def create_generator(self, parameters=None, encoded_parameters=None):
net = GeneratorNet(
self.loss_function,
nn.Sequential(
nn.ConvTranspose2d(100, self.complexity * 8, 4, 1, 0),
nn.BatchNorm2d(self.complexity * 8), nn.ReLU(True),
nn.ConvTranspose2d(self.complexity * 8, self.complexity * 4, 4,
2, 1), nn.BatchNorm2d(self.complexity * 4),
nn.ReLU(True),
nn.ConvTranspose2d(self.complexity * 4, self.complexity * 2, 4,
2, 1), nn.BatchNorm2d(self.complexity * 2),
nn.ReLU(True),
nn.ConvTranspose2d(self.complexity * 2, self.complexity, 4, 2,
1), nn.BatchNorm2d(self.complexity),
nn.ReLU(True), nn.ConvTranspose2d(self.complexity, 3, 4, 2, 1),
nn.Tanh()), self.gen_input_size)
if parameters is not None:
net.parameters = parameters
elif encoded_parameters is not None:
net.encoded_parameters = encoded_parameters
else:
net.net.apply(self._init_weights)
return net
def create_generator(self, parameters=None, encoded_parameters=None):
net = GeneratorNet(
self.loss_function,
Sequential(nn.Linear(self.gen_input_size, 128), nn.Tanh(),
nn.Linear(128, 128), nn.Tanh(),
nn.Linear(128, self.input_data_size)),
self.gen_input_size)
if parameters is not None:
net.parameters = parameters
if encoded_parameters is not None:
net.encoded_parameters = encoded_parameters
return net
def create_generator(self, parameters=None, encoded_parameters=None):
net = GeneratorNet(
self.loss_function,
Sequential(nn.Linear(64, 256), nn.LeakyReLU(0.2),
nn.Linear(256, 256), nn.LeakyReLU(0.2),
nn.Linear(256, self.input_data_size), nn.Tanh()),
self.gen_input_size)
if parameters is not None:
net.parameters = parameters
if encoded_parameters is not None:
net.encoded_parameters = encoded_parameters
return net
def create_generator(self, parameters=None, encoded_parameters=None):
net = GeneratorNet(
self.loss_function,
nn.Sequential(
nn.ConvTranspose3d(self.z_size,
self.cube_len * 8,
kernel_size=4,
stride=2,
bias=self.bias,
padding=self.pad),
nn.BatchNorm3d(self.cube_len * 8), nn.ReLU(),
nn.ConvTranspose3d(self.cube_len * 8,
self.cube_len * 4,
kernel_size=4,
stride=2,
bias=self.bias,
padding=(1, 1, 1)),
nn.BatchNorm3d(self.cube_len * 4), nn.ReLU(),
nn.ConvTranspose3d(self.cube_len * 4,
self.cube_len * 2,
kernel_size=4,
stride=2,
bias=self.bias,
padding=(1, 1, 1)),
nn.BatchNorm3d(self.cube_len * 2), nn.ReLU(),
nn.ConvTranspose3d(self.cube_len * 2,
self.cube_len,
kernel_size=4,
stride=2,
bias=self.bias,
padding=(1, 1, 1)),
nn.BatchNorm3d(self.cube_len), nn.ReLU(),
nn.ConvTranspose3d(self.cube_len,
1,
kernel_size=4,
stride=2,
bias=self.bias,
padding=(1, 1, 1)), nn.Sigmoid()),
self.gen_input_size)
if not (parameters == None):
net.parameters = parameters
elif not (encoded_parameters == None):
net.encoded_parameters = encoded_parameters
else:
net.net.apply(self._init_weights)
return net