Use PretrainableMaxouts
@@ -32,7 +32,7 @@ from preshed.maps cimport map_get
from thinc.api import layerize, chain
from thinc.neural import Model, Maxout
-from .._ml import PrecomputableAffine
+from .._ml import PrecomputableAffine, PrecomputableMaxouts
from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
@@ -93,7 +93,7 @@ def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, upper_model, low
for i in range(len(states)):
for j, tok_i in enumerate(adjusted_ids[i]):
if tok_i >= 0:
- features[i] += cached[tok_i, j]
+ features[i] += cached[j, tok_i]
scores, bp_scores = upper_model.begin_update(features, drop=drop)
scores = upper_model.ops.relu(scores)
@@ -222,7 +222,7 @@ cdef class Parser:
nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
upper = chain(Maxout(width, width), Maxout(self.moves.n_moves, width))
- lower = PrecomputableAffine(width, nF=nr_context_tokens, nI=width)
+ lower = PrecomputableMaxouts(width, nF=nr_context_tokens, nI=width)
return upper, lower
def __call__(self, Doc tokens):
