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