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体验新版 GitCode,发现更多精彩内容 >>
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8c9041f4
编写于
6月 08, 2018
作者:
Y
yuyang18
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差异文件
Refine LinearCRF
上级
0d29e659
变更
3
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3 changed file
with
28 addition
and
20 deletion
+28
-20
paddle/fluid/operators/linear_chain_crf_op.cc
paddle/fluid/operators/linear_chain_crf_op.cc
+0
-2
python/paddle/fluid/layers/layer_function_generator.py
python/paddle/fluid/layers/layer_function_generator.py
+9
-1
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+19
-17
未找到文件。
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @
8c9041f4
...
...
@@ -67,8 +67,6 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
"mini-batch. Note: S is equal to the sequence number in a mini-batch. "
"The output is no longer a LoDTensor."
);
AddComment
(
R"DOC(
LinearChainCRF Operator.
Conditional Random Field defines an undirected probabilistic graph with nodes
denoting random variables and edges denoting dependencies between these
variables. CRF learns the conditional probability $P(Y|X)$, where
...
...
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @
8c9041f4
...
...
@@ -224,6 +224,9 @@ def autodoc(comment=""):
return
__impl__
_inline_math_single_dollar
=
re
.
compile
(
r
"\$([^\$]+)\$"
)
def
templatedoc
(
op_type
=
None
):
"""
Decorator of layer function. It will use the docstring from the layer
...
...
@@ -241,6 +244,9 @@ def templatedoc(op_type=None):
def
trim_ending_dot
(
msg
):
return
msg
.
rstrip
(
'.'
)
def
escape_inline_math
(
msg
):
return
_inline_math_single_dollar
.
sub
(
repl
=
r
':math:`\1`'
,
string
=
msg
)
def
__impl__
(
func
):
if
op_type
is
None
:
op_type_name
=
func
.
__name__
...
...
@@ -254,8 +260,10 @@ def templatedoc(op_type=None):
for
line
in
comment_lines
:
line
=
line
.
strip
()
if
len
(
line
)
!=
0
:
comment
+=
line
comment
+=
escape_inline_math
(
line
)
comment
+=
" "
elif
len
(
comment
)
!=
0
:
comment
+=
"
\n
\n
"
args
=
{
"comment"
:
trim_ending_dot
(
comment
)}
for
each_input
in
op_proto
.
inputs
:
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
8c9041f4
...
...
@@ -11,6 +11,14 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
When training a model, it's often useful to decay the
learning rate during training process, this is called
learning_rate_decay. There are many strategies to do
this, this module will provide some classical method.
User can also implement their own learning_rate_decay
strategy according to this module.
"""
import
control_flow
import
nn
...
...
@@ -22,14 +30,6 @@ __all__ = [
'exponential_decay'
,
'natural_exp_decay'
,
'inverse_time_decay'
,
'polynomial_decay'
,
'piecewise_decay'
,
'noam_decay'
]
"""
When training a model, it's often useful to decay the
learning rate during training process, this is called
learning_rate_decay. There are many strategies to do
this, this module will provide some classical method.
User can also implement their own learning_rate_decay
strategy according to this module.
"""
def
_decay_step_counter
(
begin
=
0
):
...
...
@@ -41,18 +41,20 @@ def _decay_step_counter(begin=0):
def
noam_decay
(
d_model
,
warmup_steps
):
"""Apply decay to learning rate.
```python
lr_value = np.power(d_model, -0.5) * np.min([
np.power(current_steps, -0.5),
np.power(warmup_steps, -1.5) * current_steps
])
```
"""
Noam decay method. The numpy implementation of noam decay as follows.
>>> import numpy as np
>>> lr_value = np.power(d_model, -0.5) * np.min([
>>> np.power(current_steps, -0.5),
>>> np.power(warmup_steps, -1.5) * current_steps])
Please reference `attention is all you need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Args:
d_model(Variable): The dimensionality of input and output of model.
Reference: attention is all you need
https://arxiv.org/pdf/1706.03762.pdf
warmup_steps(Variable): A super parameter.
Returns:
...
...
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