return (keys, vals, lengths), None
@describe.on_data(_set_dimensions_if_needed,
lambda model, X, y: model.init_weights(model))
@describe.attributes(nI=Dimension("Input size"),
nF=Dimension("Number of features"),
nO=Dimension("Output size"),
nP=Dimension("Maxout pieces"),
W=Synapses("Weights matrix", lambda obj:
(obj.nF, obj.nO, obj.nP, obj.nI)),
b=Biases("Bias vector", lambda obj: (obj.nO, obj.nP)),
pad=Synapses("Pad", lambda obj:
(1, obj.nF, obj.nO, obj.nP),
lambda M, ops: ops.normal_init(M, 1.)),
d_W=Gradient("W"),
d_pad=Gradient("pad"),
d_b=Gradient("b"))
class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nP = nP
self.nI = nI
self.nF = nF
def begin_update(self, X, drop=0.):
Yf = self.ops.xp.dot(
X,
self.W.reshape((self.nF * self.nO * self.nP, self.nI)).T)
Yf = Yf.reshape((Yf.shape[0], self.nF, self.nO, self.nP))
@describe.on_data(_set_dimensions_if_needed,
lambda model, X, y: model.init_weights(model))
@describe.attributes(
nI=Dimension("Input size"),
nF=Dimension("Number of features"),
nO=Dimension("Output size"),
nP=Dimension("Maxout pieces"),
W=Synapses("Weights matrix", lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI)),
b=Biases("Bias vector", lambda obj: (obj.nO, obj.nP)),
pad=Synapses(
"Pad",
lambda obj: (1, obj.nF, obj.nO, obj.nP),
lambda M, ops: ops.normal_init(M, 1.0),
),
d_W=Gradient("W"),
d_pad=Gradient("pad"),
d_b=Gradient("b"),
)
class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nP = nP
self.nI = nI
self.nF = nF
def begin_update(self, X, drop=0.0):
Yf = self.ops.gemm(X,
self.W.reshape(
(self.nF * self.nO * self.nP, self.nI)),
return seqs, None
from thinc import describe
from thinc.describe import Dimension, Synapses, Gradient
from thinc.neural._lsuv import LSUVinit
@describe.on_data(LSUVinit)
@describe.attributes(
nM=Dimension("Vector dimensions"),
nO=Dimension("Size of output"),
W=Synapses("A projection matrix, to change vector dimensionality",
lambda obj: (obj.nO, obj.nM),
lambda W, ops: ops.xavier_uniform_init(W)),
d_W=Gradient("W"),
)
class SpacyVectors(Model):
ops = NumpyOps()
name = 'spacy-vectors'
def __init__(self, nlp, nO):
Model.__init__(self)
self._id_map = {0: 0}
self.nO = nO
self.nM = nlp.vocab.vectors_length
self.nlp = nlp
@property
def nV(self):
return len(self.nlp.vocab)
