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 test_forward():
device = None
act = F.leaky_relu
model = Model(device, act)
model_params = OrderedDict([x for x in model.namedparams()])
x_data = np.random.rand(4, 3, 32, 32).astype(np.float32)
y_data = np.random.randint(0, 10, 4).astype(np.int32)
x = Variable(x_data)
y = Variable(y_data)
# forward
y_pred = model(x, model_params, test=False)
l = F.softmax_cross_entropy(y_pred, y)
# backward
model.cleargrads()
l.backward(retain_grad=True)
# change variable held in model_params
for k, v in model_params.items():
w = Variable(np.copy(v.grad))
w_ = F.dropout(w)
model_params[k] = w_
# forward
y_pred = model(x, model_params, test=False)
l = F.softmax_cross_entropy(y_pred, y)
# backward
model.cleargrads()
l.backward(retain_grad=True)
# check
print("after backward")
for k, v in model_params.items():
if v.grad is not None:
print(k)
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()