explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-02-09 16:10:33 +03:00
Code Issues Packages Projects Releases Wiki Activity

Remove unused methods

Browse Source
This commit is contained in:
Ines Montani 2020-07-28 16:50:02 +02:00
parent ba22111ff4
commit 2c7a32cf12
2 changed files with 0 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
spacy/pipeline/pipe.pyx
View File

@ -152,30 +152,11 @@ class Pipe:
return sgd return sgd
def set_output(self, nO): def set_output(self, nO):
# TODO: document this across components?
if self.model.has_dim("nO") is not False: if self.model.has_dim("nO") is not False:
self.model.set_dim("nO", nO) self.model.set_dim("nO", nO)
if self.model.has_ref("output_layer"): if self.model.has_ref("output_layer"):
self.model.get_ref("output_layer").set_dim("nO", nO) self.model.get_ref("output_layer").set_dim("nO", nO)
def get_gradients(self):
"""Get non-zero gradients of the model's parameters, as a dictionary
keyed by the parameter ID. The values are (weights, gradients) tuples.
"""
# TODO: How is this used?
gradients = {}
queue = [self.model]
seen = set()
for node in queue:
if node.id in seen:
continue
seen.add(node.id)
if hasattr(node, "_mem") and node._mem.gradient.any():
gradients[node.id] = [node._mem.weights, node._mem.gradient]
if hasattr(node, "_layers"):
queue.extend(node._layers)
return gradients
def use_params(self, params): def use_params(self, params):
"""Modify the pipe's model, to use the given parameter values. At the """Modify the pipe's model, to use the given parameter values. At the
end of the context, the original parameters are restored. end of the context, the original parameters are restored.

17
spacy/syntax/nn_parser.pyx
View File

@ -378,23 +378,6 @@ cdef class Parser:
del tutor del tutor
return losses return losses
def get_gradients(self):
"""Get non-zero gradients of the model's parameters, as a dictionary
keyed by the parameter ID. The values are (weights, gradients) tuples.
"""
gradients = {}
queue = [self.model]
seen = set()
for node in queue:
if node.id in seen:
continue
seen.add(node.id)
if hasattr(node, "_mem") and node._mem.gradient.any():
gradients[node.id] = [node._mem.weights, node._mem.gradient]
if hasattr(node, "_layers"):
queue.extend(node._layers)
return gradients
def get_batch_loss(self, states, golds, float[:, ::1] scores, losses): def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
cdef StateClass state cdef StateClass state
cdef Pool mem = Pool() cdef Pool mem = Pool()