More serialization fixes. Still broken
@@ -166,6 +166,8 @@ class TokenVectorEncoder(object):
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
+ if self.model is True:
+ self.model = self.Model()
deserialize = OrderedDict((
('model', lambda b: util.model_from_bytes(self.model, b)),
('vocab', lambda b: self.vocab.from_bytes(b))
@@ -278,9 +280,14 @@ class NeuralTagger(object):
vocab.morphology = Morphology(vocab.strings, new_tag_map,
vocab.morphology.lemmatizer)
token_vector_width = pipeline[0].model.nO
- self.model = with_flatten(
+ if self.model is True:
+ self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
+
+ @classmethod
+ def Model(cls, n_tags, token_vector_width):
+ return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
- Softmax(self.vocab.morphology.n_tags, token_vector_width)))
+ Softmax(n_tags, token_vector_width)))
def use_params(self, params):
with self.model.use_params(params):
@@ -294,11 +301,16 @@ class NeuralTagger(object):
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
+ def load_model(b):
+ if self.model is True:
+ token_vector_width = util.env_opt('token_vector_width', 128)
+ self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
+ util.model_from_bytes(self.model, b)
deserialize = {
- 'model': lambda b: util.model_from_bytes(self.model, b),
- 'vocab': lambda b: self.vocab.from_bytes(b)
+ 'vocab': lambda b: self.vocab.from_bytes(b),
+ 'model': lambda b: load_model(b)
}
- util.from_bytes(deserialize, exclude)
+ util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
@@ -336,9 +348,14 @@ class NeuralLabeller(NeuralTagger):
if dep not in self.labels:
self.labels[dep] = len(self.labels)
token_vector_width = pipeline[0].model.nO
- self.model = with_flatten(
+ if self.model is True:
+ self.model = self.Model(len(self.labels), token_vector_width)
+
+ @classmethod
+ def Model(cls, n_tags, token_vector_width):
+ return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
- Softmax(len(self.labels), token_vector_width)))
+ Softmax(n_tags, token_vector_width)))
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
@@ -412,7 +429,6 @@ cdef class NeuralEntityRecognizer(NeuralParser):
return (NeuralEntityRecognizer, (self.vocab, self.moves, self.model), None, None)
-
cdef class BeamDependencyParser(BeamParser):
TransitionSystem = ArcEager
