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Repository: shayneobrien/generative-models
Branch: master
Commit: 74fbe414f81e
Files: 34
Total size: 17.3 MB

Directory structure:
gitextract_n6pjnmtg/

├── .gitignore
├── LICENSE
├── README.md
├── notebooks/
│   ├── 01-nonsaturating-gan.ipynb
│   ├── 02-minimax-gan.ipynb
│   ├── 03-wasserstein-gan.ipynb
│   ├── 04-wasserstein-gan-gradient-penalty.ipynb
│   ├── 05-least-squares-gan.ipynb
│   ├── 06-deep-regret-analytic-gan.ipynb
│   ├── 07-boundary-equilibrium-gan.ipynb
│   ├── 08-standard-autoencoder.ipynb
│   ├── 09-variational-autoencoder.ipynb
│   ├── 10-relativistic-nonsaturing-gan.ipynb
│   ├── 11-information-gan.ipynb
│   ├── 12-f-divergence-gan.ipynb
│   ├── 13-fisher-gan.ipynb
│   └── 14-bounded-information-rate-variational-autoencoder.ipynb
├── requirements.txt
└── src/
    ├── ae.py
    ├── bayes_gan.py
    ├── be_gan.py
    ├── bir_vae.py
    ├── dra_gan.py
    ├── f_gan.py
    ├── fisher_gan.py
    ├── info_gan.py
    ├── ls_gan.py
    ├── mm_gan.py
    ├── ns_gan.py
    ├── ra_gan.py
    ├── utils.py
    ├── vae.py
    ├── w_gan.py
    └── w_gp_gan.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
env
data
__pycache__
.ipynb_checkpoints
.DS_store


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2018 Shayne O'Brien

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Overview
PyTorch 0.4.1 | Python 3.6.5

Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein gradient penalty, least squares, deep regret analytic, bounded equilibrium, relativistic, f-divergence, Fisher, and information generative adversarial networks (GANs), and standard, variational, and bounded information rate variational autoencoders (VAEs).

Paper links are supplied at the beginning of each file with a short summary of the paper. See src folder for files to run via terminal, or notebooks folder for Jupyter notebook visualizations via your local browser. The main file changes can be see in the [```train```](https://github.com/shayneobrien/generative-models/blob/master/src/ns_gan.py#L94-L170), [```train_D```](https://github.com/shayneobrien/generative-models/blob/master/src/ns_gan.py#L172-L194), and [```train_G```](https://github.com/shayneobrien/generative-models/blob/master/src/ns_gan.py#L196-L216) of the Trainer class, although changes are not completely limited to only these two areas (e.g. Wasserstein GAN clamps weight in the train function, BEGAN gives multiple outputs from train_D, fGAN has a slight modification in viz_loss function to indicate method used in title).

All code in this repository operates in a generative, unsupervised manner on binary (black and white) MNIST. The architectures are compatible with a variety of datatypes (1D, 2D, square 3D images). Plotting functions work with binary/RGB images. If a GPU is detected, the models use it. Otherwise, they default to CPU. VAE Trainer classes contain methods to visualize latent space representations (see [```make_all```](https://github.com/shayneobrien/generative-models/blob/master/src/vae.py#L333-L343) function).

# Usage
To initialize an environment:
```
python -m venv env  
. env/bin/activate  
pip install -r requirements.txt  
```

For playing around in Jupyer notebooks:
```
jupyter notebook
```

To run from Terminal:
```
cd src
python bir_vae.py
```

# New Models

One of the primary purposes of this repository is to make implementing deep generative model (i.e., GAN/VAE) variants as easy as possible. This is possible because, typically but not always (e.g. BIRVAE), the proposed modifications only apply to the way loss is computed for backpropagation. Thus, the core training class is structured in such a way that most new implementations should only require edits to the ```train_D``` and ```train_G``` functions of GAN Trainer classes, and the ```compute_batch``` function of VAE Trainer classes.

Suppose we have a non-saturating GAN and we wanted to implement a least-squares GAN. To do this, all we have to do is change two lines:

[Original](https://github.com/shayneobrien/generative-models/blob/master/src/ns_gan.py#L166-L208) (NSGAN)
```
def train_D(self, images):
  ...
  D_loss = -torch.mean(torch.log(DX_score + 1e-8) + torch.log(1 - DG_score + 1e-8))

  return D_loss
```
```
def train_G(self, images):
  ...
  G_loss = -torch.mean(torch.log(DG_score + 1e-8))

  return G_loss
```

[New](https://github.com/shayneobrien/generative-models/blob/master/src/ls_gan.py#L166-L209) (LSGAN)
```
def train_D(self, images):
  ...
  D_loss = (0.50 * torch.mean((DX_score - 1.)**2)) + (0.50 * torch.mean((DG_score - 0.)**2))

  return D_loss
```
```
def train_G(self, images):
  ...
  G_loss = 0.50 * torch.mean((DG_score - 1.)**2)

  return G_loss
```

# Model Architecture
The architecture chosen in these implementations for both the generator (G) and discriminator (D) consists of a simple, two-layer feedforward network. While this will give sensible output for MNIST, in practice it is recommended to use deep convolutional architectures (i.e. DCGANs) to get nicer outputs. This can be done by editing the Generator and Discriminator classes for GANs, or the Encoder and Decoder classes for VAEs.

# Visualization
All models were trained for 25 epochs with hidden dimension 400, latent dimension 20. Other implementation specifics are as close to the respective original paper (linked) as possible.

*Model* | *Epoch 1* | *Epoch 25* | *Progress* | *Loss*
:---: | :---: | :---: | :---: | :---: |
[MMGAN](https://arxiv.org/abs/1406.2661) | <img src = 'viz/MMGAN/reconst_1.png' height = '150px'> | <img src = 'viz/MMGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/MMGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/MMGAN_loss.png' height = '150px'>
[NSGAN](https://arxiv.org/abs/1406.2661) | <img src = 'viz/NSGAN/reconst_1.png' height = '150px'> | <img src = 'viz/NSGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/NSGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/NSGAN_loss.png' height = '150px'>
[WGAN](https://arxiv.org/abs/1701.07875) | <img src = 'viz/WGAN/reconst_1.png' height = '150px'> | <img src = 'viz/WGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/WGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/WGAN_loss.png' height = '150px'>
[WGPGAN](https://arxiv.org/abs/1704.00028) | <img src = 'viz/WGPGAN/reconst_1.png' height = '150px'> | <img src = 'viz/WGPGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/WGPGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/WGPGAN_loss.png' height = '150px'>
[DRAGAN](https://arxiv.org/abs/1705.07215) | <img src = 'viz/DRAGAN/reconst_1.png' height = '150px'> | <img src = 'viz/DRAGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/DRAGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/DRAGAN_loss.png' height = '150px'>
[BEGAN](https://arxiv.org/abs/1703.10717) | <img src = 'viz/BEGAN/reconst_1.png' height = '150px'> | <img src = 'viz/BEGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/BEGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/BEGAN_loss.png' height = '150px'>
[LSGAN](https://arxiv.org/abs/1611.04076) | <img src = 'viz/LSGAN/reconst_1.png' height = '150px'> | <img src = 'viz/LSGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/LSGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/LSGAN_loss.png' height = '150px'>
[RaNSGAN](https://arxiv.org/abs/1807.00734) | <img src = 'viz/RaNSGAN/reconst_1.png' height = '150px'> | <img src = 'viz/RaNSGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/RaNSGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/RaNSGAN_loss.png' height = '150px'>
[FisherGAN](https://arxiv.org/abs/1606.07536) | <img src = 'viz/FisherGAN/reconst_1.png' height = '150px'> | <img src = 'viz/FisherGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/FisherGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/FisherGAN_loss.png' height = '150px'>
[InfoGAN](https://arxiv.org/abs/1606.03657) | <img src = 'viz/InfoGAN/reconst_1.png' height = '150px'> | <img src = 'viz/InfoGAN/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/InfoGAN_gif.gif' height = '150px'> | <img src = 'viz/losses/InfoGAN_loss.png' height = '150px'>
[f-TVGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/total_variation/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/total_variation/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_total_variation_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_total_variation_loss.png' height = '150px'>
[f-PearsonGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/pearson/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/pearson/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_pearson_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_pearson_loss.png' height = '150px'>
[f-JSGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/jensen_shannon/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/jensen_shannon/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_jensen_shannon_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_jensen_shannon_loss.png' height = '150px'>
[f-ForwGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/forward_kl/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/forward_kl/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_forward_kl_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_forward_kl_loss.png' height = '150px'>
[f-RevGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/reverse_kl/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/reverse_kl/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_reverse_kl_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_reverse_kl_loss.png' height = '150px'>
[f-HellingerGAN](https://arxiv.org/abs/1606.00709) | <img src = 'viz/fGAN/hellinger/reconst_1.png' height = '150px'> | <img src = 'viz/fGAN/hellinger/reconst_25.png' height = '150px'> | <img src = 'viz/gifs/fGAN_hellinger_gif.gif' height = '150px'> | <img src = 'viz/losses/fGAN_hellinger_loss.png' height = '150px'>
[VAE](https://arxiv.org/abs/1312.6114) | <img src = 'viz/VAE/sample_1.png' height = '150px'> | <img src = 'viz/VAE/sample_25.png' height = '150px'> | <img src = 'viz/gifs/VAE_gif.gif' height = '150px'> | <img src = 'viz/losses/VAE_loss.png' height = '150px'>
[BIRVAE](https://arxiv.org/abs/1807.07306) | <img src = 'viz/BIRVAE/sample_1.png' height = '150px'> | <img src = 'viz/BIRVAE/sample_25.png' height = '150px'> | <img src = 'viz/gifs/BIRVAE_gif.gif' height = '150px'> | <img src = 'viz/losses/BIRVAE_loss.png' height = '150px'>

# To Do
Models: CVAE, denoising VAE, adversarial autoencoder | Bayesian GAN, Self-attention GAN, Primal-Dual Wasserstein GAN  
Architectures: Add DCGAN option  
Datasets: Beyond MNIST


================================================
FILE: notebooks/01-nonsaturating-gan.ipynb
================================================
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     "text": [
      "\r",
      "  0%|          | 0/25 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/25], G Loss: 2.6815, D Loss: 0.3556\n"
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\n",
      "text/plain": [
       "<Figure size 360x360 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  4%|▍         | 1/25 [00:11<04:24, 11.01s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[2/25], G Loss: 3.4534, D Loss: 0.1147\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  8%|▊         | 2/25 [00:21<04:07, 10.77s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[3/25], G Loss: 4.2915, D Loss: 0.1249\n"
     ]
    },
    {
     "data": {
      "image/png": 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
Download .txt
gitextract_n6pjnmtg/

├── .gitignore
├── LICENSE
├── README.md
├── notebooks/
│   ├── 01-nonsaturating-gan.ipynb
│   ├── 02-minimax-gan.ipynb
│   ├── 03-wasserstein-gan.ipynb
│   ├── 04-wasserstein-gan-gradient-penalty.ipynb
│   ├── 05-least-squares-gan.ipynb
│   ├── 06-deep-regret-analytic-gan.ipynb
│   ├── 07-boundary-equilibrium-gan.ipynb
│   ├── 08-standard-autoencoder.ipynb
│   ├── 09-variational-autoencoder.ipynb
│   ├── 10-relativistic-nonsaturing-gan.ipynb
│   ├── 11-information-gan.ipynb
│   ├── 12-f-divergence-gan.ipynb
│   ├── 13-fisher-gan.ipynb
│   └── 14-bounded-information-rate-variational-autoencoder.ipynb
├── requirements.txt
└── src/
    ├── ae.py
    ├── bayes_gan.py
    ├── be_gan.py
    ├── bir_vae.py
    ├── dra_gan.py
    ├── f_gan.py
    ├── fisher_gan.py
    ├── info_gan.py
    ├── ls_gan.py
    ├── mm_gan.py
    ├── ns_gan.py
    ├── ra_gan.py
    ├── utils.py
    ├── vae.py
    ├── w_gan.py
    └── w_gp_gan.py
Download .txt
SYMBOL INDEX (288 symbols across 15 files)

FILE: src/ae.py
  class Encoder (line 29) | class Encoder(nn.Module):
    method __init__ (line 33) | def __init__(self, image_size, hidden_dim):
    method forward (line 38) | def forward(self, x):
  class Decoder (line 42) | class Decoder(nn.Module):
    method __init__ (line 46) | def __init__(self, hidden_dim, image_size):
    method forward (line 51) | def forward(self, encoder_output):
  class Autoencoder (line 55) | class Autoencoder(nn.Module):
    method __init__ (line 58) | def __init__(self, image_size=784, hidden_dim=32):
    method forward (line 66) | def forward(self, x):
  class AutoencoderTrainer (line 70) | class AutoencoderTrainer:
    method __init__ (line 71) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 87) | def train(self, num_epochs, lr=1e-3, weight_decay=1e-5):
    method compute_batch (line 147) | def compute_batch(self, batch):
    method evaluate (line 162) | def evaluate(self, iterator):
    method reconstruct_images (line 166) | def reconstruct_images(self, images, epoch, save=True):
    method viz_loss (line 195) | def viz_loss(self):
    method save_model (line 211) | def save_model(self, savepath):
    method load_model (line 215) | def load_model(self, loadpath):

FILE: src/be_gan.py
  class Generator (line 48) | class Generator(nn.Module):
    method __init__ (line 51) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 57) | def forward(self, x):
  class Discriminator (line 63) | class Discriminator(nn.Module):
    method __init__ (line 67) | def __init__(self, image_size, hidden_dim):
    method forward (line 73) | def forward(self, x):
  class BEGAN (line 79) | class BEGAN(nn.Module):
    method __init__ (line 82) | def __init__(self, image_size, hidden_dim, z_dim):
  class BEGANTrainer (line 93) | class BEGANTrainer:
    method __init__ (line 94) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 109) | def train(self, num_epochs, G_lr=1e-4, D_lr=1e-4, D_steps=1,
    method train_D (line 212) | def train_D(self, images, K):
    method train_G (line 240) | def train_G(self, images):
    method compute_noise (line 260) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 264) | def process_batch(self, iterator):
    method generate_images (line 270) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 307) | def viz_loss(self):
    method save_model (line 329) | def save_model(self, savepath):
    method load_model (line 333) | def load_model(self, loadpath):

FILE: src/bir_vae.py
  class Encoder (line 37) | class Encoder(nn.Module):
    method __init__ (line 41) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 47) | def forward(self, x):
  class Decoder (line 53) | class Decoder(nn.Module):
    method __init__ (line 57) | def __init__(self, z_dim, hidden_dim, image_size):
    method forward (line 63) | def forward(self, z):
  class BIRVAE (line 69) | class BIRVAE(nn.Module):
    method __init__ (line 73) | def __init__(self, image_size=784, hidden_dim=400, z_dim=20, I=13.3):
    method forward (line 84) | def forward(self, x):
    method reparameterize (line 90) | def reparameterize(self, mu):
  class BIRVAETrainer (line 99) | class BIRVAETrainer:
    method __init__ (line 100) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 117) | def train(self, num_epochs, lr=1e-3, weight_decay=1e-5):
    method compute_batch (line 180) | def compute_batch(self, batch, LAMBDA=1000.):
    method maximum_mean_discrepancy (line 201) | def maximum_mean_discrepancy(self, z):
    method compute_kernel (line 210) | def compute_kernel(self, x, y):
    method evaluate (line 223) | def evaluate(self, iterator):
    method reconstruct_images (line 234) | def reconstruct_images(self, images, epoch, save=True):
    method sample_images (line 265) | def sample_images(self, epoch=-100, num_images=36, save=True):
    method sample_interpolated_images (line 290) | def sample_interpolated_images(self):
    method explore_latent_space (line 307) | def explore_latent_space(self, num_epochs=3):
    method make_all (line 348) | def make_all(self):
    method viz_loss (line 360) | def viz_loss(self):
    method save_model (line 381) | def save_model(self, savepath):
    method load_model (line 385) | def load_model(self, loadpath):

FILE: src/dra_gan.py
  class Generator (line 32) | class Generator(nn.Module):
    method __init__ (line 35) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 41) | def forward(self, x):
  class Discriminator (line 47) | class Discriminator(nn.Module):
    method __init__ (line 51) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 57) | def forward(self, x):
  class DRAGAN (line 63) | class DRAGAN(nn.Module):
    method __init__ (line 66) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class DRAGANTrainer (line 77) | class DRAGANTrainer:
    method __init__ (line 80) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 94) | def train(self, num_epochs, G_lr=1e-4, D_lr=1e-4, D_steps=5):
    method train_D (line 174) | def train_D(self, images, LAMBDA=10, K=1, C=1):
    method train_G (line 227) | def train_G(self, images):
    method compute_noise (line 247) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 251) | def process_batch(self, iterator):
    method generate_images (line 257) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 293) | def viz_loss(self):
    method save_model (line 314) | def save_model(self, savepath):
    method load_model (line 318) | def load_model(self, loadpath):

FILE: src/f_gan.py
  class Generator (line 42) | class Generator(nn.Module):
    method __init__ (line 45) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 50) | def forward(self, x):
  class Discriminator (line 56) | class Discriminator(nn.Module):
    method __init__ (line 60) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 65) | def forward(self, x):
  class fGAN (line 71) | class fGAN(nn.Module):
    method __init__ (line 74) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class Divergence (line 85) | class Divergence:
    method __init__ (line 89) | def __init__(self, method):
    method D_loss (line 99) | def D_loss(self, DX_score, DG_score):
    method G_loss (line 123) | def G_loss(self, DG_score):
  class fGANTrainer (line 145) | class fGANTrainer:
    method __init__ (line 148) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 162) | def train(self, num_epochs, method, G_lr=1e-4, D_lr=1e-4, D_steps=1):
    method train_D (line 244) | def train_D(self, images):
    method train_G (line 267) | def train_G(self, images):
    method compute_noise (line 286) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 290) | def process_batch(self, iterator):
    method generate_images (line 296) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 332) | def viz_loss(self):
    method save_model (line 353) | def save_model(self, savepath):
    method load_model (line 357) | def load_model(self, loadpath):

FILE: src/fisher_gan.py
  class Generator (line 41) | class Generator(nn.Module):
    method __init__ (line 44) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 49) | def forward(self, x):
  class Discriminator (line 55) | class Discriminator(nn.Module):
    method __init__ (line 59) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 64) | def forward(self, x):
  class FisherGAN (line 70) | class FisherGAN(nn.Module):
    method __init__ (line 73) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class FisherGANTrainer (line 84) | class FisherGANTrainer:
    method __init__ (line 87) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 101) | def train(self, num_epochs, G_lr=1e-4, D_lr=1e-4, D_steps=1, RHO=1e-6):
    method train_D (line 193) | def train_D(self, images):
    method train_G (line 231) | def train_G(self, images):
    method compute_noise (line 250) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 254) | def process_batch(self, iterator):
    method generate_images (line 260) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 296) | def viz_loss(self):
    method save_model (line 317) | def save_model(self, savepath):
    method load_model (line 321) | def load_model(self, loadpath):

FILE: src/info_gan.py
  class Generator (line 45) | class Generator(nn.Module):
    method __init__ (line 49) | def __init__(self, image_size, hidden_dim, z_dim, disc_dim, cont_dim):
    method forward (line 54) | def forward(self, x):
  class Discriminator (line 60) | class Discriminator(nn.Module):
    method __init__ (line 64) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 72) | def forward(self, x):
  class Q (line 78) | class Q(nn.Module):
    method __init__ (line 82) | def __init__(self, image_size, hidden_dim, disc_dim, cont_dim):
    method forward (line 90) | def forward(self, x):
  class InfoGAN (line 97) | class InfoGAN(nn.Module):
    method __init__ (line 100) | def __init__(self, image_size, hidden_dim, z_dim, disc_dim, cont_dim, ...
  class InfoGANTrainer (line 112) | class InfoGANTrainer:
    method __init__ (line 115) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 130) | def train(self, num_epochs, G_lr=2e-4, D_lr=2e-4, D_steps=1):
    method train_D (line 223) | def train_D(self, images):
    method train_G (line 248) | def train_G(self, images):
    method train_Q (line 269) | def train_Q(self, images, LAMBDA=1):
    method compute_noise (line 306) | def compute_noise(self, batch_size, z_dim, disc_dim, cont_dim, c=None):
    method process_batch (line 327) | def process_batch(self, iterator):
    method generate_images (line 333) | def generate_images(self, epoch, num_outputs=36, save=True, c=None):
    method viz_loss (line 367) | def viz_loss(self):
    method save_model (line 388) | def save_model(self, savepath):
    method load_model (line 392) | def load_model(self, loadpath):

FILE: src/ls_gan.py
  class Generator (line 33) | class Generator(nn.Module):
    method __init__ (line 36) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 42) | def forward(self, x):
  class Discriminator (line 48) | class Discriminator(nn.Module):
    method __init__ (line 52) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 58) | def forward(self, x):
  class LSGAN (line 64) | class LSGAN(nn.Module):
    method __init__ (line 67) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class LSGANTrainer (line 78) | class LSGANTrainer:
    method __init__ (line 81) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 95) | def train(self, num_epochs, G_lr=1e-4, D_lr=1e-4, D_steps=1):
    method train_D (line 173) | def train_D(self, images, a=0, b=1):
    method train_G (line 197) | def train_G(self, images, c=1):
    method compute_noise (line 217) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 221) | def process_batch(self, iterator):
    method generate_images (line 227) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 263) | def viz_loss(self):
    method save_model (line 284) | def save_model(self, savepath):
    method load_model (line 288) | def load_model(self, loadpath):

FILE: src/mm_gan.py
  class Generator (line 35) | class Generator(nn.Module):
    method __init__ (line 38) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 44) | def forward(self, x):
  class Discriminator (line 50) | class Discriminator(nn.Module):
    method __init__ (line 54) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 60) | def forward(self, x):
  class MMGAN (line 66) | class MMGAN(nn.Module):
    method __init__ (line 69) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class MMGANTrainer (line 80) | class MMGANTrainer:
    method __init__ (line 83) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 97) | def train(self, num_epochs, G_lr=2e-4, D_lr=2e-4, D_steps=1, G_init=5):
    method train_D (line 195) | def train_D(self, images):
    method train_G (line 220) | def train_G(self, images):
    method compute_noise (line 239) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 243) | def process_batch(self, iterator):
    method generate_images (line 249) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 282) | def viz_loss(self):
    method save_model (line 303) | def save_model(self, savepath):
    method load_model (line 307) | def load_model(self, loadpath):

FILE: src/ns_gan.py
  class Generator (line 35) | class Generator(nn.Module):
    method __init__ (line 38) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 43) | def forward(self, x):
  class Discriminator (line 49) | class Discriminator(nn.Module):
    method __init__ (line 52) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 57) | def forward(self, x):
  class NSGAN (line 63) | class NSGAN(nn.Module):
    method __init__ (line 66) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class NSGANTrainer (line 77) | class NSGANTrainer:
    method __init__ (line 80) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 94) | def train(self, num_epochs, G_lr=2e-4, D_lr=2e-4, D_steps=1):
    method train_D (line 172) | def train_D(self, images):
    method train_G (line 196) | def train_G(self, images):
    method compute_noise (line 218) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 222) | def process_batch(self, iterator):
    method generate_images (line 228) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 264) | def viz_loss(self):
    method save_model (line 283) | def save_model(self, savepath):
    method load_model (line 287) | def load_model(self, loadpath):

FILE: src/ra_gan.py
  class Generator (line 46) | class Generator(nn.Module):
    method __init__ (line 49) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 54) | def forward(self, x):
  class Discriminator (line 60) | class Discriminator(nn.Module):
    method __init__ (line 64) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 69) | def forward(self, x):
  class RaNSGAN (line 75) | class RaNSGAN(nn.Module):
    method __init__ (line 78) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class RaNSGANTrainer (line 89) | class RaNSGANTrainer:
    method __init__ (line 92) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 106) | def train(self, num_epochs, G_lr=2e-4, D_lr=2e-4, D_steps=1):
    method train_D (line 183) | def train_D(self, images):
    method train_G (line 209) | def train_G(self, images):
    method compute_noise (line 231) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 235) | def process_batch(self, iterator):
    method generate_images (line 241) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 277) | def viz_loss(self):
    method save_model (line 298) | def save_model(self, savepath):
    method load_model (line 302) | def load_model(self, loadpath):

FILE: src/utils.py
  function to_var (line 6) | def to_var(x):
  function to_cuda (line 10) | def to_cuda(x):
  function get_data (line 16) | def get_data(BATCH_SIZE=100):

FILE: src/vae.py
  class Encoder (line 47) | class Encoder(nn.Module):
    method __init__ (line 51) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 58) | def forward(self, x):
  class Decoder (line 64) | class Decoder(nn.Module):
    method __init__ (line 68) | def __init__(self, z_dim, hidden_dim, image_size):
    method forward (line 74) | def forward(self, z):
  class VAE (line 80) | class VAE(nn.Module):
    method __init__ (line 84) | def __init__(self, image_size=784, hidden_dim=400, z_dim=20):
    method forward (line 94) | def forward(self, x):
    method reparameterize (line 100) | def reparameterize(self, mu, log_var):
  class VAETrainer (line 109) | class VAETrainer:
    method __init__ (line 110) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 127) | def train(self, num_epochs, lr=1e-3, weight_decay=1e-5):
    method compute_batch (line 193) | def compute_batch(self, batch):
    method kl_divergence (line 210) | def kl_divergence(self, mu, log_var):
    method evaluate (line 214) | def evaluate(self, iterator):
    method reconstruct_images (line 225) | def reconstruct_images(self, images, epoch, save=True):
    method sample_images (line 254) | def sample_images(self, epoch=-100, num_images=36, save=True):
    method sample_interpolated_images (line 278) | def sample_interpolated_images(self):
    method explore_latent_space (line 295) | def explore_latent_space(self, num_epochs=3):
    method make_all (line 336) | def make_all(self):
    method viz_loss (line 348) | def viz_loss(self):
    method save_model (line 367) | def save_model(self, savepath):
    method load_model (line 371) | def load_model(self, loadpath):

FILE: src/w_gan.py
  class Generator (line 43) | class Generator(nn.Module):
    method __init__ (line 46) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 52) | def forward(self, x):
  class Discriminator (line 58) | class Discriminator(nn.Module):
    method __init__ (line 62) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 68) | def forward(self, x):
  class WGAN (line 74) | class WGAN(nn.Module):
    method __init__ (line 77) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class WGANTrainer (line 88) | class WGANTrainer:
    method __init__ (line 91) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 105) | def train(self, num_epochs, G_lr=5e-5, D_lr=5e-5, D_steps=5, clip=0.01):
    method train_D (line 190) | def train_D(self, images):
    method train_G (line 212) | def train_G(self, images):
    method compute_noise (line 231) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 235) | def process_batch(self, iterator):
    method clip_D_weights (line 241) | def clip_D_weights(self, clip):
    method generate_images (line 245) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 281) | def viz_loss(self):
    method save_model (line 302) | def save_model(self, savepath):
    method load_model (line 306) | def load_model(self, loadpath):

FILE: src/w_gp_gan.py
  class Generator (line 34) | class Generator(nn.Module):
    method __init__ (line 37) | def __init__(self, image_size, hidden_dim, z_dim):
    method forward (line 43) | def forward(self, x):
  class Discriminator (line 49) | class Discriminator(nn.Module):
    method __init__ (line 53) | def __init__(self, image_size, hidden_dim, output_dim):
    method forward (line 59) | def forward(self, x):
  class WGPGAN (line 65) | class WGPGAN(nn.Module):
    method __init__ (line 68) | def __init__(self, image_size, hidden_dim, z_dim, output_dim=1):
  class WGPGANTrainer (line 79) | class WGPGANTrainer:
    method __init__ (line 82) | def __init__(self, model, train_iter, val_iter, test_iter, viz=False):
    method train (line 96) | def train(self, num_epochs, G_lr=1e-4, D_lr=1e-4, D_steps=5):
    method train_D (line 177) | def train_D(self, images, LAMBDA=10):
    method train_G (line 222) | def train_G(self, images):
    method compute_noise (line 241) | def compute_noise(self, batch_size, z_dim):
    method process_batch (line 245) | def process_batch(self, iterator):
    method generate_images (line 251) | def generate_images(self, epoch, num_outputs=36, save=True):
    method viz_loss (line 287) | def viz_loss(self):
    method save_model (line 308) | def save_model(self, savepath):
    method load_model (line 312) | def load_model(self, loadpath):
Copy disabled (too large) Download .json
Condensed preview — 34 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (18,163K chars).
[
  {
    "path": ".gitignore",
    "chars": 50,
    "preview": "env\ndata\n__pycache__\n.ipynb_checkpoints\n.DS_store\n"
  },
  {
    "path": "LICENSE",
    "chars": 1071,
    "preview": "MIT License\n\nCopyright (c) 2018 Shayne O'Brien\n\nPermission is hereby granted, free of charge, to any person obtaining a "
  },
  {
    "path": "README.md",
    "chars": 9576,
    "preview": "# Overview\nPyTorch 0.4.1 | Python 3.6.5\n\nAnnotated implementations with comparative introductions for minimax, non-satur"
  },
  {
    "path": "notebooks/01-nonsaturating-gan.ipynb",
    "chars": 1187034,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/02-minimax-gan.ipynb",
    "chars": 1082268,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/03-wasserstein-gan.ipynb",
    "chars": 1645015,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/04-wasserstein-gan-gradient-penalty.ipynb",
    "chars": 1538459,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/05-least-squares-gan.ipynb",
    "chars": 1150079,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/06-deep-regret-analytic-gan.ipynb",
    "chars": 1544229,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/07-boundary-equilibrium-gan.ipynb",
    "chars": 1014176,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/08-standard-autoencoder.ipynb",
    "chars": 333380,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outpu"
  },
  {
    "path": "notebooks/09-variational-autoencoder.ipynb",
    "chars": 1358597,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/10-relativistic-nonsaturing-gan.ipynb",
    "chars": 1310991,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/11-information-gan.ipynb",
    "chars": 1346137,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/12-f-divergence-gan.ipynb",
    "chars": 1367377,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/13-fisher-gan.ipynb",
    "chars": 1458342,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "notebooks/14-bounded-information-rate-variational-autoencoder.ipynb",
    "chars": 1597218,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\":"
  },
  {
    "path": "requirements.txt",
    "chars": 57,
    "preview": "ipython\njupyter\nmatplotlib\ntqdm\ntorch==0.4.1\ntorchvision\n"
  },
  {
    "path": "src/ae.py",
    "chars": 7934,
    "preview": "\"\"\" (AE)\nStandard Autoencoder\n\nAutoencoders take an input representation, encode it into a reduced\ndimensionality space "
  },
  {
    "path": "src/bayes_gan.py",
    "chars": 1599,
    "preview": "# TODO\n\"\"\" (BayesGAN) https://arxiv.org/abs/1705.09558\nBayesian GAN\n\nFrom the authors:\n\n\"Bayesian GAN (Saatchi and Wilso"
  },
  {
    "path": "src/be_gan.py",
    "chars": 12812,
    "preview": "\"\"\" (BEGAN) https://arxiv.org/abs/1703.10717\nBoundary Equilibrium GAN\n\nBEGAN uses an autoencoder as a discriminator and "
  },
  {
    "path": "src/bir_vae.py",
    "chars": 15003,
    "preview": "\"\"\" (BIR-VAE) https://arxiv.org/abs/1807.07306\nBounded Information Rate Variational Autoencoder\n\nThis VAE variant makes "
  },
  {
    "path": "src/dra_gan.py",
    "chars": 12120,
    "preview": "\"\"\" (DRAGAN) https://arxiv.org/abs/1705.07215\nDeep Regret Analytic GAN\n\nThe output of DRAGAN's D can be interpretted as "
  },
  {
    "path": "src/f_gan.py",
    "chars": 13644,
    "preview": "\"\"\" (f-GAN) https://arxiv.org/abs/1606.00709\nf-Divergence GANs\n\nThe authors empirically demonstrate that when the genera"
  },
  {
    "path": "src/fisher_gan.py",
    "chars": 12163,
    "preview": "\"\"\" (FisherGAN) https://arxiv.org/abs/1606.07536\nFisher GAN\n\nFrom the abstract:\n\"In this paper we introduce Fisher GAN w"
  },
  {
    "path": "src/info_gan.py",
    "chars": 15547,
    "preview": "\"\"\" (InfoGAN) https://arxiv.org/abs/1606.03657\nInformation GAN\n\nFrom the paper:\n\n\"In this paper, we present a simple mod"
  },
  {
    "path": "src/ls_gan.py",
    "chars": 10861,
    "preview": "\"\"\" (LS GAN) https://arxiv.org/abs/1611.04076\nLeast Squares GAN\n\nThe output of LSGAN's D is unbounded unless passed thro"
  },
  {
    "path": "src/mm_gan.py",
    "chars": 11507,
    "preview": "\"\"\" (MM GAN) https://arxiv.org/abs/1406.2661\nMini-max GAN\n\nFrom the abstract: 'We propose a new framework for estimating"
  },
  {
    "path": "src/ns_gan.py",
    "chars": 10648,
    "preview": "\"\"\" (NS GAN) https://arxiv.org/abs/1406.2661\nNon-saturating GAN.\n\nFrom the abstract: 'We propose a new framework for est"
  },
  {
    "path": "src/ra_gan.py",
    "chars": 11476,
    "preview": "\"\"\" (RaGAN) https://arxiv.org/abs/1807.00734\nRelativistic GAN\n\nRelativistic GANs argue that the GAN generator should dec"
  },
  {
    "path": "src/utils.py",
    "chars": 1957,
    "preview": "import torch\nimport torchvision.datasets as datasets\nimport torchvision.transforms as transforms\n\n\ndef to_var(x):\n    \"\""
  },
  {
    "path": "src/vae.py",
    "chars": 14617,
    "preview": "\"\"\" (VAE) https://arxiv.org/abs/1312.6114\nVariational Autoencoder\n\nFrom the abstract:\n\n\"We introduce a stochastic variat"
  },
  {
    "path": "src/w_gan.py",
    "chars": 11706,
    "preview": "\"\"\" (WGAN) https://arxiv.org/abs/1701.07875\nWasserstein GAN\n\nThe output of WGAN's D is unbounded unless passed through a"
  },
  {
    "path": "src/w_gp_gan.py",
    "chars": 12008,
    "preview": "\"\"\" (WGPGAN) https://arxiv.org/abs/1701.07875\nWasserstein GAN with Gradient Penalties ('Improved Training of Wasserstein"
  }
]

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This page contains the full source code of the shayneobrien/generative-models GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 34 files (17.3 MB), approximately 4.5M tokens, and a symbol index with 288 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

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