def parse_old_rnn():
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 test():
data = conf_helps.data_layer(name="word", size=dict_dim)
embd = conf_helps.embedding_layer(input=data, size=word_dim)
conf_helps.recurrent_group(name="rnn", step=step, input=embd)
return str(parse_network(test))
def parse_old_rnn():
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 test():
data = conf_helps.data_layer(name="word", size=dict_dim)
embd = conf_helps.embedding_layer(input=data, size=word_dim)
conf_helps.recurrent_group(name="rnn", step=step, input=embd)
return str(parse_network(test))
def parse_old_rnn():
reset_parser()
def test():
data = conf_helps.data_layer(name="word", size=dict_dim)
label = conf_helps.data_layer(name="label", size=label_dim)
emb = conf_helps.embedding_layer(input=data, size=word_dim)
boot_layer = conf_helps.data_layer(name="boot", size=10)
boot_layer = conf_helps.fc_layer(
name='boot_fc', input=boot_layer, size=10)
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
out = conf_helps.recurrent_group(
name="rnn", step=step, input=[emb, data])
rep = conf_helps.last_seq(input=out)
prob = conf_helps.fc_layer(
size=label_dim,
input=rep,
act=conf_helps.SoftmaxActivation(),
bias_attr=True)
conf_helps.outputs(
conf_helps.classification_cost(
input=prob, label=label))
return str(parse_network(test))
def parse_old_rnn():
reset_parser()
def test():
data = conf_helps.data_layer(name="word", size=dict_dim)
label = conf_helps.data_layer(name="label", size=label_dim)
emb = conf_helps.embedding_layer(input=data, size=word_dim)
boot_layer = conf_helps.data_layer(name="boot", size=10)
boot_layer = conf_helps.fc_layer(name='boot_fc',
input=boot_layer,
size=10)
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
out = conf_helps.recurrent_group(name="rnn",
step=step,
input=[emb, data])
rep = conf_helps.last_seq(input=out)
prob = conf_helps.fc_layer(size=label_dim,
input=rep,
act=conf_helps.SoftmaxActivation(),
bias_attr=True)
conf_helps.outputs(
conf_helps.classification_cost(input=prob, label=label))
return str(parse_network(test))