explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-02-21 22:10:34 +03:00
Code Issues Packages Projects Releases Wiki Activity

Move get_characters_loss

Browse Source
This commit is contained in:
Matthew Honnibal 2020-07-02 21:45:59 +02:00
parent 892f2552e0
commit cd2fa89d93
2 changed files with 15 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
spacy/_ml.py
View File

@ -988,3 +988,17 @@ def get_cossim_loss(yh, y, ignore_zeros=False):
losses[zero_indices] = 0
loss = losses.sum()
return loss, -d_yh
def get_characters_loss(ops, docs, prediction, nr_char=10):
target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
target_ids = target_ids.reshape((-1,))
target = ops.asarray(to_categorical(target_ids, nb_classes=256), dtype="f")
target = target.reshape((-1, 256*nr_char))
diff = prediction - target
loss = (diff**2).sum()
d_target = diff / float(prediction.shape[0])
return loss, d_target

13
spacy/cli/pretrain.py
View File

@ -19,7 +19,7 @@ from ..errors import Errors
from ..tokens import Doc
from ..attrs import ID, HEAD
from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
from .._ml import masked_language_model, get_cossim_loss
from .._ml import masked_language_model, get_cossim_loss, get_characters_loss
from .._ml import MultiSoftmax
from .. import util
from .train import _load_pretrained_tok2vec
@ -304,17 +304,6 @@ def make_docs(nlp, batch, min_length, max_length):
return docs, skip_count
def get_characters_loss(ops, docs, prediction, nr_char=10):
target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
target_ids = target_ids.reshape((-1,))
target = ops.asarray(to_categorical(target_ids, nb_classes=256), dtype="f")
target = target.reshape((-1, 256*nr_char))
diff = prediction - target
loss = (diff**2).sum()
d_target = diff / float(prediction.shape[0])
return loss, d_target
def get_vectors_loss(ops, docs, prediction, objective="L2"):
"""Compute a mean-squared error loss between the documents' vectors and
the prediction.