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体验新版 GitCode,发现更多精彩内容 >>
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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
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.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.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'))
...
...
@@ -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.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.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.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/operators/sum_op.cc
浏览文件 @
b1218d05
...
...
@@ -175,19 +175,21 @@ class SumOp : public framework::OperatorWithKernel {
class
SumOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
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
();
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"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Sum operator.
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"
);
AddComment
(
R"DOC(This OP is used to sum one or more Tensor or LoDTensor
of the input. If the input is LoDTensor, the output only
shares 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,
def sum(x):
"""
${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:
x (Variable): ${x_comment}
x (Variable
|list(Variable)
): ${x_comment}
Returns:
out (Variable)
: ${out_comment}
Variable
: ${out_comment}
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
input0 = layers.data(name="input0", shape=[13, 11], dtype='float32')
input1 = layers.data(name="input1", shape=[13, 11], dtype='float32')
out = layers.sum([input0,input1])
input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
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())
...
...
@@ -15064,85 +15112,90 @@ class PyFuncRegistry(object):
@templatedoc()
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
"""
PyFunc Operator.
User can use :code:`py_func` to register operators in Python side.
The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
numpy array or :code:`LoDTensor`. Paddle would call the registered
:code:`func` in forward part, and call :code:`backward_func` in
backward part (if :code:`backward_func` is not None).
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
``backward_func``. Paddle will call ``func`` at forward runtime and call
``backward_func`` at backward runtime(if ``backward_func`` is not None).
``x`` is the input of ``func``, whose type must be LoDTensor; ``out`` is
the output of ``func``, whose type can be either LoDTensor or NumPy array.
User should set the right data type and shape of :code:`out` before
calling this function. However, data types and shapes of gradients of
:code:`out` and :code:`x` would be inferred automatically.
The input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If some variables of ``out`` have no gradient, the
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
:code:`x`, forward outputs :code:`out` and backward input gradients of
:code:`out`. If some variables of :code:`out` have no gradient, the input
tensor would be None in Python side. If some variables of :code:`in` have
no gradient, users should return None.
The data type and shape of ``out`` should also be set correctly before this
API is called, and the data type and shape of the gradient of ``out`` and
``x`` will be inferred automatically.
This function can also be used to debug the running network. User can
add a :code:`py_func` operator without output, and print input
:code:`x` inside :code:`func`.
This API can also be used to debug the neural network by setting the ``func``
as a function that only print variables.
Args:
func (callable): forward Python function.
x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
Paddle cannot infer shapes and data types of :code:`out`. Users
should create :code:`out` beforehand.
backward_func (callable|None): backward Python function.
None means no backward. Default None.
skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
Variables that are not needed in :code:`backward_func` inputs.
These variables must be any of :code:`x` and :code:`out`.
If set, these vars would not be inputs of :code:`backward_func`,
Only useful when :code:`backward_func` is not None. Default None.
Returns:
out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this
function and the forward input ``x``.
x (Variable): The input of the forward function ``func``, its type can be
Variable | tuple[Variable] | list[Variale], in which Variable is LoDTensor.
out (Variable): The output of the forward function ``func``, its type can be
Variable | tuple[Variable] | list[Variale], in which Variable can be either
LoDTensor or NumPy array. Since Paddle cannot automatically infer the shape
and data type of ``out``, ``out`` must be created in advance.
backward_func (callable, optional): The backward function of the registered OP.
Its default value is None, which means there is no reverse calculation. If
it is not None, ``backward_func`` is called to calculate the gradient of
``x`` when the network is at backward runtime.
skip_vars_in_backward_input (Variable, optional): It's used to limit the input
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:
.. code-block:: python
>>> import paddle.fluid as fluid
>>> import six
>>>
>>> def create_tmp_var(name, dtype, shape):
>>> return fluid.default_main_program().current_block().create_var(
>>> name=name, dtype=dtype, shape=shape)
>>>
>>> # tanh activation has been provided by Paddle C++ op
>>> # Here, we only use tanh to be an example to show the usage
>>> # of py_func
>>> def tanh(x):
>>> return np.tanh(x)
>>>
>>> # forward input x is skipped
>>> def tanh_grad(y, dy):
>>> return np.array(dy) * (1 - np.square(np.array(y)))
>>>
>>> def debug_func(x):
>>> print(x)
>>>
>>> def simple_net(img, label):
>>> hidden = img
>>> for idx in six.moves.range(4):
>>> hidden = fluid.layers.fc(hidden, size=200)
>>> new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
>>> dtype=hidden.dtype, shape=hidden.shape)
>>>
>>> # user-defined layers with forward and backward
>>> hidden = fluid.layers.py_func(func=tanh, x=hidden,
>>> out=new_hidden, backward_func=tanh_grad,
>>> skip_vars_in_backward_input=hidden)
>>>
>>> # user-defined debug layers to print variables
>>> fluid.layers.py_func(func=debug_func, x=hidden, out=None)
>>>
>>> prediction = fluid.layers.fc(hidden, size=10, act='softmax')
>>> loss = fluid.layers.cross_entropy(input=prediction, label=label)
>>> return fluid.layers.mean(loss)
import paddle.fluid as fluid
import six
def create_tmp_var(name, dtype, shape):
return fluid.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
# Tanh activation function provided by Paddle C++ op
# Here, tanh is used as an example to show how to use py_func
def tanh(x):
return np.tanh(x)
# Skip forward input x
def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y)))
def debug_func(x):
print(x)
def simple_net(img, label):
hidden = img
for idx in six.moves.range(4):
hidden = fluid.layers.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward
hidden = fluid.layers.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden)
# User-defined debugging layer, which can print out variable details
fluid.layers.py_func(func=debug_func, x=hidden, out=None)
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(loss)
"""
helper = LayerHelper('py_func', **locals())
if x is None:
...
...
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