def to_link(self):
args = self.to_chainer_args()
del args["use_weightnorm"]
# mean
if hasattr(self, "_initialW_mean"):
args["initialW"] = getattr(self, "_initialW_mean")
if self.use_weightnorm:
layer_mean = weightnorm.Linear(**args)
else:
layer_mean = chainer.links.Linear(**args)
# ln_var
if hasattr(self, "_initialW_ln_var"):
args["initialW"] = getattr(self, "_initialW_ln_var")
if self.use_weightnorm:
layer_ln_var = weightnorm.Linear(**args)
else:
layer_ln_var = chainer.links.Linear(**args)
return links.Gaussian(layer_mean, layer_ln_var)
def to_link(self):
args = self.to_chainer_args()
del args["use_weightnorm"]
if self.use_weightnorm:
if hasattr(self, "_initialW"):
args["initialV"] = self._initialW
return weightnorm.Linear(**args)
if hasattr(self, "_initialW"):
args["initialW"] = self._initialW
return chainer.links.Linear(**args)
def to_link(self):
link = links.Merge()
for i in xrange(self.num_inputs):
args = self.to_chainer_args()
del args["use_weightnorm"]
del args["num_inputs"]
if self.use_weightnorm:
if hasattr(self, "_initialW"):
args["initialV"] = self._initialW
merge_layer = weightnorm.Linear(None, **args)
else:
if hasattr(self, "_initialW_%d" % i):
args["initialW"] = getattr(self, "_initialW_%d" % i)
merge_layer = chainer.links.Linear(None, **args)
link.append_layer(merge_layer)
return link
