#

Deep Convolutional Generative Adversarial Networks (DCGAN)

This is a PyTorch implementation of paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

This implementation is based on the PyTorch DCGAN Tutorial.

15import torch.nn as nn
16
17from labml import experiment
18from labml.configs import calculate
19from labml_helpers.module import Module
20from labml_nn.gan.original.experiment import Configs

#

Convolutional Generator Network

This is similar to the de-convolutional network used for CelebA faces, but modified for MNIST images.

DCGan Architecture

23class Generator(Module):

#

33    def __init__(self):
34        super().__init__()

#

The input is $1 \times 1$ with 100 channels

36        self.layers = nn.Sequential(

#

This gives $3 \times 3$ output

38            nn.ConvTranspose2d(100, 1024, 3, 1, 0, bias=False),
39            nn.BatchNorm2d(1024),
40            nn.ReLU(True),

#

This gives $7 \times 7$

42            nn.ConvTranspose2d(1024, 512, 3, 2, 0, bias=False),
43            nn.BatchNorm2d(512),
44            nn.ReLU(True),

#

This gives $14 \times 14$

46            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
47            nn.BatchNorm2d(256),
48            nn.ReLU(True),

#

This gives $28 \times 28$

50            nn.ConvTranspose2d(256, 1, 4, 2, 1, bias=False),
51            nn.Tanh()
52        )
53
54        self.apply(_weights_init)

#

56    def forward(self, x):

#

Change from shape [batch_size, 100] to [batch_size, 100, 1, 1]

58        x = x.unsqueeze(-1).unsqueeze(-1)
59        x = self.layers(x)
60        return x

#

Convolutional Discriminator Network

63class Discriminator(Module):

#

68    def __init__(self):
69        super().__init__()

#

The input is $28 \times 28$ with one channel

71        self.layers = nn.Sequential(

#

This gives $14 \times 14$

73            nn.Conv2d(1, 256, 4, 2, 1, bias=False),
74            nn.LeakyReLU(0.2, inplace=True),

#

This gives $7 \times 7$

76            nn.Conv2d(256, 512, 4, 2, 1, bias=False),
77            nn.BatchNorm2d(512),
78            nn.LeakyReLU(0.2, inplace=True),

#

This gives $3 \times 3$

80            nn.Conv2d(512, 1024, 3, 2, 0, bias=False),
81            nn.BatchNorm2d(1024),
82            nn.LeakyReLU(0.2, inplace=True),

#

This gives $1 \times 1$

84            nn.Conv2d(1024, 1, 3, 1, 0, bias=False),
85        )
86        self.apply(_weights_init)

#

88    def forward(self, x):
89        x = self.layers(x)
90        return x.view(x.shape[0], -1)

#

93def _weights_init(m):
94    classname = m.__class__.__name__
95    if classname.find('Conv') != -1:
96        nn.init.normal_(m.weight.data, 0.0, 0.02)
97    elif classname.find('BatchNorm') != -1:
98        nn.init.normal_(m.weight.data, 1.0, 0.02)
99        nn.init.constant_(m.bias.data, 0)

#

We import the simple gan experiment and change the generator and discriminator networks

104calculate(Configs.generator, 'cnn', lambda c: Generator().to(c.device))
105calculate(Configs.discriminator, 'cnn', lambda c: Discriminator().to(c.device))

#

108def main():
109    conf = Configs()
110    experiment.create(name='mnist_dcgan')
111    experiment.configs(conf,
112                       {'discriminator': 'cnn',
113                        'generator': 'cnn',
114                        'label_smoothing': 0.01})
115    with experiment.start():
116        conf.run()
117
118
119if __name__ == '__main__':
120    main()