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bff4cec3
编写于
6月 14, 2018
作者:
Y
Yibing Liu
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Format lod_reset's doc
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python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
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python/paddle/fluid/layers/nn.py
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...
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@@ -3411,8 +3411,8 @@ def softmax_with_cross_entropy(logits, label, soft_label=False):
def
smooth_l1
(
x
,
y
,
inside_weight
=
None
,
outside_weight
=
None
,
sigma
=
None
):
"""
This layer computes the smooth L1 loss for Variable
`x` and
`y`.
It takes the first dimension of
`x` and
`y` as batch size.
This layer computes the smooth L1 loss for Variable
:attr:`x` and :attr:
`y`.
It takes the first dimension of
:attr:`x` and :attr:
`y` as batch size.
For each instance, it computes the smooth L1 loss element by element first
and then sums all the losses. So the shape of ouput Variable is
[batch_size, 1].
...
...
@@ -3421,15 +3421,15 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
x (Variable): A tensor with rank at least 2. The input value of smooth
L1 loss op with shape [batch_size, dim1, ..., dimN].
y (Variable): A tensor with rank at least 2. The target value of smooth
L1 loss op with same shape as `x`.
L1 loss op with same shape as
:attr:
`x`.
inside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with
`x`. If provided,
the result of (`x - y`) will be multiplied by this tensor element by
element.
input is optional and should have same shape with
:attr:`x`. If
provided, the result of (:attr:`x` - :attr:`y`) will be multiplied
by this tensor element by
element.
outside_weight (Variable|None): A tensor with rank at least 2. This
input is optional and should have same shape with
x. If provided,
the out smooth L1 loss will be multiplied by this tensor element
by element.
input is optional and should have same shape with
:attr:`x`. If
provided, the out smooth L1 loss will be multiplied by this tensor
element
by element.
sigma (float|None): Hyper parameter of smooth L1 loss layer. A float
scalar with default value 1.0.
...
...
@@ -3634,12 +3634,12 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
def
lod_reset
(
x
,
y
=
None
,
target_lod
=
None
):
"""
LoD Reset Operator. Set LoD of **x** to a new one specified by **y**
or
**target_lod**. When **y** provided, **y.lod** would be considered as target
LoD first, otherwise **y.data** would be considered as target LoD. If **y**
is not provided, target LoD should be specified by **target_lod**.
If target LoD is specified by **Y.data** or **target_lod**, only one level
LoD is supported.
Set LoD of :attr:`x` to a new one specified by :attr:`y`
or
:attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be
considered as target LoD first, otherwise :attr:`y.data` would be
considered as target LoD. If :attr:`y` is not provided, target LoD should
be specified by :attr:`target_lod`. If target LoD is specified by
:attr:`Y.data` or :attr:`target_lod`, only one level
LoD is supported.
.. code-block:: text
...
...
@@ -3692,15 +3692,16 @@ def lod_reset(x, y=None, target_lod=None):
Args:
x (Variable): Input variable which could be a Tensor or LodTensor.
y (Variable|None): If provided, output's LoD would be derived from y.
y (Variable|None): If provided, output's LoD would be derived
from :attr:`y`.
target_lod (list|tuple|None): One level LoD which should be considered
as target LoD when
y
not provided.
as target LoD when
:attr:`y`
not provided.
Returns:
Variable: Output variable with LoD specified by this
operato
r.
Variable: Output variable with LoD specified by this
laye
r.
Raises:
ValueError: If
y and target_lod
are both None.
ValueError: If
:attr:`y` and :attr:`target_lod`
are both None.
Examples:
.. code-block:: python
...
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