def get_opt():
opt = Namespace()
# Model
opt.CAPACITY = 32
opt.NUM_COMPONENTS = 10
opt.GMM_NUM_COMPONENTS = 10
# Training
opt.LR = 0.001
opt.NUM_ITER = 3000
opt.CUDA = True
opt.REC_FREQ = 10
# Meta
opt.ALPHA_LR = 0.1
# opt.ALPHA_NUM_ITER = 500
# opt.ALPHA_NUM_ITER = 50
opt.ALPHA_NUM_ITER = 10
opt.FINETUNE_LR = 0.001
opt.FINETUNE_NUM_ITER = 10
opt.PARAM_DISTRY = lambda mean: normal(mean, 2, opt.NUM_SAMPLES)
opt.PARAM_SAMPLER = lambda: np.random.uniform(-4, 4)
# Sampling
opt.NUM_SAMPLES = 1000
opt.TRAIN_DISTRY = lambda: normal(0, 2, opt.NUM_SAMPLES)
opt.TRANS_DISTRY = lambda: normal(random.randint(-4, 4), 2, opt.NUM_SAMPLES
)
return opt
print(f"Encoded (U, V) = {_X_de.item()}, {_Y_de.item()}")
opt = Namespace()
# Model
opt.CAPACITY = 32
opt.NUM_COMPONENTS = 10
opt.GMM_NUM_COMPONENTS = 10
# Training
opt.LR = 0.01
opt.NUM_ITER = 20
opt.NUM_META_ITER = 1000
opt.ENCODER_LR = 0.01
opt.ALPHA_LR = 0.001
opt.CUDA = True
opt.REC_FREQ = 10
opt.ALPHA_INIT = 0.
opt.USE_BASELINE = True
# Fine tuning
opt.FINETUNE_NUM_ITER = 5
opt.FINETUNE_LR = 0.001
opt.EM_ITERS = 500
# Sampling
opt.NUM_SAMPLES = 1000
opt.TRAIN_DISTRY = lambda: normal(0, 2, opt.NUM_SAMPLES)
opt.TRANS_DISTRY = lambda: normal(np.random.uniform(-4, 4), 2, opt.NUM_SAMPLES)
def normal(mean, std, N):
return torch.normal(torch.ones(N).mul_(mean), torch.ones(N).mul_(std)).view(-1, 1)
opt = Namespace()
# Model
opt.CAPACITY = 32
opt.NUM_COMPONENTS = 10
opt.GMM_NUM_COMPONENTS = 10
# Training
opt.LR = 0.001
opt.NUM_ITER = 3000
opt.CUDA = False
opt.REC_FREQ = 10
# Meta
opt.ALPHA_LR = 0.1
opt.ALPHA_NUM_ITER = 50
opt.FINETUNE_LR = 0.001
opt.FINETUNE_NUM_ITER = 10
opt.PARAM_DISTRY = lambda mean: normal(mean, 2, opt.NUM_TRANS_SAMPLES)
opt.PARAM_SAMPLER = lambda: np.random.uniform(-4, 4)
# Sampling
opt.NUM_SAMPLES = 1000
opt.NUM_TRANS_SAMPLES = 1000
opt.TRAIN_DISTRY = lambda: normal(0, 2, opt.NUM_SAMPLES)
opt.TRAIN_EVAL_DISTRY = lambda: normal(0, 2, 10000)
#opt.TRANS_DISTRY = lambda: normal(random.randint(-4, 4), 2, opt.NUM_SAMPLES)
alpha_list = []
beta_list = []
gamma_list = []
