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c2e8f40d
编写于
6月 17, 2018
作者:
Y
Yu Yang
提交者:
GitHub
6月 17, 2018
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差异文件
Merge pull request #11492 from dzhwinter/doc/api1
[API Reference] fix some typo in layers
上级
20e5ef62
f4a49cb0
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
111 addition
and
25 deletion
+111
-25
paddle/fluid/operators/clip_by_norm_op.cc
paddle/fluid/operators/clip_by_norm_op.cc
+10
-1
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
+2
-2
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+39
-2
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+2
-3
python/paddle/fluid/layers/metric.py
python/paddle/fluid/layers/metric.py
+25
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+33
-16
未找到文件。
paddle/fluid/operators/clip_by_norm_op.cc
浏览文件 @
c2e8f40d
...
...
@@ -54,10 +54,19 @@ be linearly scaled to make the L2 norm of $Out$ equal to $max\_norm$, as
shown in the following formula:
$$
Out = \
frac{max
\_norm * X}{norm(X)},
Out = \
\frac{max\
\_norm * X}{norm(X)},
$$
where $norm(X)$ represents the L2 norm of $X$.
Examples:
.. code-block:: python
data = fluid.layer.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.clip_by_norm(
x=data, max_norm=0.5)
)DOC"
);
}
};
...
...
paddle/fluid/operators/uniform_random_batch_size_like_op.cc
浏览文件 @
c2e8f40d
...
...
@@ -35,10 +35,10 @@ class UniformRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
protected:
void
Apply
()
override
{
AddComment
(
R"DOC(
Uniform
random operator
Uniform
RandomBatchSizeLike operator.
This operator initializes a tensor with the same batch_size as the Input tensor
with random values sampled from a uniform distribution.
with random values sampled from a uniform distribution.
)DOC"
);
AddAttr
<
float
>
(
"min"
,
...
...
python/paddle/fluid/framework.py
浏览文件 @
c2e8f40d
...
...
@@ -1034,6 +1034,37 @@ class Block(object):
class
Program
(
object
):
"""
Python Program. Beneath it is a ProgramDesc, which is used for
create c++ Program. A program is a self-contained programing
language like container. It has at least one Block, when the
control flow op like conditional_block, while_op is included,
it will contains nested block.
Please reference the framework.proto for details.
Notes: we have default_startup_program and default_main_program
by default, a pair of them will shared the parameters.
The default_startup_program only run once to initialize parameters,
default_main_program run in every minibatch and adjust the weights.
Args:
None
Returns:
Python Program
Examples:
.. code-block:: python
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program=main_program, startup_program=startup_program):
fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
"""
def
__init__
(
self
):
self
.
desc
=
core
.
ProgramDesc
()
self
.
blocks
=
[
Block
(
self
,
0
)]
...
...
@@ -1099,6 +1130,8 @@ class Program(object):
def
clone
(
self
,
for_test
=
False
):
"""Clone the Program object
Args:
for_test(bool): indicate whether clone for test.
Set for_test to False when we want to clone the program for training.
Set for_test to True when we want to clone the program for testing.
...
...
@@ -1109,8 +1142,9 @@ class Program(object):
the is_test attributes in these operators will be set to True for
testing purposes, otherwise, they remain unchanged.
Returns(Program):
The cloned Program object.
Returns:
Program: The cloned Program object.
"""
if
for_test
:
p
=
self
.
inference_optimize
()
...
...
@@ -1228,6 +1262,7 @@ class Program(object):
def
copy_param_info_from
(
self
,
other
):
"""
Copy the information of parameters from other program.
Args:
other(Program): Other program
...
...
@@ -1246,6 +1281,7 @@ class Program(object):
def
copy_data_info_from
(
self
,
other
):
"""
Copy the information of data variables from other program.
Args:
other(Program): Other program
...
...
@@ -1299,6 +1335,7 @@ class Parameter(Variable):
def
to_string
(
self
,
throw_on_error
,
with_details
=
False
):
"""
To debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
c2e8f40d
...
...
@@ -902,8 +902,7 @@ def increment(x, value=1.0, in_place=True):
in_place (bool): If the increment should be performed in-place.
Returns:
Variable: The tensor variable storing the transformation of
element-wise increment of each value in the input.
Variable: The elementwise-incremented object.
Examples:
.. code-block:: python
...
...
@@ -945,7 +944,7 @@ def array_write(x, i, array=None):
Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.
Examples:
.. code-block::python
.. code-block::
python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
...
...
python/paddle/fluid/layers/metric.py
浏览文件 @
c2e8f40d
...
...
@@ -27,8 +27,32 @@ __all__ = ['accuracy', 'auc']
def
accuracy
(
input
,
label
,
k
=
1
,
correct
=
None
,
total
=
None
):
"""
accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
The output is the top k inputs and their indices.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network.
Carry LoD information is supported.
label(Variable): The label of dataset.
k(int): The top k predictions for each class will be checked.
correct(Variable): The correct predictions count.
total(Variable): The total entries count.
Returns:
Variable: The correct rate.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32")
predict = fluid.layers.fc(input=data, size=10)
acc = fluid.layers.accuracy(input=predict, label=label, k=5)
"""
helper
=
LayerHelper
(
"accuracy"
,
**
locals
())
topk_out
,
topk_indices
=
nn
.
topk
(
input
,
k
=
k
)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
c2e8f40d
...
...
@@ -794,11 +794,14 @@ def linear_chain_crf(input, label, param_attr=None):
Args:
input(${emission_type}): ${emission_comment}
input(${transition_type}): ${transition_comment}
label(${label_type}): ${label_comment}
param_attr(ParamAttr): The attribute of the learnable parameter.
Returns:
${log_likelihood_comment}
output(${emission_exps_type}): ${emission_exps_comment}
\n
output(${transition_exps_type}): ${transition_exps_comment}
\n
output(${log_likelihood_type}): ${log_likelihood_comment}
"""
helper
=
LayerHelper
(
'linear_chain_crf'
,
**
locals
())
...
...
@@ -1131,10 +1134,6 @@ def sequence_conv(input,
Variable: output of sequence_conv
"""
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes.
# such as, padding_trainable, context_start.
helper
=
LayerHelper
(
'sequence_conv'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
filter_shape
=
[
filter_size
*
input
.
shape
[
1
],
num_filters
]
...
...
@@ -2068,15 +2067,37 @@ def layer_norm(input,
def
beam_search_decode
(
ids
,
scores
,
name
=
None
):
"""
${beam_search_decode}
Beam Search Decode
This layers is to pack the output of beam search layer into sentences and
associated scores. It is usually called after the beam search layer.
Typically, the output of beam search layer is a tensor of selected ids, with
a tensor of the score of each id. Beam search layer's output ids, however,
are generated directly during the tree search, and they are stacked by each
level of the search tree. Thus we need to reorganize them into sentences,
based on the score of each id. This layer takes the output of beam search
layer as input and repack them into sentences.
Args:
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
ids (Variable): The selected ids, output of beam search layer.
scores (Variable): The associated scores of the ids, out put of beam
search layer.
name (str): The name of this layer. It is optional.
Returns:
tuple: a tuple of two output variable: sentence_ids, sentence_scores
tuple(Variable): a tuple of two output tensors: sentence_ids, sentence_scores.
sentence_ids is a tensor with shape [size, length], where size is the
beam size of beam search, and length is the length of each sentence.
Note that the length of sentences may vary.
sentence_scores is a tensor with the same shape as sentence_ids.
Examples:
.. code-block:: python
ids, scores = fluid.layers.beam_search(
pre_ids, ids, scores, beam_size, end_id)
sentence_ids, sentence_scores = fluid.layers.beam_search_decode(
ids, scores)
"""
helper
=
LayerHelper
(
'beam_search_decode'
,
**
locals
())
sentence_ids
=
helper
.
create_tmp_variable
(
dtype
=
ids
.
dtype
)
...
...
@@ -2957,7 +2978,7 @@ def split(input, num_or_sections, dim=-1, name=None):
will be named automatically.
Returns:
List
: The list of segmented tensor variables.
list(Variable)
: The list of segmented tensor variables.
Examples:
.. code-block:: python
...
...
@@ -3690,8 +3711,6 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
Examples:
As an example:
.. code-block:: text
Given:
...
...
@@ -3735,7 +3754,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
output.lod = [[4, 4]]
The simple usage i
s:
Example
s:
.. code-block:: python
...
...
@@ -4220,9 +4239,7 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
.. math::
Output(i, x, y) = Input(i, x, y) / \left(
k +
\a
lpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
(Input(j, x, y))^2
\r
ight)^{
\b
eta}
Output(i, x, y) = Input(i, x, y) /
\\
left(k +
\\
alpha
\\
sum
\\
limits^{
\\
min(C, c + n/2)}_{j =
\\
max(0, c - n/2)}(Input(j, x, y))^2
\\
right)^{
\\
beta}
In the above equation:
...
...
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