Created
February 3, 2021 17:12
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DCGan Model for DCGAN Tutorial
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class Generator(nn.Module): | |
def __init__(self, ngpu): | |
super(Generator, self).__init__() | |
self.ngpu = ngpu | |
self.main = nn.Sequential( | |
# input is Z, going into a convolution | |
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False), | |
nn.BatchNorm2d(ngf * 8), | |
nn.ReLU(True), | |
# state size. (ngf*8) x 4 x 4 | |
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ngf * 4), | |
nn.ReLU(True), | |
# state size. (ngf*4) x 8 x 8 | |
nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ngf * 2), | |
nn.ReLU(True), | |
# state size. (ngf*2) x 16 x 16 | |
nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ngf), | |
nn.ReLU(True), | |
# state size. (ngf) x 32 x 32 | |
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False), | |
nn.Tanh() | |
# state size. (nc) x 64 x 64 | |
) | |
def forward(self, input): | |
return self.main(input) | |
class Discriminator(nn.Module): | |
def __init__(self, ngpu): | |
super(Discriminator, self).__init__() | |
self.ngpu = ngpu | |
self.main = nn.Sequential( | |
# input is (nc) x 64 x 64 | |
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), | |
nn.LeakyReLU(0.2, inplace=True), | |
# state size. (ndf) x 32 x 32 | |
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ndf * 2), | |
nn.LeakyReLU(0.2, inplace=True), | |
# state size. (ndf*2) x 16 x 16 | |
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ndf * 4), | |
nn.LeakyReLU(0.2, inplace=True), | |
# state size. (ndf*4) x 8 x 8 | |
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), | |
nn.BatchNorm2d(ndf * 8), | |
nn.LeakyReLU(0.2, inplace=True), | |
# state size. (ndf*8) x 4 x 4 | |
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False), | |
nn.Sigmoid() | |
) | |
def forward(self, input): | |
return self.main(input) |
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