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b1218d05
编写于
10月 11, 2019
作者:
Z
zhouwei25
提交者:
liuwei1031
10月 11, 2019
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电子邮件补丁
差异文件
fix English Doc of API:layers.py_func/sum (#20329)
* fix English Doc of API:layers.py_func/sum
上级
63194d6e
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
143 addition
and
88 deletion
+143
-88
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-2
paddle/fluid/operators/sum_op.cc
paddle/fluid/operators/sum_op.cc
+11
-9
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+130
-77
未找到文件。
paddle/fluid/API.spec
浏览文件 @
b1218d05
...
@@ -257,7 +257,7 @@ paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shap
...
@@ -257,7 +257,7 @@ paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shap
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '2490492db3b41af9144bb1539e4e9116'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '2490492db3b41af9144bb1539e4e9116'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '
f4b60847cb0f1ae00823ba6fb1b11310
'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '
42c43fc74347bfe9528850aa7f59b2b2
'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '33b8dfd6708443ae93f1a0016ff6a5ef'))
paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '33b8dfd6708443ae93f1a0016ff6a5ef'))
paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '39534cccdb8e727e287316c7c42e6663'))
paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '39534cccdb8e727e287316c7c42e6663'))
...
@@ -288,7 +288,7 @@ paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], v
...
@@ -288,7 +288,7 @@ paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], v
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '276a1213dd431228cefa33c3146df34a'))
paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '
8404e472ac12b4a30a505d3d3a3e5fdb
'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '
231f91231430f5dae2b757df22317c67
'))
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '454c7ea8c73313dd41513929d7526303'))
paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '454c7ea8c73313dd41513929d7526303'))
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', 'b0e07aa41caae04b07a8e8217cc96020'))
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', 'b0e07aa41caae04b07a8e8217cc96020'))
...
...
paddle/fluid/operators/sum_op.cc
浏览文件 @
b1218d05
...
@@ -175,19 +175,21 @@ class SumOp : public framework::OperatorWithKernel {
...
@@ -175,19 +175,21 @@ class SumOp : public framework::OperatorWithKernel {
class
SumOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
SumOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
public:
void
Make
()
override
{
void
Make
()
override
{
AddInput
(
"X"
,
"(vector<Tensor>) The input tensors of sum operator."
)
AddInput
(
"X"
,
"A Varaible list. The shape and data type of the list elements"
"should be consistent. Variable can be multi-dimensional Tensor"
"or LoDTensor, and data types can be: float32, float64, int32, "
"int64."
)
.
AsDuplicable
();
.
AsDuplicable
();
AddOutput
(
"Out"
,
"(Tensor) The output tensor of sum operator."
);
AddOutput
(
"Out"
,
"the sum of input :code:`x`. its shape and data types are "
"consistent with :code:`x`."
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
.
SetDefault
(
false
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(This OP is used to sum one or more Tensor or LoDTensor
Sum operator.
of the input. If the input is LoDTensor, the output only
shares LoD information with the first input.)DOC"
);
This operators sums the input tensors. All the inputs can carry the
LoD (Level of Details) information. However, the output only shares
the LoD information with the first input.
)DOC"
);
}
}
};
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
b1218d05
...
@@ -12508,21 +12508,69 @@ def gaussian_random_batch_size_like(input,
...
@@ -12508,21 +12508,69 @@ def gaussian_random_batch_size_like(input,
def sum(x):
def sum(x):
"""
"""
${comment}
${comment}
Case 1:
::
Input:
Input. Shape = [2, 3]
Input = [[1, 2, 3],
[4, 5, 6]]
Output:
The output. Shape = [2, 3]
Output = [[1, 2, 3],
[4, 5, 6]]
Case 2:
::
Input:
First input:
Input1. Shape = [2, 3]
Input1 = [[1, 2, 3],
[4, 5, 6]]
The second input:
Input2. Shape = [2, 3]
Input2 = [[7, 8, 9],
[10, 11, 12]]
Output:
The output. Shape = [2, 3]
Output = [[8, 10, 12],
[14, 16, 18]]
Args:
Args:
x (Variable): ${x_comment}
x (Variable
|list(Variable)
): ${x_comment}
Returns:
Returns:
out (Variable)
: ${out_comment}
Variable
: ${out_comment}
Examples:
Examples:
.. code-block:: python
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid as fluid
import paddle.fluid.layers as layers
input0 = layers.data(name="input0", shape=[13, 11], dtype='float32')
input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
out = layers.sum([input0,input1])
sum = fluid.layers.sum([input0, input1])
# You can print out 'sum' via executor.
out = fluid.layers.Print(sum, message="the sum of input0 and input1: ")
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_main_program())
# The printed result is:
# 1570701754 the sum of input0 and input1: The place is:CPUPlace
# Tensor[sum_0.tmp_0]
# shape: [2,3,]
# dtype: l
# data: 8,8,8,8,8,8,
# the sum of input0 and input1 is 2-D Tensor with shape [2,3].
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
# so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
# and '__int64' on Windows. They both represent 64-bit integer variables.
"""
"""
helper = LayerHelper('sum', **locals())
helper = LayerHelper('sum', **locals())
...
@@ -15064,85 +15112,90 @@ class PyFuncRegistry(object):
...
@@ -15064,85 +15112,90 @@ class PyFuncRegistry(object):
@templatedoc()
@templatedoc()
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
"""
"""
PyFunc Operator.
This API is used to register customized OP to Fluid. The forward function
of the registered OP is ``func`` and the backward function of that is
User can use :code:`py_func` to register operators in Python side.
``backward_func``. Paddle will call ``func`` at forward runtime and call
The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
``backward_func`` at backward runtime(if ``backward_func`` is not None).
numpy array or :code:`LoDTensor`. Paddle would call the registered
``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is
:code:`func` in forward part, and call :code:`backward_func` in
the output of ``func``, whose type can be either LoDTensor or NumPy array.
backward part (if :code:`backward_func` is not None).
User should set the right data type and shape of :code:`out` before
The input of the backward function ``backward_func`` is ``x``, ``out`` and
calling this function. However, data types and shapes of gradients of
the gradient of ``out``. If some variables of ``out`` have no gradient, the
:code:`out` and :code:`x` would be inferred automatically.
relevant input variable of ``backward_func`` is None. If some variables of
``x`` do not have a gradient, the user should return None in ``backward_func``.
Input orders of :code:`backward_func` would be: forward inputs
The data type and shape of ``out`` should also be set correctly before this
:code:`x`, forward outputs :code:`out` and backward input gradients of
API is called, and the data type and shape of the gradient of ``out`` and
:code:`out`. If some variables of :code:`out` have no gradient, the input
``x`` will be inferred automatically.
tensor would be None in Python side. If some variables of :code:`in` have
no gradient, users should return None.
This function can also be used to debug the running network. User can
This API can also be used to debug the neural network by setting the ``func``
add a :code:`py_func` operator without output, and print input
as a function that only print variables.
:code:`x` inside :code:`func`.
Args:
Args:
func (callable): forward Python function.
func (callable): The forward function of the registered OP. When the network
x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
is running, the forward output ``out`` will be calculated according to this
out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
function and the forward input ``x``.
Paddle cannot infer shapes and data types of :code:`out`. Users
x (Variable): The input of the forward function ``func``, its type can be
should create :code:`out` beforehand.
Variable | tuple[Variable] | list[Variale], in which Variable is LoDTensor.
backward_func (callable|None): backward Python function.
out (Variable): The output of the forward function ``func``, its type can be
None means no backward. Default None.
Variable | tuple[Variable] | list[Variale], in which Variable can be either
skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
LoDTensor or NumPy array. Since Paddle cannot automatically infer the shape
Variables that are not needed in :code:`backward_func` inputs.
and data type of ``out``, ``out`` must be created in advance.
These variables must be any of :code:`x` and :code:`out`.
backward_func (callable, optional): The backward function of the registered OP.
If set, these vars would not be inputs of :code:`backward_func`,
Its default value is None, which means there is no reverse calculation. If
Only useful when :code:`backward_func` is not None. Default None.
it is not None, ``backward_func`` is called to calculate the gradient of
``x`` when the network is at backward runtime.
Returns:
skip_vars_in_backward_input (Variable, optional): It's used to limit the input
out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
variable list of ``backward_func``, and it can be single Variable, tuple[Variable]
or list[Variable]. It must belong to either ``x`` or ``out``. The default
value is None, which means that no variables need to be removed from ``x``
and ``out``. If it is not None, these variables will not be the input of
``backward_func``. This parameter is only useful when ``backward_func`` is
not None.
Returns:
Variable: The output ``out`` of the forward function ``func``.
Examples:
Examples:
.. code-block:: python
>>> import paddle.fluid as fluid
import paddle.fluid as fluid
>>> import six
import six
>>>
>>> def create_tmp_var(name, dtype, shape):
def create_tmp_var(name, dtype, shape):
>>> return fluid.default_main_program().current_block().create_var(
return fluid.default_main_program().current_block().create_var(
>>> name=name, dtype=dtype, shape=shape)
name=name, dtype=dtype, shape=shape)
>>>
>>> # tanh activation has been provided by Paddle C++ op
# Tanh activation function provided by Paddle C++ op
>>> # Here, we only use tanh to be an example to show the usage
# Here, tanh is used as an example to show how to use py_func
>>> # of py_func
def tanh(x):
>>> def tanh(x):
return np.tanh(x)
>>> return np.tanh(x)
>>>
# Skip forward input x
>>> # forward input x is skipped
def tanh_grad(y, dy):
>>> def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y)))
>>> return np.array(dy) * (1 - np.square(np.array(y)))
>>>
def debug_func(x):
>>> def debug_func(x):
print(x)
>>> print(x)
>>>
def simple_net(img, label):
>>> def simple_net(img, label):
hidden = img
>>> hidden = img
for idx in six.moves.range(4):
>>> for idx in six.moves.range(4):
hidden = fluid.layers.fc(hidden, size=200)
>>> hidden = fluid.layers.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
>>> new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape)
>>> dtype=hidden.dtype, shape=hidden.shape)
>>>
# User-defined forward and backward
>>> # user-defined layers with forward and backward
hidden = fluid.layers.py_func(func=tanh, x=hidden,
>>> hidden = fluid.layers.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad,
>>> out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden)
>>> skip_vars_in_backward_input=hidden)
>>>
# User-defined debugging layer, which can print out variable details
>>> # user-defined debug layers to print variables
fluid.layers.py_func(func=debug_func, x=hidden, out=None)
>>> fluid.layers.py_func(func=debug_func, x=hidden, out=None)
>>>
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
>>> prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
>>> loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(loss)
>>> return fluid.layers.mean(loss)
"""
"""
helper = LayerHelper('py_func', **locals())
helper = LayerHelper('py_func', **locals())
if x is None:
if x is None:
...
...
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