def step(y):
mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
out = conf_helps.fc_layer(input=[y, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
def step(y):
mem = conf_helps.memory(name="rnn_state", size=hidden_dim)
out = conf_helps.fc_layer(
input=[y, mem],
size=hidden_dim,
act=activation.Tanh(),
bias_attr=True,
name="rnn_state")
return out
def step(y, wid):
z = conf_helps.embedding_layer(input=wid, size=word_dim)
mem = conf_helps.memory(name="rnn_state",
size=hidden_dim,
boot_layer=boot_layer)
out = conf_helps.fc_layer(input=[y, z, mem],
size=hidden_dim,
act=conf_helps.TanhActivation(),
bias_attr=True,
name="rnn_state")
return out
def step(y, wid):
z = conf_helps.embedding_layer(input=wid, size=word_dim)
mem = conf_helps.memory(
name="rnn_state",
size=hidden_dim,
boot_layer=boot_layer)
out = conf_helps.fc_layer(
input=[y, z, mem],
size=hidden_dim,
act=conf_helps.TanhActivation(),
bias_attr=True,
name="rnn_state")
return out
def to_proto_impl(self, **kwargs):
args = dict()
for each in kwargs:
args[each] = kwargs[each]
for each in self.__kwargs__:
args[each] = self.__kwargs__[each]
if self.__boot_layer_name__ is not None:
args['boot_layer'] = self.__context__[self.__boot_layer_name__]
size = args.get('size', None)
if size is not None:
if callable(size):
real_size = size()
else:
real_size = size
args['size'] = real_size
return conf_helps.memory(name=self.name, **args)
def to_proto_impl(self, **kwargs):
args = dict()
for each in kwargs:
args[each] = kwargs[each]
for each in self.__kwargs__:
args[each] = self.__kwargs__[each]
if self.__boot_layer_name__ is not None:
args['boot_layer'] = self.__context__[self.__boot_layer_name__]
size = args.get('size', None)
if size is not None:
if callable(size):
real_size = size()
else:
real_size = size
args['size'] = real_size
return conf_helps.memory(name=self.name, **args)