</title>
<link rel="shortcut icon" href="/icon.png"/> <link rel="shortcut icon" href="/icon.png"/>
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@ -66,6 +66,23 @@
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<div class='section' id='section-0'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-0'>#</a>
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<h1>Generative Adversarial Networks</h1>
<ul>
<li><a href="original/index.html">Original GAN</a></li>
<li><a href="dcgan/index.html">GAN with deep convolutional network</a></li>
<li><a href="cycle_gan/index.html">Cycle GAN</a></li>
<li><a href="wasserstein/index.html">Wasserstein GAN</a></li>
</ul>
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<h1><a href="https://nn.labml.ai/gan/original/index.html">Generative Adversarial Networks - GAN</a></h1>
<p>This is an annotated implementation of
<a href="https://arxiv.org/abs/1406.2661">Generative Adversarial Networks</a>.</p>
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@ -72,7 +72,8 @@
<div class='section-link'> <div class='section-link'>
<a href='#section-0'>#</a> <a href='#section-0'>#</a>
</div> </div>
<p>This is an implementation of <h1>Wasserstein GAN (WGAN)</h1>
<p>This is an implementation of
<a href="https://arxiv.org/abs/1701.07875">Wasserstein GAN</a>.</p> <a href="https://arxiv.org/abs/1701.07875">Wasserstein GAN</a>.</p>
<p>The original GAN loss is based on Jensen-Shannon (JS) divergence <p>The original GAN loss is based on Jensen-Shannon (JS) divergence
between the real distribution $\mathbb{P}_r$ and generated distribution $\mathbb{P}_g$. between the real distribution $\mathbb{P}_r$ and generated distribution $\mathbb{P}_g$.
@ -140,10 +141,10 @@ network that defines $f$ clipped within a range.</em></p>
<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/gan/wasserstein/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p> <p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/gan/wasserstein/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">85</span><span></span><span class="kn">import</span> <span class="nn">torch.utils.data</span> <div class="highlight"><pre><span class="lineno">87</span><span></span><span class="kn">import</span> <span class="nn">torch.utils.data</span>
<span class="lineno">86</span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span> <span class="lineno">88</span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="lineno">87</span> <span class="lineno">89</span>
<span class="lineno">88</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span></pre></div> <span class="lineno">90</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-1'> <div class='section' id='section-1'>
@ -160,7 +161,7 @@ so we minimize,
</p> </p>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">91</span><span class="k">class</span> <span class="nc">DiscriminatorLoss</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div> <div class="highlight"><pre><span class="lineno">93</span><span class="k">class</span> <span class="nc">DiscriminatorLoss</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-2'> <div class='section' id='section-2'>
@ -174,7 +175,7 @@ so we minimize,
</ul> </ul>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">102</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f_real</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div> <div class="highlight"><pre><span class="lineno">104</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f_real</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-3'> <div class='section' id='section-3'>
@ -185,7 +186,7 @@ so we minimize,
<p>We use ReLUs to clip the loss to keep $f \in [-1, +1]$ range.</p> <p>We use ReLUs to clip the loss to keep $f \in [-1, +1]$ range.</p>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">109</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">f_real</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">f_fake</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></pre></div> <div class="highlight"><pre><span class="lineno">111</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">f_real</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">f_fake</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-4'> <div class='section' id='section-4'>
@ -202,7 +203,7 @@ so we minimize,
</p> </p>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">112</span><span class="k">class</span> <span class="nc">GeneratorLoss</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div> <div class="highlight"><pre><span class="lineno">114</span><span class="k">class</span> <span class="nc">GeneratorLoss</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-5'> <div class='section' id='section-5'>
@ -215,7 +216,7 @@ so we minimize,
</ul> </ul>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">124</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div> <div class="highlight"><pre><span class="lineno">126</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
</div> </div>
</div> </div>
<div class='section' id='section-6'> <div class='section' id='section-6'>
@ -226,7 +227,7 @@ so we minimize,
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">128</span> <span class="k">return</span> <span class="o">-</span><span class="n">f_fake</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></pre></div> <div class="highlight"><pre><span class="lineno">130</span> <span class="k">return</span> <span class="o">-</span><span class="n">f_fake</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></pre></div>
</div> </div>
</div> </div>
</div> </div>

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<h1><a href="https://nn.labml.ai/gan/wasserstein/index.html">Wasserstein GAN - WGAN</a></h1>
<p>This is an implementation of
<a href="https://arxiv.org/abs/1701.07875">Wasserstein GAN</a>.</p>
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@ -102,9 +102,10 @@ implementations.</p>
<h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4> <h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
<h4>✨ <a href="gan/index.html">Generative Adversarial Networks</a></h4> <h4>✨ <a href="gan/index.html">Generative Adversarial Networks</a></h4>
<ul> <ul>
<li><a href="gan/simple_mnist_experiment.html">GAN with a multi-layer perceptron</a></li> <li><a href="gan/original/index.html">Original GAN</a></li>
<li><a href="gan/dcgan.html">GAN with deep convolutional network</a></li> <li><a href="gan/dcgan/index.html">GAN with deep convolutional network</a></li>
<li><a href="gan/cycle_gan.html">Cycle GAN</a></li> <li><a href="gan/cycle_gan/index.html">Cycle GAN</a></li>
<li><a href="gan/wasserstein/index.html">Wasserstein GAN</a></li>
</ul> </ul>
<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4> <h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
<h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4> <h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4>

4
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@ -15,14 +15,14 @@
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7
labml_nn/__init__.py
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@ -36,9 +36,10 @@ implementations.
#### ✨ [Capsule Networks](capsule_networks/index.html) #### ✨ [Capsule Networks](capsule_networks/index.html)
#### ✨ [Generative Adversarial Networks](gan/index.html) #### ✨ [Generative Adversarial Networks](gan/index.html)
* [GAN with a multi-layer perceptron](gan/simple_mnist_experiment.html) * [Original GAN](gan/original/index.html)
* [GAN with deep convolutional network](gan/dcgan.html) * [GAN with deep convolutional network](gan/dcgan/index.html)
* [Cycle GAN](gan/cycle_gan.html) * [Cycle GAN](gan/cycle_gan/index.html)
* [Wasserstein GAN](gan/wasserstein/index.html)
#### ✨ [Sketch RNN](sketch_rnn/index.html) #### ✨ [Sketch RNN](sketch_rnn/index.html)

14
labml_nn/gan/__init__.py
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@ -0,0 +1,14 @@
"""
---
title: Generative Adversarial Networks
summary: >
A set of PyTorch implementations/tutorials of GANs.
---
# Generative Adversarial Networks
* [Original GAN](original/index.html)
* [GAN with deep convolutional network](dcgan/index.html)
* [Cycle GAN](cycle_gan/index.html)
* [Wasserstein GAN](wasserstein/index.html)
"""

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@ -0,0 +1,4 @@
# [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593).

4
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@ -0,0 +1,4 @@
# [Deep Convolutional Generative Adversarial Networks - DCGAN](https://nn.labml.ai/gan/dcgan/index.html)
This is a [PyTorch](https://pytorch.org) implementation of paper
[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434).

4
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@ -0,0 +1,4 @@
# [Generative Adversarial Networks - GAN](https://nn.labml.ai/gan/original/index.html)
This is an annotated implementation of
[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).

2
labml_nn/gan/wasserstein/__init__.py
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@ -4,6 +4,8 @@ title: Wasserstein GAN (WGAN)
summary: A simple PyTorch implementation/tutorial of Wasserstein Generative Adversarial Networks (WGAN) loss functions. summary: A simple PyTorch implementation/tutorial of Wasserstein Generative Adversarial Networks (WGAN) loss functions.
--- ---
# Wasserstein GAN (WGAN)
This is an implementation of This is an implementation of
[Wasserstein GAN](https://arxiv.org/abs/1701.07875). [Wasserstein GAN](https://arxiv.org/abs/1701.07875).

4
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@ -0,0 +1,4 @@
# [Wasserstein GAN - WGAN](https://nn.labml.ai/gan/wasserstein/index.html)
This is an implementation of
[Wasserstein GAN](https://arxiv.org/abs/1701.07875).

7
readme.md
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@ -42,9 +42,10 @@ implementations almost weekly.
#### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html) #### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
#### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html) #### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
* [GAN with a multi-layer perceptron](https://nn.labml.ai/gan/simple_mnist_experiment.html) * [Original GAN](https://nn.labml.ai/gan/original/index.html)
* [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan.html) * [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan.html) * [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html) #### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)