def __init__(self, memblock, memdim=None, indim=None, attdim=None, **kw):
assert (indim is not None and memdim is not None and attdim is not None)
indim = memdim + indim
innerdim = attdim
super(LinearGateMemAddr, self).__init__(memblock, **kw)
self.W = param((indim, innerdim), name="attention_ff").uniform()
self.U = param((innerdim,), name="attention_agg").uniform()
def makeparams(self):
if not self.noinput:
self.w = param((self.indim, self.innerdim),
name="w").init(self.paraminit)
self.wm = param((self.indim, self.innerdim),
name="wm").init(self.paraminit)
self.whf = param((self.indim, self.innerdim),
name="whf").init(self.paraminit)
else:
self.w, self.wm, self.whf = 0, 0, 0
self.u = param((self.innerdim, self.innerdim),
name="u").init(self.paraminit)
self.um = param((self.innerdim, self.innerdim),
name="um").init(self.paraminit)
self.uhf = param((self.innerdim, self.innerdim),
name="uhf").init(self.paraminit)
if not self.nobias:
self.b = param((self.innerdim, ), name="b").init(self.biasinit)
if self._init_carry_bias > 0:
amnt = default_init_carry_gate_bias\
if self._init_carry_bias is True else self._init_carry_bias
self.bm = param((self.innerdim, ), name="bm").constant(amnt)
else:
self.bm = param((self.innerdim, ),
name="bm").init(self.biasinit)
self.bhf = param((self.innerdim, ), name="bhf").init(self.biasinit)
else:
self.b, self.bm, self.bhf = 0, 0, 0
def __init__(self, vocsize, embdim, seed=None, **kw):
super(EmbeddingVAE, self).__init__(**kw)
self.W_m = param((vocsize, embdim)).glorotuniform()
self.W_s = param((vocsize, embdim)).glorotuniform()
self.O = param((embdim, vocsize)).glorotuniform()
self.embdim = embdim
self.seed = seed
def __init__(self,
indim,
dim,
w_init="glorotuniform",
b_init="uniform",
init_carry_bias=True,
dropout=False,
carry_activation=T.nnet.sigmoid,
**kw):
""" init_carry_bias sets carry gate bias to negative value to encourage carry behavior (see Highway Networks paper) """
super(ForwardHighway, self).__init__(indim,
dim,
w_init=w_init,
b_init=b_init,
dropout=dropout,
**kw)
self.carry_activation = carry_activation
self.W_t = None
if indim != dim:
self.W_t = param((indim, dim), name="W_t").init(self.w_init)
self.W_c = param((self.indim, self.dim),
name="carry_W").init(self.w_init)
if init_carry_bias > 0:
amnt = default_carry_bias if init_carry_bias is True else init_carry_bias
self.b_c = param((self.dim, ), name="carry_b").constant(-amnt)
else:
self.b_c = param((self.dim, ), name="carry_b").init(self.b_init)
def __init__(self, memblock, memdim=None, indim=None, attdim=None, **kw):
assert (indim is not None and memdim is not None and attdim is not None)
indim = memdim + indim
innerdim = attdim
super(LinearGateMemAddr, self).__init__(memblock, **kw)
self.W = param((indim, innerdim), name="attention_ff").uniform()
self.U = param((innerdim,), name="attention_agg").uniform()
def makeparams(self):
if not self.noinput:
self.w = param((self.indim, self.innerdim),
name="w").init(self.paraminit)
else:
self.w = 0
self.u = param((self.innerdim, self.innerdim),
name="u").init(self.paraminit)
if self.nobias is False:
self.b = param((self.innerdim, ), name="b").init(self.biasinit)
else:
self.b = 0
def makeparams(self):
super(IFGRU, self).makeparams()
self.uif = param((self.innerdim, self.indim),
name="uif").init(self.paraminit)
if not self.noinput:
self.wif = param((self.indim, self.indim),
name="wif").init(self.paraminit)
else:
self.wif = 0
if not self.nobias:
self.bif = param((self.indim, ), name="bif").init(self.biasinit)
else:
self.bif = 0
def __init__(self, encdim, invocsize, outvocsize, innerdim, seqlen, **kw):
super(idx2seqTheano, self).__init__(**kw)
self.encdim = encdim
self.invocsize = invocsize
self.outvocsize = outvocsize
self.innerdim = innerdim
self.seqlen = seqlen
self.wordemb = param((invocsize, encdim)).uniform()
self.idxtovec = Val(np.eye(outvocsize, outvocsize))
self.rnu_w = param((encdim + outvocsize, innerdim)).uniform()
self.rnu_u = param((innerdim, innerdim)).uniform()
self.outpf = theano.tensor.tanh
self.olin = param((innerdim, outvocsize)).uniform()
self.ownparams = [self.wordemb, self.rnu_u, self.rnu_w, self.olin]
def __init__(self, encdim, invocsize, outvocsize, innerdim, seqlen, **kw):
super(idx2seqTheano, self).__init__(**kw)
self.encdim = encdim
self.invocsize = invocsize
self.outvocsize = outvocsize
self.innerdim = innerdim
self.seqlen = seqlen
self.wordemb = param((invocsize, encdim)).uniform()
self.idxtovec = Val(np.eye(outvocsize, outvocsize))
self.rnu_w = param((encdim + outvocsize, innerdim)).uniform()
self.rnu_u = param((innerdim, innerdim)).uniform()
self.outpf = theano.tensor.tanh
self.olin = param((innerdim, outvocsize)).uniform()
self.ownparams = [self.wordemb, self.rnu_u, self.rnu_w, self.olin]
def __init__(self,
indim,
dim,
w_init="glorotuniform",
b_init="uniform",
dropout=False,
**kw):
super(Linear, self).__init__(**kw)
self.indim = indim
self.dim = dim
self.w_init = w_init
self.b_init = b_init
self.W = param((self.indim, self.dim), name="linear_W").init(w_init)
self.b = param((self.dim, ), name="linear_b").init(b_init)
self.dropout = Dropout(dropout)
def __init__(self,
indim=None,
dim=None,
value=None,
normalize=False,
trainfrac=1.0,
init=None,
maskid=None,
**kw):
super(VectorEmbed, self).__init__(indim,
dim,
normalize=normalize,
trainfrac=trainfrac,
**kw)
self.maskid = maskid
if value is None:
self.W = param((indim, dim), lrmul=self.trainfrac, name="embedder")
if init == "zero":
self.W = self.W.constant(0.0)
elif init in ["glorot", None]:
self.W = self.W.glorotuniform()
elif init == "uniform":
self.W = self.W.uniform()
elif value is False:
self.W = None # no initialization
else:
self.setvalue(value)
self.indim, self.outdim = value.shape
if self.normalize:
self.W = self.W.normalize(axis=1)
# assertions
assert (self.W is None
or self.W.d.get_value().shape == (self.indim, self.outdim))
def __init__(self,
indim=1000,
dim=50,
value=None,
normalize=False,
trainfrac=1.0,
**kw):
super(VectorEmbed, self).__init__(indim, dim, **kw)
self.dim = dim
self.indim = indim
self.trainfrac = trainfrac
if value is None:
self.W = param((indim, dim), lrmul=self.trainfrac,
name="embedder").glorotuniform()
else:
if trainfrac == 0.0:
self.W = Val(value, name="embedder_val")
else:
self.W = Parameter(value,
lrmul=self.trainfrac,
name="embedder")
if normalize:
self.W = self.W.normalize(axis=1)
# assertions
assert (self.W.d.get_value().shape == (self.indim, self.dim))
def setUp(self):
dim = 50
self.vocabsize = 2000
data = np.arange(0, self.vocabsize).astype("int32")
self.O = param((dim, self.vocabsize)).uniform()
self.W = VectorEmbed(indim=self.vocabsize, dim=50)
self.out = stack(self.W, asblock(lambda x: T.dot(self.O, x)),
Softmax())(Input(ndim=1, dtype="int32"))
def setUp(self):
dim=50
self.vocabsize=2000
data = np.arange(0, self.vocabsize).astype("int32")
self.O = param((dim, self.vocabsize)).uniform()
self.W = VectorEmbed(indim=self.vocabsize, dim=50)
self.out = stack(self.W,
asblock(lambda x: T.dot(self.O, x)),
Softmax())(Input(ndim=1, dtype="int32"))
def __init__(self,
memblock,
memdim=None,
indim=None,
attdim=None,
**kw): # indim should be mem_dim, innerdim should be crit_dim
assert (indim is not None and memdim is not None
) # can not specify separate attention dimensions
super(GeneralDotMemAddr, self).__init__(memblock, **kw)
self.W = param((memdim, indim), name="addressing").uniform()
def __init__(self, **kw):
super(SimpleSeq2Sca, self).__init__(**kw)
self.enc.all_outputs().with_mask()
if "innerdim" in kw:
kwindim = kw["innerdim"]
if issequence(kwindim):
summdim = kwindim[-1]
else:
summdim = kwindim
else:
summdim = 100
self.summ = param((summdim, ), name="summarize").uniform()
def __init__(self,
indim,
dim,
value=None,
init="glorotuniform",
dropout=False,
**kw):
super(MatDot, self).__init__(**kw)
self.indim = indim
self.dim = dim
if value is None:
self.W = param((self.indim, self.dim), name="matdot").init(init)
else:
self.W = value
self.dropout = Dropout(dropout)
def __init__(self, outpactivation=T.tanh, param_init_states=False, **kw):
self.outpactivation = outpactivation
super(RNU, self).__init__(**kw)
self.initstateparams = None
if param_init_states:
self.initstateparams = []
for spec in self.get_statespec():
if spec[0] == "state":
initstateparam = param(spec[1],
name="init_state").uniform()
self.initstateparams.append(initstateparam)
else:
self.initstateparams.append(None)
self.param_init_states = param_init_states
self.makeparams()
def __init__(self, indim=1000, dim=50, value=None, normalize=False, trainfrac=1.0, **kw):
super(VectorEmbed, self).__init__(indim, dim, **kw)
self.dim = dim
self.indim = indim
self.trainfrac = trainfrac
if value is None:
self.W = param((indim, dim), lrmul=self.trainfrac, name="embedder").glorotuniform()
else:
if trainfrac == 0.0:
self.W = Val(value, name="embedder_val")
else:
self.W = Parameter(value, lrmul=self.trainfrac, name="embedder")
if normalize:
self.W = self.W.normalize(axis=1)
# assertions
assert(self.W.d.get_value().shape == (self.indim, self.dim))
def __init__(self,
indim=50,
outdim=50,
window=5,
border_mode="half",
stride=1,
filter_flip=True,
**kw):
super(Conv1D, self).__init__(**kw)
if isinstance(border_mode, tuple):
(border_mode, ) = border_mode
if isinstance(border_mode, int):
border_mode = (border_mode, 0)
self.border_mode = border_mode
self.stride = stride
self.filter_flip = filter_flip
self.filter_shape = (outdim, indim, window, 1)
self.filter = param(self.filter_shape, name="conv_w").glorotuniform()
self.maskfilter_shape = (1, 1, window, 1)
self.maskfilter = Val(np.ones(self.maskfilter_shape, dtype="float32"))
def initparams(self):
for n, _ in self.rnuparams.items(): # delete existing params
if hasattr(self, n):
delattr(self, n)
self.rnuparams = {}
for paramname in self.paramnames:
shape = None
if isinstance(paramname, tuple):
shape = paramname[1]
paramname = paramname[0]
if paramname[0] == "b" and self.nobias is True:
setattr(self, paramname, 0)
continue
if shape is None:
if paramname[0] == "b" or paramname[0] == "p": # bias or peepholes, internal weights
shape = (self.innerdim,)
elif paramname[0] == "w": #input processing matrices
shape = (self.indim, self.innerdim)
else: # internal recurrent matrices
shape = (self.innerdim, self.innerdim)
self.rnuparams[paramname] = param(shape, name=paramname).init(self.paraminit)
setattr(self, paramname, self.rnuparams[paramname])
def initparams(self):
for n, _ in self.rnuparams.items(): # delete existing params
if hasattr(self, n):
delattr(self, n)
self.rnuparams = {}
for paramname in self.paramnames:
shape = None
if isinstance(paramname, tuple):
shape = paramname[1]
paramname = paramname[0]
if paramname[0] == "b" and self.nobias is True:
setattr(self, paramname, 0)
continue
if shape is None:
if paramname[0] == "b" or paramname[
0] == "p": # bias or peepholes, internal weights
shape = (self.innerdim, )
elif paramname[0] == "w": #input processing matrices
shape = (self.indim, self.innerdim)
else: # internal recurrent matrices
shape = (self.innerdim, self.innerdim)
self.rnuparams[paramname] = param(shape, name=paramname).init(
self.paraminit)
setattr(self, paramname, self.rnuparams[paramname])
def __init__(self, memblock, memdim=None, indim=None, attdim=None, **kw): # indim should be mem_dim, innerdim should be crit_dim
assert(indim is not None and memdim is not None) # can not specify separate attention dimensions
super(GeneralDotMemAddr, self).__init__(memblock, **kw)
self.W = param((memdim, indim), name="addressing").uniform()
from teafacto.core.base import tensorops as T, Val, param import numpy as np import sys x = Val(np.random.random((10, 10))) # y = Val(np.random.random((10,10))) y = param((10, 10), name="y").uniform() w = param((10, 10), name="w").uniform() # z = T.dot(x, y) z = x + y u = z * w s = T.nnet.sigmoid s2 = T.nnet.sigmoid print s == s2 sys.exit() print z.allparams print T.dot print z.ndim print z.dimswap zd = z.dimswap(1, 0) print z.dimswap(0, 1).allparams print y.dimswap(0, 1).allparams print T.nnet.conv.conv2d print u.norm(2).allparams print u.dimswap(0, 1).allparams print T.nnet.softmax(z).allparams zs = T.nnet.sigmoid(z) zs = zs + x zs.autobuild() zs.autobuild()
def __init__(self, indim, aggdim, **kw):
super(LinearDistance, self).__init__(**kw)
self.W = param((indim, aggdim), name="attention_ff").uniform()
self.U = param((aggdim,), name="attention_agg").uniform()
def __init__(self, embdim, numents, numrels, **kw):
self.A = VectorEmbed(indim=numents, dim=embdim)
self.R = param((numrels, embdim, embdim),
name="rel_embed").glorotuniform()
self.scorer = DotDistance()
super(Rescal, self).__init__(**kw)
def __init__(self,
inpvocsize=None,
inpembdim=None,
inpemb=None,
inpencinnerdim=None,
bidir=False,
maskid=None,
dropout=False,
rnu=GRU,
inpencoder=None,
memvocsize=None,
memembdim=None,
memembmat=None,
memencinnerdim=None,
memencoder=None,
inp_att_dist=CosineDistance(),
mem_att_dist=CosineDistance(),
inp_attention=None,
mem_attention=None,
coredims=None,
corernu=GRU,
core=None,
explicit_interface=False,
scalaraggdim=None,
write_value_dim=None,
nsteps=100,
posvecdim=None,
mem_pos_repr=None,
inp_pos_repr=None,
inp_addr_extractor=None,
mem_addr_extractor=None,
write_addr_extractor=None,
write_addr_generator=None,
write_addr_dist=CosineDistance(),
write_value_generator=None,
write_value_extractor=None,
mem_erase_generator=None,
mem_change_generator=None,
memsampler=None,
memsamplemethod=None,
memsampletemp=0.3,
**kw):
# INPUT ENCODING
if inpencoder is None:
inpencoder = SeqEncoder.RNN(indim=inpvocsize,
inpembdim=inpembdim,
inpemb=inpemb,
innerdim=inpencinnerdim,
bidir=bidir,
maskid=maskid,
dropout_in=dropout,
dropout_h=dropout,
rnu=rnu).all_outputs()
lastinpdim = inpencinnerdim if not issequence(
inpencinnerdim) else inpencinnerdim[-1]
else:
lastinpdim = inpencoder.block.layers[-1].innerdim
# MEMORY ENCODING
if memembmat is None:
memembmat = param((memvocsize, memembdim),
name="memembmat").glorotuniform()
if memencoder is None:
memencoder = SeqEncoder.RNN(inpemb=False,
innerdim=memencinnerdim,
bidir=bidir,
dropout_in=dropout,
dropout_h=dropout,
rnu=rnu,
inpembdim=memembdim).all_outputs()
lastmemdim = memencinnerdim if not issequence(
memencinnerdim) else memencinnerdim[-1]
else:
lastmemdim = memencoder.block.layers[-1].innerdim
# POSITION VECTORS
if posvecdim is not None and inp_pos_repr is None:
inp_pos_repr = RNNWithoutInput(posvecdim, dropout=dropout)
if posvecdim is not None and mem_pos_repr is None:
mem_pos_repr = RNNWithoutInput(posvecdim, dropout=dropout)
xtra_dim = posvecdim if posvecdim is not None else 0
# CORE RNN - THE THINKER
if core is None:
corelayers, _ = MakeRNU.fromdims(
[lastinpdim + lastmemdim + xtra_dim * 2] + coredims,
rnu=corernu,
dropout_in=dropout,
dropout_h=dropout,
param_init_states=True)
core = RecStack(*corelayers)
lastcoredim = core.get_statespec()[-1][0][1][0]
# ATTENTIONS
if mem_attention is None:
mem_attention = Attention(mem_att_dist)
if inp_attention is None:
inp_attention = Attention(inp_att_dist)
if write_addr_generator is None:
write_addr_generator = AttGen(write_addr_dist)
# WRITE VALUE
if write_value_generator is None:
write_value_generator = WriteValGenerator(write_value_dim,
memvocsize,
dropout=dropout)
# MEMORY SAMPLER
if memsampler is not None:
assert (memsamplemethod is None)
if memsamplemethod is not None:
assert (memsampler is None)
memsampler = GumbelSoftmax(temperature=memsampletemp)
################ STATE INTERFACES #################
if not explicit_interface:
if inp_addr_extractor is None:
inp_addr_extractor = Forward(lastcoredim,
lastinpdim + xtra_dim,
dropout=dropout)
if mem_addr_extractor is None:
inp_addr_extractor = Forward(lastcoredim,
lastmemdim + xtra_dim,
dropout=dropout)
# WRITE INTERFACE
if write_addr_extractor is None:
write_addr_extractor = Forward(lastcoredim,
lastmemdim + xtra_dim,
dropout=dropout)
if write_value_extractor is None:
write_value_extractor = Forward(lastcoredim,
write_value_dim,
dropout=dropout)
# MEM UPDATE INTERFACE
if mem_erase_generator is None:
mem_erase_generator = StateToScalar(lastcoredim, scalaraggdim)
if mem_change_generator is None:
mem_change_generator = StateToScalar(lastcoredim, scalaraggdim)
else:
inp_addr_extractor, mem_addr_extractor, write_addr_extractor, \
write_value_extractor, mem_erase_generator, mem_change_generator = \
make_vector_slicers(0, lastinpdim + xtra_dim, lastmemdim + xtra_dim,
lastmemdim + xtra_dim, write_value_dim, 1, 1)
super(SimpleBulkNN,
self).__init__(inpencoder=inpencoder,
memembmat=memembmat,
memencoder=memencoder,
inp_attention=inp_attention,
mem_attention=mem_attention,
core=core,
memsampler=memsampler,
nsteps=nsteps,
inp_addr_extractor=inp_addr_extractor,
mem_addr_extractor=mem_addr_extractor,
write_addr_extractor=write_addr_extractor,
write_addr_generator=write_addr_generator,
mem_erase_generator=mem_erase_generator,
mem_change_generator=mem_change_generator,
write_value_generator=write_value_generator,
write_value_extractor=write_value_extractor,
inp_pos_repr=inp_pos_repr,
mem_pos_repr=mem_pos_repr,
**kw)
def __init__(self, indim=100, inpembdim=50, outdim=100, **kw):
self.E = param((indim, inpembdim), name="emb").uniform()
self.W = param((inpembdim, outdim), name="W").uniform()
super(StupidAtis, self).__init__(**kw)
def __init__(self, dim=500, sharpness=1, **kw):
super(SpatialEmb, self).__init__(**kw)
self.xes = param((dim, ), name="xes").constant(5000.0)
self.yes = param((dim, ), name="yes").constant(5000.0)
self.divmul = param((), name="divmul").constant(1.0)
self.e = sharpness
def __init__(self, indim=100, inpembdim=50, outdim=100, **kw):
self.E = param((indim, inpembdim), name="emb").uniform()
self.W = param((inpembdim, outdim), name="W").uniform()
super(StupidAtis, self).__init__(**kw)
def __init__(self, indim=100, inpembdim=50, outdim=100, **kw):
super(StupidAtisNative, self).__init__(**kw)
self.E = self.add_param(param((indim, inpembdim), name="emb").uniform())
self.W = self.add_param(param((inpembdim, outdim), name="W").uniform())
def test_params_propagated_through_rnu(self):
O = param((self.dim, self.dim), name="bigo").uniform()
i = Input(ndim=2, dtype="int32")
x = O[i, :]
out = self.rnu(x)
self.assertIn(O, out.allparams)
def __init__(self, dim=500, sharpness=1, **kw):
super(SpatialEmb, self).__init__(**kw)
self.xes = param((dim,), name="xes").constant(5000.0)
self.yes = param((dim,), name="yes").constant(5000.0)
self.divmul = param((), name="divmul").constant(1.0)
self.e = sharpness
def __init__(self, indim, dim, w_init="uniform", b_init="uniform", **kw):
super(Linear, self).__init__(**kw)
self.indim = indim
self.dim = dim
self.W = param((self.indim, self.dim)).init(w_init)
self.b = param((self.dim, )).init(b_init)
def __init__(self, indim, dim, init="uniform", **kw):
super(MatDot, self).__init__(**kw)
self.indim = indim
self.dim = dim
self.W = param((self.indim, self.dim), name="matdot").init(init)
def __init__(self, indim, dim, w_init="uniform", b_init="uniform", **kw):
super(Linear, self).__init__(**kw)
self.indim = indim
self.dim = dim
self.W = param((self.indim, self.dim)).init(w_init)
self.b = param((self.dim,)).init(b_init)
def __init__(self, **kw):
super(LinearGateAttentionGenerator, self).__init__(**kw)
self.W = param((self.indim, self.attdim), name="attention_ff").uniform()
self.U = param((self.attdim,), name="attention_agg").uniform()
def __init__(self, memblock, memdim=None, indim=None, attdim=None, **kw):
super(TransDotMemAddr, self).__init__(memblock, **kw)
self.W = param((memdim, attdim), name="addr_memtrans").uniform()
self.U = param((indim, attdim), name="addr_crittrans").uniform()
def __init__(self, memblock, memdim=None, indim=None, attdim=None, **kw):
super(TransDotMemAddr, self).__init__(memblock, **kw)
self.W = param((memdim, attdim), name="addr_memtrans").uniform()
self.U = param((indim, attdim), name="addr_crittrans").uniform()
def makeparams(self):
self.usf = param((self.innerdim, self.innerdim),
name="usf").init(self.paraminit)
self.umf = param((self.innerdim, self.innerdim),
name="umf").init(self.paraminit)
self.u = param((self.innerdim, self.innerdim),
name="u").init(self.paraminit)
self.uug = param((self.innerdim, self.innerdim),
name="uug").init(self.paraminit)
self.uwf = param((self.innerdim, self.innerdim),
name="uwf").init(self.paraminit)
self.uma = param((self.innerdim, self.innerdim),
name="uma").init(self.paraminit)
self.uma2 = param((self.innerdim, self.innerdim),
name="uma2").init(self.paraminit)
self.uif = param((self.innerdim, self.indim),
name="uif").init(self.paraminit)
self.w = param((self.indim, self.innerdim),
name="w").init(self.paraminit)
self.wsf = param((self.indim, self.innerdim),
name="wsf").init(self.paraminit)
self.wmf = param((self.indim, self.innerdim),
name="wmf").init(self.paraminit)
self.wug = param((self.indim, self.innerdim),
name="wug").init(self.paraminit)
self.wwf = param((self.indim, self.innerdim),
name="wwf").init(self.paraminit)
self.wma = param((self.indim, self.innerdim),
name="wma").init(self.paraminit)
self.wma2 = param((self.indim, self.innerdim),
name="wma2").init(self.paraminit)
self.wif = param((self.indim, self.indim),
name="wif").init(self.paraminit)
self.m = param((self.innerdim, self.innerdim),
name="m").init(self.paraminit)
self.msf = param((self.innerdim, self.innerdim),
name="msf").init(self.paraminit)
self.mmf = param((self.innerdim, self.innerdim),
name="mmf").init(self.paraminit)
self.mug = param((self.innerdim, self.innerdim),
name="mug").init(self.paraminit)
self.mwf = param((self.innerdim, self.innerdim),
name="mwf").init(self.paraminit)
self.mma = param((self.innerdim, self.innerdim),
name="mma").init(self.paraminit)
self.mma2 = param((self.innerdim, self.innerdim),
name="mma2").init(self.paraminit)
self.mif = param((self.innerdim, self.indim),
name="mif").init(self.paraminit)
if not self.nobias:
self.b = param((self.innerdim, ), name="b").init(self.biasinit)
self.bsf = param((self.innerdim, ), name="bsf").init(self.biasinit)
self.bmf = param((self.innerdim, ), name="bmf").init(self.biasinit)
self.bug = param((self.innerdim, ), name="bug").init(self.biasinit)
self.bwf = param((self.innerdim, ), name="bwf").init(self.biasinit)
self.bma = param((self.innerdim, ), name="bma").init(self.biasinit)
self.bma2 = param((self.innerdim, ),
name="bma2").init(self.biasinit)
self.bif = param((self.indim, ), name="bif").init(self.biasinit)
else:
self.b, self.bsf, self.bmf, self.bug, self.bwf, self.bma, self.bma2, self.bif = \
0, 0, 0, 0, 0, 0, 0, 0
def __init__(self, embdim, numents, numrels, **kw):
self.A = VectorEmbed(indim=numents, dim=embdim)
self.R = param((numrels, embdim, embdim), name="rel_embed").glorotuniform()
self.scorer = DotDistance()
super(Rescal, self).__init__(**kw)
def __init__(self, indim=100, inpembdim=50, outdim=100, **kw):
super(StupidAtisNative, self).__init__(**kw)
self.E = self.add_param(
param((indim, inpembdim), name="emb").uniform())
self.W = self.add_param(param((inpembdim, outdim), name="W").uniform())
def __init__(self, indim, aggdim, **kw):
super(LinearDistance, self).__init__(**kw)
self.W = param((indim, aggdim), name="attention_ff").uniform()
self.U = param((aggdim, ), name="attention_agg").uniform()
def __init__(self, dim, outdim, **kw):
super(StateToScalar, self).__init__(**kw)
self.block = Forward(dim, outdim)
self.agg = param((outdim, ), name="scalartostate_agg").uniform()
def __init__(self, ldim, rdim, **kw):
super(GenDotDistance, self).__init__(**kw)
self.W = param((rdim, ldim), name="gendotdist").glorotuniform()
def __init__(self, ldim, rdim, **kw):
super(GenDotDistance, self).__init__(**kw)
self.W = param((rdim, ldim), name="gendotdist").glorotuniform()
from teafacto.core.base import tensorops as T, Val, param import numpy as np import sys x = Val(np.random.random((10, 10))) #y = Val(np.random.random((10,10))) y = param((10, 10), name="y").uniform() w = param((10, 10), name="w").uniform() #z = T.dot(x, y) z = (x + y) u = z * w s = T.nnet.sigmoid s2 = T.nnet.sigmoid print s == s2 sys.exit() print z.allparams print T.dot print z.ndim print z.dimswap zd = z.dimswap(1, 0) print z.dimswap(0, 1).allparams print y.dimswap(0, 1).allparams print T.nnet.conv.conv2d print u.norm(2).allparams print u.dimswap(0, 1).allparams print T.nnet.softmax(z).allparams zs = T.nnet.sigmoid(z) zs = zs + x zs.autobuild() zs.autobuild()
def __init__(self, indim, dim, init="uniform", **kw):
super(MatDot, self).__init__(**kw)
self.indim = indim
self.dim = dim
self.W = param((self.indim, self.dim), name="matdot").init(init)
def __init__(self, dim, **kw):
self.dim = dim
self.w = param((dim,), name="weights").uniform()
self.b = param((1,), name="bias").uniform()
super(BinaryLinear, self).__init__(**kw)
