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71053063
编写于
2月 23, 2018
作者:
Y
Yu Yang
浏览文件
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差异文件
test Parallel.Do and DynRNN
上级
574bcdab
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
59 addition
and
1 deletion
+59
-1
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+2
-1
python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
...n/paddle/v2/fluid/tests/book/test_understand_sentiment.py
+57
-0
未找到文件。
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
71053063
...
...
@@ -652,7 +652,8 @@ class While(object):
parent_block
.
append_op
(
type
=
'while'
,
inputs
=
{
'X'
:
[
parent_block
.
var
(
x_name
)
for
x_name
in
x_name_list
],
'X'
:
[
parent_block
.
var_recursive
(
x_name
)
for
x_name
in
x_name_list
],
'Condition'
:
[
self
.
cond_var
]
},
outputs
=
{
'Out'
:
out_vars
,
...
...
python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
浏览文件 @
71053063
...
...
@@ -47,6 +47,46 @@ def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
return
avg_cost
,
accuracy
,
prediction
def
dyn_rnn_lstm
(
data
,
label
,
input_dim
,
class_dim
=
2
,
emb_dim
=
32
,
lstm_size
=
128
):
emb
=
fluid
.
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
],
is_sparse
=
True
)
sentence
=
fluid
.
layers
.
fc
(
input
=
emb
,
size
=
lstm_size
,
act
=
'tanh'
)
rnn
=
fluid
.
layers
.
DynamicRNN
()
with
rnn
.
block
():
word
=
rnn
.
step_input
(
sentence
)
prev_hidden
=
rnn
.
memory
(
value
=
0.0
,
shape
=
[
lstm_size
])
prev_cell
=
rnn
.
memory
(
value
=
0.0
,
shape
=
[
lstm_size
])
def
gate_common
(
ipt
,
hidden
,
size
):
gate0
=
fluid
.
layers
.
fc
(
input
=
ipt
,
size
=
size
,
bias_attr
=
True
)
gate1
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
size
,
bias_attr
=
False
)
return
gate0
+
gate1
forget_gate
=
fluid
.
layers
.
sigmoid
(
x
=
gate_common
(
word
,
prev_hidden
,
lstm_size
))
input_gate
=
fluid
.
layers
.
sigmoid
(
x
=
gate_common
(
word
,
prev_hidden
,
lstm_size
))
output_gate
=
fluid
.
layers
.
sigmoid
(
x
=
gate_common
(
word
,
prev_hidden
,
lstm_size
))
cell_gate
=
fluid
.
layers
.
sigmoid
(
x
=
gate_common
(
word
,
prev_hidden
,
lstm_size
))
cell
=
forget_gate
*
prev_cell
+
input_gate
*
cell_gate
hidden
=
output_gate
*
fluid
.
layers
.
tanh
(
x
=
cell
)
rnn
.
update_memory
(
prev_cell
,
cell
)
rnn
.
update_memory
(
prev_hidden
,
hidden
)
rnn
.
output
(
hidden
)
last
=
fluid
.
layers
.
sequence_last_step
(
rnn
())
prediction
=
fluid
.
layers
.
fc
(
input
=
last
,
size
=
class_dim
,
act
=
"softmax"
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
accuracy
,
prediction
def
stacked_lstm_net
(
data
,
label
,
input_dim
,
...
...
@@ -270,6 +310,23 @@ class TestUnderstandSentiment(unittest.TestCase):
use_cuda
=
True
,
parallel
=
True
)
@
unittest
.
skip
(
reason
=
'make CI faster'
)
def
test_dynrnn_lstm_gpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
dyn_rnn_lstm
,
use_cuda
=
True
,
parallel
=
False
)
def
test_dynrnn_lstm_gpu_parallel
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
dyn_rnn_lstm
,
use_cuda
=
True
,
parallel
=
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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