def __init__(self, device=None, learning_rate=1e-3, act=F.relu, n_cls=10):
super(Experiment003, self).__init__(device=device,
learning_rate=learning_rate,
act=act,
n_cls=n_cls)
# Loss
self.recon_loss = ReconstructionLoss()
self.er_loss = EntropyRegularizationLoss()
def __init__(self, device=None, learning_rate=1e-3, act=F.leaky_relu):
# Settings
self.device = device
self.act = act
self.learning_rate = learning_rate
# Loss
self.recon_loss = ReconstructionLoss()
# Model
from meta_st.cifar10.ref_cnn_model_000 import Model
self.model = Model(device, act)
self.model.to_gpu(device) if device is not None else None
# Optimizer
self.optimizer = optimizers.Adam(learning_rate)
self.optimizer.setup(self.model)
self.optimizer.use_cleargrads()
def __init__(self, device=None, learning_rate=1e-3, act=F.relu, T=3):
# Settings
self.device = device
self.act = act
self.learning_rate = learning_rate
self.T = T
self.t = 0
# Loss
self.recon_loss = ReconstructionLoss()
# Model
from meta_st.cifar10.cnn_model_001 import Model
self.model = Model(device, act)
self.model.to_gpu(device) if device is not None else None
self.model_params = OrderedDict([x for x in self.model.namedparams()])
# Optimizer
self.setup_meta_learners()
def __init__(self, device=None, learning_rate=1e-3, act=F.relu, T=3):
# Settings
self.device = device
self.act = act
self.learning_rate = learning_rate
# Loss
self.recon_loss = ReconstructionLoss()
# Model
from meta_st.cifar10.cnn_model_001_small import Model
self.model = Model(device, act)
self.model.to_gpu(device) if device is not None else None
self.model_params = OrderedDict([x for x in self.model.namedparams()])
# Optimizer for model
self.optimizer = Adam()
self.optimizer.setup(self.model)
self.optimizer.use_cleargrads()
# Optimizer, or Meta-Learner (ML)
self.setup_meta_learners()
def __init__(self, device=None, learning_rate=1e-3, act=F.leaky_relu, T=3):
# Settings
self.device = device
self.act = act
self.learning_rate = learning_rate
self.T = 3
self.t = 0
self.loss_ml = 0
# Loss
self.rc_loss = ReconstructionLoss()
# Model
from meta_st.cifar10.cnn_model_001 import Model
self.model = Model(device, act)
self.model.to_gpu(device) if device is not None else None
self.model_params = OrderedDict([x for x in self.model.namedparams()])
# Optimizer
self.optimizer = Adam(learning_rate) #TODO: adam is appropriate?
self.optimizer.setup(self.model)
self.optimizer.use_cleargrads()
self.setup_meta_learners()