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Make parser hidden shape consistent even if maxout==1

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Matthew Honnibal 2017-10-20 16:23:31 +02:00
parent b101736555
commit 3faf9189a2
1 changed files with 10 additions and 9 deletions
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19
spacy/_ml.py
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@ -110,17 +110,19 @@ def _preprocess_doc(docs, drop=0.):
nI=Dimension("Input size"), nI=Dimension("Input size"),
nF=Dimension("Number of features"), nF=Dimension("Number of features"),
nO=Dimension("Output size"), nO=Dimension("Output size"),
nP=Dimension("Maxout pieces"),
W=Synapses("Weights matrix", W=Synapses("Weights matrix",
lambda obj: (obj.nF, obj.nO, obj.nI)), lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI)),
b=Biases("Bias vector", b=Biases("Bias vector",
lambda obj: (obj.nO,)), lambda obj: (obj.nO, obj.nP)),
d_W=Gradient("W"), d_W=Gradient("W"),
d_b=Gradient("b") d_b=Gradient("b")
) )
class PrecomputableAffine(Model): class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, **kwargs): def __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
Model.__init__(self, **kwargs) Model.__init__(self, **kwargs)
self.nO = nO self.nO = nO
self.nP = nP
self.nI = nI self.nI = nI
self.nF = nF self.nF = nF
@ -128,16 +130,16 @@ class PrecomputableAffine(Model):
tensordot = self.ops.xp.tensordot tensordot = self.ops.xp.tensordot
ascontiguous = self.ops.xp.ascontiguousarray ascontiguous = self.ops.xp.ascontiguousarray
Yf = tensordot(X, self.W, axes=[[1], [2]]) Yf = tensordot(X, self.W, axes=[[1], [3]])
def backward(dY_ids, sgd=None): def backward(dY_ids, sgd=None):
dY, ids = dY_ids dY, ids = dY_ids
Xf = X[ids] Xf = X[ids]
dXf = tensordot(dY, self.W, axes=[[1], [1]]) dXf = tensordot(dY, self.W, axes=[[1,2], [1,2]])
dW = tensordot(dY, Xf, axes=[[0], [0]]) dW = tensordot(dY, Xf, axes=[[0], [0]])
# (o, p, f, i) --> (f, o, p, i)
self.d_W += dW.transpose((1, 0, 2)) self.d_W += dW.transpose((2, 0, 1, 3))
self.d_b += dY.sum(axis=0) self.d_b += dY.sum(axis=0)
if sgd is not None: if sgd is not None:
@ -167,11 +169,10 @@ class PrecomputableAffine(Model):
def predict(ids, tokvecs): def predict(ids, tokvecs):
hiddens = model(tokvecs) hiddens = model(tokvecs)
vector = model.ops.allocate((hiddens.shape[0], model.nO)) vector = model.ops.allocate((hiddens.shape[0], model.nO, model.nP))
model.ops.scatter_add(vector, ids, hiddens) model.ops.scatter_add(vector, ids, hiddens)
vector += model.b vector += model.b
if model.nP >= 2: if model.nP >= 2:
vector = vector.reshape((ids.shape[0], model.nO//model.nP, model.nP))
return model.ops.maxout(vector)[0] return model.ops.maxout(vector)[0]
else: else:
return vector * (vector >= 0) return vector * (vector >= 0)