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Use cross entropy from thinc.legacy

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This commit is contained in:
Daniël de Kok 2022-12-02 16:30:03 +01:00
parent 1d705cc683
commit 1041f68be2
4 changed files with 16 additions and 9 deletions
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7
spacy/pipeline/edit_tree_lemmatizer.py
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@ -5,8 +5,9 @@ from itertools import islice
import numpy as np
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy
from thinc.api import Config, Model
from thinc.types import ArrayXd, Floats2d, Ints1d
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from ._edit_tree_internals.edit_trees import EditTrees
from ._edit_tree_internals.schemas import validate_edit_tree
@ -129,7 +130,9 @@ class EditTreeLemmatizer(TrainablePipe):
self, examples: Iterable[Example], scores: List[Floats2d]
) -> Tuple[float, List[Floats2d]]:
validate_examples(examples, "EditTreeLemmatizer.get_loss")
loss_func = SequenceCategoricalCrossentropy(normalize=False, missing_value=-1)
loss_func = LegacySequenceCategoricalCrossentropy(
normalize=False, missing_value=-1
)
truths = []
for eg in examples:

5
spacy/pipeline/morphologizer.pyx
View File

@ -1,7 +1,8 @@
# cython: infer_types=True, profile=True, binding=True
from typing import Callable, Dict, Iterable, List, Optional, Union
import srsly
from thinc.api import SequenceCategoricalCrossentropy, Model, Config
from thinc.api import Model, Config
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from itertools import islice
@ -290,7 +291,7 @@ class Morphologizer(Tagger):
DOCS: https://spacy.io/api/morphologizer#get_loss
"""
validate_examples(examples, "Morphologizer.get_loss")
loss_func = SequenceCategoricalCrossentropy(names=tuple(self.labels), normalize=False)
loss_func = LegacySequenceCategoricalCrossentropy(names=tuple(self.labels), normalize=False)
truths = []
for eg in examples:
eg_truths = []

6
spacy/pipeline/senter.pyx
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@ -3,7 +3,9 @@ from typing import Dict, Iterable, Optional, Callable, List, Union
from itertools import islice
import srsly
from thinc.api import Model, SequenceCategoricalCrossentropy, Config
from thinc.api import Model, Config
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
from ..tokens.doc cimport Doc
@ -161,7 +163,7 @@ class SentenceRecognizer(Tagger):
"""
validate_examples(examples, "SentenceRecognizer.get_loss")
labels = self.labels
loss_func = SequenceCategoricalCrossentropy(names=labels, normalize=False)
loss_func = LegacySequenceCategoricalCrossentropy(names=labels, normalize=False)
truths = []
for eg in examples:
eg_truth = []

7
spacy/pipeline/tagger.pyx
View File

@ -2,7 +2,8 @@
from typing import Callable, Dict, Iterable, List, Optional, Union
import numpy
import srsly
from thinc.api import Model, set_dropout_rate, SequenceCategoricalCrossentropy, Config
from thinc.api import Model, set_dropout_rate, Config
from thinc.legacy import LegacySequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d
import warnings
from itertools import islice
@ -244,7 +245,7 @@ class Tagger(TrainablePipe):
DOCS: https://spacy.io/api/tagger#rehearse
"""
loss_func = SequenceCategoricalCrossentropy()
loss_func = LegacySequenceCategoricalCrossentropy()
if losses is None:
losses = {}
losses.setdefault(self.name, 0.0)
@ -275,7 +276,7 @@ class Tagger(TrainablePipe):
DOCS: https://spacy.io/api/tagger#get_loss
"""
validate_examples(examples, "Tagger.get_loss")
loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"])
loss_func = LegacySequenceCategoricalCrossentropy(names=self.labels, normalize=False, neg_prefix=self.cfg["neg_prefix"])
# Convert empty tag "" to missing value None so that both misaligned
# tokens and tokens with missing annotation have the default missing
# value None.