def __init__(self, nc, ndf, alpha, beta, ngpu=1):
super(DiscriminatorNetVBN, self).__init__()
self.ngpu = ngpu
self.net = nnrd.RelevanceNet(
nnrd.Layer(
nnrd.FirstConvolution(nc, ndf, 5, 1, 2),
nnrd.ReLu(),
),
nnrd.Layer(
nnrd.NextConvolution(ndf, ndf, 4, '0', 2, 1, alpha=alpha),
nnrd.BatchNorm2d(ndf),
nnrd.ReLu(),
),
# state size. (ndf) x 32 x 32
nnrd.Layer(
nnrd.NextConvolution(ndf, ndf * 2, 4, '1', 2, 1, alpha=alpha),
nnrd.BatchNorm2d(ndf * 2),
nnrd.ReLu(),
),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
nnrd.NextConvolution(ndf * 2,
ndf * 4,
4,
'2',
2,
1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 4),
nnrd.ReLu(),
),
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
nnrd.NextConvolution(ndf * 4,
ndf * 8,
4,
'3',
2,
1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 8),
nnrd.ReLu(),
),
# state size. (ndf*8) x 4 x 4
nnrd.Layer(nnrd.NextConvolution(ndf * 8, 1, 4, '4', 1, 0),
nn.Sigmoid()))
def __init__(self, ngpu):
super(DiscriminatorNet, self).__init__()
self.ngpu = ngpu
# All attributes are automatically assigned to the modules parameter list
n_features = 784
n_out = 1
self.net = nnrd.RelevanceNet(
nnrd.Layer(nnrd.FirstLinear(n_features, 1024), nnrd.ReLu(),
nnrd.Dropout(0.3)),
nnrd.Layer(nnrd.NextLinear(1024, 1024), nnrd.ReLu(),
nnrd.Dropout(0.3)),
nnrd.Layer(
nnrd.NextLinear(1024, 512),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
nnrd.Layer(nnrd.NextLinear(512, 512), nnrd.ReLu(),
nnrd.Dropout(0.3)),
nnrd.Layer(
nnrd.NextLinear(512, 256),
nnrd.ReLu(),
nnrd.Dropout(0.3),
), nnrd.Layer(nnrd.NextLinear(256, n_out), nn.Sigmoid()))
def __init__(self, nc, ndf, alpha, beta, ngpu=1):
super(DiscriminatorNetLessCheckerboard, self).__init__()
self.ngpu = ngpu
self.net = nnrd.RelevanceNet(
nnrd.Layer(
nnrd.FirstConvolution(in_channels=nc,
out_channels=ndf,
kernel_size=5,
stride=1,
padding=0),
nnrd.ReLu(),
),
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf,
kernel_size=4,
name='0',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf) x 32 x 32
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf * 2,
kernel_size=4,
name='1',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 2),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf * 2,
out_channels=ndf * 4,
kernel_size=4,
name='2',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 4),
nnrd.ReLu(),
nnrd.Dropout(0.3),
), # state size. (ndf*2) x 16 x 16
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf * 4,
out_channels=ndf * 8,
kernel_size=4,
name='3',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 8),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf*8) x 4 x 4
nnrd.Layer(
nnrd.NextConvolutionEps(in_channels=ndf * 8,
out_channels=1,
kernel_size=4,
name='4',
stride=1,
padding=0), nn.Sigmoid()))