Pass values for CNN maxout pieces option

spacy/pipeline.pyx

@@ -215,6 +215,7 @@ class TokenVectorEncoder(BaseThincComponent):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
         """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
@@ -286,9 +287,7 @@ class TokenVectorEncoder(BaseThincComponent):
         pipeline (list): The pipeline the model is part of.
         """
         if self.model is True:
-            self.model = self.Model(
-                pretrained_dims=self.vocab.vectors_length,
-                **self.cfg)
+            self.model = self.Model(**self.cfg)
 
 
 class NeuralTagger(BaseThincComponent):
@@ -297,6 +296,7 @@ class NeuralTagger(BaseThincComponent):
         self.vocab = vocab
         self.model = model
         self.cfg = dict(cfg)
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
         tags = self.predict(([doc], [doc.tensor]))
@@ -442,6 +442,7 @@ class NeuralTagger(BaseThincComponent):
         return self
 
     def to_disk(self, path, **exclude):
+        self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
         serialize = OrderedDict((
             ('vocab', lambda p: self.vocab.to_disk(p)),
             ('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
@@ -486,6 +487,7 @@ class NeuralLabeller(NeuralTagger):
         self.vocab = vocab
         self.model = model
         self.cfg = dict(cfg)
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     @property
     def labels(self):

spacy/syntax/nn_parser.pyx

@@ -309,6 +309,7 @@ cdef class Parser:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
         if 'pretrained_dims' not in cfg:
             cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+        cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():