def forward(self, x):
out = F.Sigmoid(self.bn1(self.conv1(x)))
out = F.Sigmoid(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.Sigmoid(out)
return out
def forward(self, x):
out = F.Sigmoid(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def __init__(self):
super(CVAE, self).__init__()
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
# For Encoder
self.fcE = nn.Linear(X_dim + y_dim, h_dim)
self.fcE_mu = nn.Linear(h_dim, Z_dim)
self.fcE_var = nn.Linear(h_dim, Z_dim)
# For Decoder
self.fcD1 = nn.Linear(Z_dim + y_dim, h_dim)
self.fcD2 = nn.Linear(h_dim, X_dim)
('conv3_1', nn.Conv2d(64, 64, kernel_size=3,stride = 1, padding = 1)),
('relu3_1', nn.ReLU()),
('weight_norm3_1', nn.utils.weight_norm()), # Look into this later
('conv3_2', nn.Conv2d(64, 64, kernel_size=3,stride = 1, padding = 1)),
('relu3_2', nn.ReLU())
('weight_norm3_2', nn.utils.weight_norm()), # Look into this later
('pool3', nn.MaxPool2d(2,2)),
('fc4', nn.Linear(12288, 100)),
('tanh4' nn.Tanh()),
('fc5', nn.Linear(100,2)),
('tanh5' nn.Tanh()),
('fc6', nn.Linear(2,1)),
# ('sigmoid6' F.Sigmoid())
]))
return F.Sigmoid(net)
class DiscriminatorNet(nn.Module):
def __init__(self):
super(DiscriminatorNet, self).__init__()
self.net = nn.Sequential(OrderedDict([
('merge', nn.Conv2d(4, 3, kernel_size=1,stride = 1, padding = 0)),
('conv1', nn.Conv2d(3, 32, kernel_size=3,stride = 1, padding = 1)),
('relu1', nn.ReLU()),
('pool1', nn.MaxPool2d(4,4)),
('conv2_1', nn.Conv2d(32, 64, kernel_size=3,stride = 1, padding = 1)),
('relu2_1', nn.ReLU()),
('conv2_2', nn.Conv2d(64, 64, kernel_size=3,stride = 1, padding = 1)),
('relu2_2', nn.ReLU()),