def __init__(self):
super().__init__()
self.m = nn.ConvTranspose2d(3,
3,
3,
stride=3,
bias=False,
padding=1,
output_padding=2)
def __init__(self):
super(TraceModel, self).__init__()
self.conv1 = nn.ConvTranspose1d(16, 33, 3, stride=2)
self.conv2 = nn.ConvTranspose2d(16,
33, (3, 5),
stride=(2, 1),
padding=(4, 2),
dilation=(3, 1))
self.conv3 = nn.ConvTranspose3d(16,
33, (3, 5, 2),
stride=(2, 1, 1),
padding=(4, 2, 0))
def __init__(self, nz, ngf, nc):
super(DCGANGenerator, self).__init__()
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
)
