Fixed auto initialization and removed spurious statements
@@ -122,7 +122,7 @@ class RelationalTucker3(KgeModel):
log=True,
)
- # core tensor weight -> initial scores have var=1
+ # core tensor weight -> initial scores have var=1 (when no dropout / eval)
config.set(
config.get("model") + ".relation_embedder.initialize",
"normal",
@@ -130,19 +130,10 @@ class RelationalTucker3(KgeModel):
)
config.set(
config.get("model") + ".relation_embedder.initialize_arg",
- math.sqrt(0.5),
+ 1.0,
log=True
)
super().__init__(
config, dataset, scorer=RescalScorer(config=config, dataset=dataset)
)
- import numpy
- print(
- torch.std(
- self.score_spo(
- torch.tensor(numpy.arange(100)),
- torch.tensor(numpy.arange(100)),
- torch.tensor(numpy.arange(100)+100),
- ))
- )
