def make_decoder(self):
return nn.Sequential(
nn.ReLU(),
nn.Dropout2d(p=self.dropout),
nn.Linear(self.latent_dim, 1500),
nn.BatchNorm1d(1500),
nn.ReLU(),
nn.Linear(1500, 3136),
nn.BatchNorm1d(3136),
nn.ReLU(),
nn.Dropout2d(p=self.dropout),
Reshape(64, 7, 7),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.ConvTranspose2d(32, 1, kernel_size=3, stride=1, padding=1)
)
def make_decoder(self):
return nn.Sequential(
nn.Linear(self.latent_dim, 512), nn.ReLU(), Reshape(32, 4, 4),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16), nn.ReLU(),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(16, 8, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(8), nn.ReLU(),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(8, 3, kernel_size=3, stride=1, padding=1))
def make_decoder(self):
return nn.Sequential(
nn.ReLU(), nn.Linear(self.latent_dim, 4096), nn.ReLU(),
Reshape(256, 4, 4), nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.BatchNorm2d(256),
nn.ConvTranspose2d(256, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(), nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.BatchNorm2d(128),
nn.ConvTranspose2d(128, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(), nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.BatchNorm2d(64),
nn.ConvTranspose2d(64, 3, kernel_size=3, stride=1, padding=1),
nn.ReLU())
def make_decoder(self):
return nn.Sequential(
nn.ReLU(), nn.Linear(512, 4096), nn.BatchNorm1d(4096), nn.ReLU(),
Reshape(64, 8, 8), nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), nn.ReLU(),
nn.ConvTranspose2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32), nn.ReLU(),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(32, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32), nn.ReLU(),
nn.ConvTranspose2d(32, 3, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(3))
def make_decoder(self):
return nn.Sequential(
nn.ReLU(),
nn.Linear(160, 3200),
nn.ReLU(),
nn.Linear(3200, 5000),
Reshape(50, 10, 10),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(50, 50, kernel_size=5, stride=1, padding=0),
nn.BatchNorm2d(50),
nn.ReLU(),
nn.ConvTranspose2d(50, 1, kernel_size=5, stride=1, padding=0)
)
def make_decoder(self):
return nn.Sequential(
nn.ReLU(),
nn.Dropout2d(p=self.dropout),
nn.Linear(self.latent_dim, 3136),
nn.ReLU(),
nn.Dropout2d(p=self.dropout),
nn.Linear(3136, 6272),
nn.ReLU(),
Reshape(32, 14, 14),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(32, 1, kernel_size=3, stride=1, padding=1)
)
def make_decoder(self):
return nn.Sequential(
nn.ReLU(),
nn.Linear(self.latent_dim, 2450),
nn.BatchNorm1d(2450),
nn.ReLU(),
nn.Dropout2d(p=self.dropout),
Reshape(50, 7, 7),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(50, 20, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(20),
nn.ReLU(),
nn.MaxUnpool2d(kernel_size=2, stride=2),
nn.ConvTranspose2d(20, 1, kernel_size=5, stride=1, padding=2)
)