def hook(settings, dict_file, **kwargs):
- settings.word_dict = dict_file
+19 settings.word_dict = dict_file
settings.input_types = [
integer_value_sequence(len(settings.word_dict)), integer_value(3)
]
@@ -320,6 +319,7 @@
settings.word_dict[w] for w in words if w in settings.word_dict
]
yield words, label
+
## for hierarchical sequence network
-def hook2(settings, dict_file, **kwargs):
+29def hook2(settings, dict_file, **kwargs):
settings.word_dict = dict_file
settings.input_types = [
integer_value_sub_sequence(len(settings.word_dict)),
@@ -628,24 +626,24 @@
15
16
17
-18hidden_dim = 8
-label_dim = 2
-
-speaker1 = data_layer(name="word1", size=dict_dim)
-speaker2 = data_layer(name="word2", size=dict_dim)
+18 def calrnn(y):
+ mem = memory(name='rnn_state_' + y.name, size=hidden_dim)
+ out = fc_layer(
+ input=[y, mem],
+ size=hidden_dim,
+ act=TanhActivation(),
+ bias_attr=True,
+ name='rnn_state_' + y.name)
+ return out
-emb1 = embedding_layer(input=speaker1, size=word_dim)
-emb2 = embedding_layer(input=speaker2, size=word_dim)
+ encoder1 = calrnn(x1)
+ encoder2 = calrnn(x2)
+ return [encoder1, encoder2]
-# This hierachical RNN is designed to be equivalent to the RNN in
-# sequence_nest_rnn_multi_unequalength_inputs.conf
+encoder1_rep, encoder2_rep = recurrent_group(
+ name="stepout", step=step, input=[emb1, emb2])
-def step(x1, x2):
- def calrnn(y):
- mem = memory(name='rnn_state_' + y.name, size=hidden_dim)
- out = fc_layer(
- input=[y, mem],
def outer_step(x1, x2):
- index = [0]
-
+40
def inner_step(ipt):
index[0] += 1
i = index[0]
@@ -730,6 +726,8 @@
step=outer_step,
input=[SubsequenceInput(emb1), SubsequenceInput(emb2)],
targetInlink=emb2)
+
+encoder1_last = last_seq(input=encoder1_rep)
在上面代码中，单层和双层序列的使用和示例2中的示例类似，区别是同时处理了两个输入。而对于双层序列，两个输入的子序列长度也并不相同。但是，我们使用了targetInlink参数设置了外层recurrent_group的输出格式。所以外层输出的序列形状，和emb2的序列形状一致。
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