clean up duplicate code
@@ -12,8 +12,8 @@ from thinc.api import chain
from thinc.v2v import Affine, Maxout, Softmax
from thinc.misc import LayerNorm
from thinc.neural.util import to_categorical
-from thinc.neural.util import get_array_module
+from ..cli.pretrain import get_cossim_loss
from .functions import merge_subtokens
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
@@ -1162,26 +1162,11 @@ class EntityLinker(Pipe):
return 0
def get_loss(self, docs, golds, scores):
- targets = [[1] for _ in golds] # assuming we're only using positive examples
- loss, gradients = self.get_cossim_loss_2(yh=scores, y=golds, t=targets)
+ # this loss function assumes we're only using positive examples
+ loss, gradients = get_cossim_loss(yh=scores, y=golds)
loss = loss / len(golds)
return loss, gradients
- def get_cossim_loss_2(self, yh, y, t):
- # Add a small constant to avoid 0 vectors
- yh = yh + 1e-8
- y = y + 1e-8
- # https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
- xp = get_array_module(yh)
- norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
- norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
- mul_norms = norm_yh * norm_y
- cos = (yh * y).sum(axis=1, keepdims=True) / mul_norms
- d_yh = (y / mul_norms) - (cos * (yh / norm_yh ** 2))
- loss = xp.abs(cos - t).sum()
- inverse = np.asarray([int(t[i][0]) * d_yh[i] for i in range(len(t))])
- return loss, -inverse
-
def __call__(self, doc):
entities, kb_ids = self.predict([doc])
self.set_annotations([doc], entities, kb_ids)
