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develop/doc_cn/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html
.../doc_cn/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html
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...
@@ -301,8 +301,7 @@
...
@@ -301,8 +301,7 @@
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17
17
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18
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</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span><span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
hook
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
dict_file
</span><span
class=
"p"
>
,
</span>
<span
class=
"o"
>
**
</span><span
class=
"n"
>
kwargs
</span><span
class=
"p"
>
):
</span>
19
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
dict_file
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
dict_file
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
input_types
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"p"
>
[
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
input_types
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"p"
>
[
</span>
<span
class=
"n"
>
integer_value_sequence
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
len
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
)),
</span>
<span
class=
"n"
>
integer_value
</span><span
class=
"p"
>
(
</span><span
class=
"mi"
>
3
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
integer_value_sequence
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
len
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
)),
</span>
<span
class=
"n"
>
integer_value
</span><span
class=
"p"
>
(
</span><span
class=
"mi"
>
3
</span><span
class=
"p"
>
)
</span>
<span
class=
"p"
>
]
</span>
<span
class=
"p"
>
]
</span>
...
@@ -320,6 +319,7 @@
...
@@ -320,6 +319,7 @@
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
w
</span><span
class=
"p"
>
]
</span>
<span
class=
"k"
>
for
</span>
<span
class=
"n"
>
w
</span>
<span
class=
"ow"
>
in
</span>
<span
class=
"n"
>
words
</span>
<span
class=
"k"
>
if
</span>
<span
class=
"n"
>
w
</span>
<span
class=
"ow"
>
in
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
w
</span><span
class=
"p"
>
]
</span>
<span
class=
"k"
>
for
</span>
<span
class=
"n"
>
w
</span>
<span
class=
"ow"
>
in
</span>
<span
class=
"n"
>
words
</span>
<span
class=
"k"
>
if
</span>
<span
class=
"n"
>
w
</span>
<span
class=
"ow"
>
in
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"p"
>
]
</span>
<span
class=
"p"
>
]
</span>
<span
class=
"k"
>
yield
</span>
<span
class=
"n"
>
words
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
label
</span>
<span
class=
"k"
>
yield
</span>
<span
class=
"n"
>
words
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
label
</span>
</pre></div>
</pre></div>
</td></tr></table></div>
</td></tr></table></div>
<ul
class=
"simple"
>
<ul
class=
"simple"
>
...
@@ -360,9 +360,7 @@
...
@@ -360,9 +360,7 @@
26
26
27
27
28
28
29
29
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span><span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
hook2
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
dict_file
</span><span
class=
"p"
>
,
</span>
<span
class=
"o"
>
**
</span><span
class=
"n"
>
kwargs
</span><span
class=
"p"
>
):
</span>
30
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span><span
class=
"c1"
>
## for hierarchical sequence network
</span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
hook2
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
dict_file
</span><span
class=
"p"
>
,
</span>
<span
class=
"o"
>
**
</span><span
class=
"n"
>
kwargs
</span><span
class=
"p"
>
):
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
dict_file
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
dict_file
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
input_types
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"p"
>
[
</span>
<span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
input_types
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"p"
>
[
</span>
<span
class=
"n"
>
integer_value_sub_sequence
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
len
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
)),
</span>
<span
class=
"n"
>
integer_value_sub_sequence
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
len
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
settings
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
word_dict
</span><span
class=
"p"
>
)),
</span>
...
@@ -628,24 +626,24 @@
...
@@ -628,24 +626,24 @@
15
15
16
16
17
17
18
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span><span
class=
"n"
>
hidden_dim
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"mi"
>
8
</span>
18
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
calrnn
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
y
</span><span
class=
"p"
>
):
</span>
<span
class=
"n"
>
label_dim
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"mi"
>
2
</span>
<span
class=
"n"
>
mem
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
memory
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s1"
>
'
rnn_state_
'
</span>
<span
class=
"o"
>
+
</span>
<span
class=
"n"
>
y
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
name
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
hidden_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
out
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
fc_layer
</span><span
class=
"p"
>
(
</span>
<span
class=
"n"
>
speaker1
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
data_layer
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s2"
>
"
word1
"
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
dict_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
y
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
mem
</span><span
class=
"p"
>
],
</span>
<span
class=
"n"
>
speaker2
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
data_layer
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s2"
>
"
word2
"
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
dict_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
hidden_dim
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
act
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
TanhActivation
</span><span
class=
"p"
>
(),
</span>
<span
class=
"n"
>
bias_attr
</span><span
class=
"o"
>
=
</span><span
class=
"bp"
>
True
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s1"
>
'
rnn_state_
'
</span>
<span
class=
"o"
>
+
</span>
<span
class=
"n"
>
y
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
name
</span><span
class=
"p"
>
)
</span>
<span
class=
"k"
>
return
</span>
<span
class=
"n"
>
out
</span>
<span
class=
"n"
>
emb1
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
embedding_layer
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
speaker1
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
word_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
encoder1
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
calrnn
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
x1
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
emb2
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
embedding_layer
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
speaker2
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
word_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
encoder2
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
calrnn
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
x2
</span><span
class=
"p"
>
)
</span>
<span
class=
"k"
>
return
</span>
<span
class=
"p"
>
[
</span><span
class=
"n"
>
encoder1
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
encoder2
</span><span
class=
"p"
>
]
</span>
<span
class=
"c1"
>
# This hierachical RNN is designed to be equivalent to the RNN in
</span>
<span
class=
"c1"
>
# sequence_nest_rnn_multi_unequalength_inputs.conf
</span>
<span
class=
"n"
>
encoder1_rep
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
encoder2_rep
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
recurrent_group
</span><span
class=
"p"
>
(
</span>
<span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s2"
>
"
stepout
"
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
step
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
step
</span><span
class=
"p"
>
,
</span>
<span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
emb1
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
emb2
</span><span
class=
"p"
>
])
</span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
step
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
x1
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
x2
</span><span
class=
"p"
>
):
</span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
calrnn
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
y
</span><span
class=
"p"
>
):
</span>
<span
class=
"n"
>
mem
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
memory
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
name
</span><span
class=
"o"
>
=
</span><span
class=
"s1"
>
'
rnn_state_
'
</span>
<span
class=
"o"
>
+
</span>
<span
class=
"n"
>
y
</span><span
class=
"o"
>
.
</span><span
class=
"n"
>
name
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
size
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
hidden_dim
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
out
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
fc_layer
</span><span
class=
"p"
>
(
</span>
<span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
y
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
mem
</span><span
class=
"p"
>
],
</span>
</pre></div>
</pre></div>
</td></tr></table></div>
</td></tr></table></div>
<ul
class=
"simple"
>
<ul
class=
"simple"
>
...
@@ -690,9 +688,7 @@
...
@@ -690,9 +688,7 @@
37
37
38
38
39
39
40
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span><span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
outer_step
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
x1
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
x2
</span><span
class=
"p"
>
):
</span>
40
</pre></div></td><td
class=
"code"
><div
class=
"highlight"
><pre><span></span>
<span
class=
"n"
>
index
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"p"
>
[
</span><span
class=
"mi"
>
0
</span><span
class=
"p"
>
]
</span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
inner_step
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
ipt
</span><span
class=
"p"
>
):
</span>
<span
class=
"k"
>
def
</span>
<span
class=
"nf"
>
inner_step
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
ipt
</span><span
class=
"p"
>
):
</span>
<span
class=
"n"
>
index
</span><span
class=
"p"
>
[
</span><span
class=
"mi"
>
0
</span><span
class=
"p"
>
]
</span>
<span
class=
"o"
>
+=
</span>
<span
class=
"mi"
>
1
</span>
<span
class=
"n"
>
index
</span><span
class=
"p"
>
[
</span><span
class=
"mi"
>
0
</span><span
class=
"p"
>
]
</span>
<span
class=
"o"
>
+=
</span>
<span
class=
"mi"
>
1
</span>
<span
class=
"n"
>
i
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
index
</span><span
class=
"p"
>
[
</span><span
class=
"mi"
>
0
</span><span
class=
"p"
>
]
</span>
<span
class=
"n"
>
i
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
index
</span><span
class=
"p"
>
[
</span><span
class=
"mi"
>
0
</span><span
class=
"p"
>
]
</span>
...
@@ -730,6 +726,8 @@
...
@@ -730,6 +726,8 @@
<span
class=
"n"
>
step
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
outer_step
</span><span
class=
"p"
>
,
</span>
<span
class=
"n"
>
step
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
outer_step
</span><span
class=
"p"
>
,
</span>
<span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
SubsequenceInput
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
emb1
</span><span
class=
"p"
>
),
</span>
<span
class=
"n"
>
SubsequenceInput
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
emb2
</span><span
class=
"p"
>
)],
</span>
<span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"p"
>
[
</span><span
class=
"n"
>
SubsequenceInput
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
emb1
</span><span
class=
"p"
>
),
</span>
<span
class=
"n"
>
SubsequenceInput
</span><span
class=
"p"
>
(
</span><span
class=
"n"
>
emb2
</span><span
class=
"p"
>
)],
</span>
<span
class=
"n"
>
targetInlink
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
emb2
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
targetInlink
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
emb2
</span><span
class=
"p"
>
)
</span>
<span
class=
"n"
>
encoder1_last
</span>
<span
class=
"o"
>
=
</span>
<span
class=
"n"
>
last_seq
</span><span
class=
"p"
>
(
</span><span
class=
"nb"
>
input
</span><span
class=
"o"
>
=
</span><span
class=
"n"
>
encoder1_rep
</span><span
class=
"p"
>
)
</span>
</pre></div>
</pre></div>
</td></tr></table></div>
</td></tr></table></div>
<p>
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
<code
class=
"code docutils literal"
><span
class=
"pre"
>
targetInlink
</span></code>
参数设置了外层
<code
class=
"code docutils literal"
><span
class=
"pre"
>
recurrent_group
</span></code>
的输出格式。所以外层输出的序列形状,和
<code
class=
"code docutils literal"
><span
class=
"pre"
>
emb2
</span></code>
的序列形状一致。
</p>
<p>
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
<code
class=
"code docutils literal"
><span
class=
"pre"
>
targetInlink
</span></code>
参数设置了外层
<code
class=
"code docutils literal"
><span
class=
"pre"
>
recurrent_group
</span></code>
的输出格式。所以外层输出的序列形状,和
<code
class=
"code docutils literal"
><span
class=
"pre"
>
emb2
</span></code>
的序列形状一致。
</p>
...
...
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