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体验新版 GitCode,发现更多精彩内容 >>
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d2e1b46f
编写于
9月 20, 2016
作者:
L
Luo Tao
浏览文件
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浏览文件
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电子邮件补丁
差异文件
update beam_search and seqToseq config, and add ExpActivation api
上级
425e5b0b
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
68 addition
and
99 deletion
+68
-99
demo/seqToseq/seqToseq_net.py
demo/seqToseq/seqToseq_net.py
+9
-13
doc/ui/api/trainer_config_helpers/activations.rst
doc/ui/api/trainer_config_helpers/activations.rst
+7
-0
paddle/trainer/tests/sample_trainer_rnn_gen.conf
paddle/trainer/tests/sample_trainer_rnn_gen.conf
+42
-85
python/paddle/trainer_config_helpers/activations.py
python/paddle/trainer_config_helpers/activations.py
+10
-1
未找到文件。
demo/seqToseq/seqToseq_net.py
浏览文件 @
d2e1b46f
...
...
@@ -128,12 +128,16 @@ def gru_encoder_decoder(data_conf,
return
out
decoder_group_name
=
"decoder_group"
group_inputs
=
[
StaticInput
(
input
=
encoded_vector
,
is_seq
=
True
),
StaticInput
(
input
=
encoded_proj
,
is_seq
=
True
)]
if
not
is_generating
:
trg_embedding
=
embedding_layer
(
input
=
data_layer
(
name
=
'target_language_word'
,
size
=
target_dict_dim
),
size
=
word_vector_dim
,
param_attr
=
ParamAttr
(
name
=
'_target_language_embedding'
))
group_inputs
.
append
(
trg_embedding
)
# For decoder equipped with attention mechanism, in training,
# target embeding (the groudtruth) is the data input,
...
...
@@ -142,22 +146,13 @@ def gru_encoder_decoder(data_conf,
# for the recurrent_group.
decoder
=
recurrent_group
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
[
StaticInput
(
input
=
encoded_vector
,
is_seq
=
True
),
StaticInput
(
input
=
encoded_proj
,
is_seq
=
True
),
trg_embedding
])
input
=
group_inputs
)
lbl
=
data_layer
(
name
=
'target_language_next_word'
,
size
=
target_dict_dim
)
cost
=
classification_cost
(
input
=
decoder
,
label
=
lbl
,
)
cost
=
classification_cost
(
input
=
decoder
,
label
=
lbl
)
outputs
(
cost
)
else
:
gen_inputs
=
[
StaticInput
(
input
=
encoded_vector
,
is_seq
=
True
),
StaticInput
(
input
=
encoded_proj
,
is_seq
=
True
),
]
# In generation, the decoder predicts a next target word based on
# the encoded source sequence and the last generated target word.
...
...
@@ -171,10 +166,11 @@ def gru_encoder_decoder(data_conf,
size
=
target_dict_dim
,
embedding_name
=
'_target_language_embedding'
,
embedding_size
=
word_vector_dim
)
gen_inputs
.
append
(
trg_embedding
)
group_inputs
.
append
(
trg_embedding
)
beam_gen
=
beam_search
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
g
en
_inputs
,
input
=
g
roup
_inputs
,
id_input
=
data_layer
(
name
=
"sent_id"
,
size
=
1
),
dict_file
=
trg_dict_path
,
...
...
doc/ui/api/trainer_config_helpers/activations.rst
浏览文件 @
d2e1b46f
...
...
@@ -12,6 +12,13 @@ AbsActivation
:members: AbsActivation
:noindex:
ExpActivation
===============
.. automodule:: paddle.trainer_config_helpers.activations
:members: ExpActivation
:noindex:
IdentityActivation
==================
...
...
paddle/trainer/tests/sample_trainer_rnn_gen.conf
浏览文件 @
d2e1b46f
...
...
@@ -13,96 +13,53 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
import
math
from
paddle
.
trainer_config_helpers
import
*
beam_search
=
get_config_arg
(
'beam_search'
,
bool
,
False
)
model_type
(
"recurrent_nn"
)
Settings
(
learning_rate
=
0
,
batch_size
=
15
,
algorithm
=
'sgd'
)
Inputs
(
"sent_id"
,
"dummy_data_input"
)
Outputs
(
"predict_word"
)
settings
(
batch_size
=
15
,
learning_rate
=
0
)
num_words
=
5
beam_flag
=
get_config_arg
(
'beam_search'
,
bool
,
False
)
DataLayer
(
name
=
"sent_id"
,
size
=
1
,
)
sent_id
=
data_layer
(
name
=
"sent_id"
,
size
=
1
)
# This layer has no actual use, but only to decide batch_size in generation.
# When generating, at least one Memory in RecurrentLayer MUST have a boot layer.
DataLayer
(
name
=
"dummy_data_input"
,
size
=
2
, )
if
beam_search
:
RecurrentLayerGroupBegin
(
"decoding_layer_group"
,
in_links
=[],
out_links
=[
"predict_word"
],
generator
=
Generator
(
max_num_frames
=
10
,
beam_size
=
2
,
num_results_per_sample
=
2
, ))
else
:
RecurrentLayerGroupBegin
(
"decoding_layer_group"
,
in_links
=[],
out_links
=[
"predict_word"
],
generator
=
Generator
(
max_num_frames
=
10
, ))
dummy_memory
=
Memory
(
name
=
"dummy_memory"
,
size
=
2
,
boot_layer
=
"dummy_data_input"
)
MixedLayer
(
name
=
"dummy_memory"
,
size
=
2
,
bias
=
False
,
inputs
=[
IdentityProjection
(
dummy_memory
)], )
state_memory
=
Memory
(
name
=
"state"
,
size
=
num_words
,
#boot_bias=True,
#boot_bias_active_type = "tanh",
)
predict_word_memory
=
Memory
(
name
=
"predict_word"
,
size
=
num_words
,
boot_with_const_id
=
0
, )
MixedLayer
(
name
=
"word_embedding"
,
size
=
num_words
,
# word embedding dim is the same as num_words in this test.
bias
=
False
,
inputs
=
TableProjection
(
predict_word_memory
,
initial_std
=
1
,
learning_rate
=
0
,
parameter_name
=
"wordvec"
))
Layer
(
# simplified RNN for testing
name
=
"state"
,
type
=
"mixed"
,
size
=
num_words
,
bias
=
False
,
inputs
=[
FullMatrixProjection
(
"word_embedding"
,
parameter_name
=
"transtable"
)])
Layer
(
name
=
"output"
,
type
=
"mixed"
,
size
=
num_words
,
active_type
=
"exponential"
,
bias
=
False
,
inputs
=
TransposedFullMatrixProjection
(
"state"
,
initial_std
=
1
,
learning_rate
=
0
,
parameter_name
=
"wordvec"
), )
Layer
(
name
=
"predict_word"
,
type
=
"maxid"
,
inputs
=[
"output"
], )
Layer
(
name
=
"eos_check"
,
type
=
"eos_id"
,
eos_id
=
num_words
-
1
,
inputs
=[
"predict_word"
], )
RecurrentLayerGroupEnd
(
"decoding_layer_group"
)
Evaluator
(
name
=
"answer_printer"
,
type
=
"seq_text_printer"
,
dict_file
=
"./trainer/tests/test_gen_dict.txt"
,
result_file
=
"./trainer/tests/dump_text.test"
,
inputs
=[
"sent_id"
,
"predict_word"
,
], )
dummy_data
=
data_layer
(
name
=
"dummy_data_input"
,
size
=
2
)
gen_inputs
= [
StaticInput
(
input
=
dummy_data
,
size
=
2
),
GeneratedInput
(
size
=
num_words
,
embedding_name
=
"wordvec"
,
embedding_size
=
num_words
)]
def
step
(
dummy_memory
,
predict_word
):
# simplified RNN for testing
with
mixed_layer
(
size
=
num_words
)
as
layer
:
layer
+=
full_matrix_projection
(
input
=
predict_word
,
param_attr
=
ParamAttr
(
name
=
"transtable"
))
with
mixed_layer
(
size
=
num_words
,
act
=
ExpActivation
())
as
out
:
out
+=
trans_full_matrix_projection
(
input
=
layer
,
param_attr
=
ParamAttr
(
name
=
"wordvec"
))
return
out
beam_gen
=
beam_search
(
name
=
"rnn_gen"
,
step
=
step
,
input
=
gen_inputs
,
id_input
=
sent_id
,
dict_file
=
"./trainer/tests/test_gen_dict.txt"
,
result_file
=
"./trainer/tests/dump_text.test"
,
bos_id
=
0
,
eos_id
=
num_words
-
1
,
beam_size
=
2
if
beam_flag
else
1
,
num_results_per_sample
=
2
if
beam_flag
else
1
,
max_length
=
10
)
#outputs(beam_gen)
# In this config, as dummy_data_input doesn't work on beam_gen (we can find dummy_memory
# is read-only memory, and isn't used by other layers of step), we show the Inputs and Outputs
# as follows. Note that "__beam_search_predict__" is the default output name of beam_search.
Inputs
(
"sent_id"
,
"dummy_data_input"
)
Outputs
(
"__beam_search_predict__"
)
python/paddle/trainer_config_helpers/activations.py
浏览文件 @
d2e1b46f
...
...
@@ -14,7 +14,7 @@
__all__
=
[
"TanhActivation"
,
"SigmoidActivation"
,
"SoftmaxActivation"
,
"IdentityActivation"
,
"LinearActivation"
,
'SequenceSoftmaxActivation'
,
'SequenceSoftmaxActivation'
,
'ExpActivation'
,
"ReluActivation"
,
"BReluActivation"
,
"SoftReluActivation"
,
"STanhActivation"
,
"AbsActivation"
,
"SquareActivation"
,
"BaseActivation"
]
...
...
@@ -185,3 +185,12 @@ class SquareActivation(BaseActivation):
"""
def
__init__
(
self
):
BaseActivation
.
__init__
(
self
,
'square'
,
False
)
class
ExpActivation
(
BaseActivation
):
"""
Exponential Activation.
.. math::
f(z) = e^z.
"""
def
__init__
(
self
):
BaseActivation
.
__init__
(
self
,
'exponential'
,
False
)
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