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fix the margin ranking loss doc and api, test=develop (#26266)

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* upate the target to label, test=develop

* Update the code for the margin_ranking_loss, test=develop
revert-24895-update_cub
wawltor 5 years ago committed by GitHub
parent 00e08ce07b
commit d03dd9d5f1
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2 changed files with 19 additions and 19 deletions
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22
python/paddle/nn/functional/loss.py
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@ -73,16 +73,16 @@ __all__ = [
def margin_ranking_loss(input,
other,
target,
label,
margin=0.0,
reduction='mean',
name=None):
"""
This op the calcluate the the margin rank loss between the input x, y and target, use the math function as follows.
This op the calcluate the the margin rank loss between the input, other and label, use the math function as follows.
.. math::
margin\_rank\_loss = max(0, -target * (input - other) + margin)
margin\_rank\_loss = max(0, -label * (input - other) + margin)
If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
@ -99,7 +99,7 @@ def margin_ranking_loss(input,
Parameters:
input(Tensor): the first input tensor, it's data type should be float32, float64.
other(Tensor): the second input tensor, it's data type should be float32, float64.
target(Tensor): the target value corresponding to input, it's data type should be float32, float64.
label(Tensor): the label value corresponding to input, it's data type should be float32, float64.
margin (float, optional): The margin value to add, default value is 0;
reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
@ -115,15 +115,15 @@ def margin_ranking_loss(input,
paddle.disable_static()
x = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype('float32'))
y = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype('float32'))
target = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype('float32'))
loss = paddle.nn.functional.margin_ranking_loss(x, y, target)
input = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype('float32'))
other = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype('float32'))
label = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype('float32'))
loss = paddle.nn.functional.margin_ranking_loss(input, other, label)
print(loss.numpy()) # [0.75]
"""
if fluid.framework.in_dygraph_mode():
out = core.ops.elementwise_sub(other, input)
out = core.ops.elementwise_mul(out, target)
out = core.ops.elementwise_mul(out, label)
if margin != 0.0:
margin = fluid.dygraph.base.to_variable([margin], dtype=out.dtype)
out = core.ops.elementwise_add(out, margin)
@ -140,10 +140,10 @@ def margin_ranking_loss(input,
fluid.data_feeder.check_variable_and_dtype(
other, 'other', ['float32', 'float64'], 'margin_rank_loss')
fluid.data_feeder.check_variable_and_dtype(
target, 'target', ['float32', 'float64'], 'margin_rank_loss')
label, 'label', ['float32', 'float64'], 'margin_rank_loss')
out = paddle.elementwise_sub(other, input)
out = paddle.multiply(out, target)
out = paddle.multiply(out, label)
if margin != 0.0:
margin_var = out.block.create_var(dtype=out.dtype)

16
python/paddle/nn/layer/loss.py
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@ -648,11 +648,11 @@ class MarginRankingLoss(fluid.dygraph.Layer):
"""
This interface is used to construct a callable object of the ``MarginRankingLoss`` class.
The MarginRankingLoss layer calculates the margin rank loss between the input, other and target
The MarginRankingLoss layer calculates the margin rank loss between the input, other and label
, use the math function as follows.
.. math::
margin\_rank\_loss = max(0, -target * (input - other) + margin)
margin\_rank\_loss = max(0, -label * (input - other) + margin)
If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
@ -674,8 +674,8 @@ class MarginRankingLoss(fluid.dygraph.Layer):
Shape:
input: N-D Tensor, the shape is [N, *], N is batch size and `*` means any number of additional dimensions., available dtype is float32, float64.
other: N-D Tensor, `other` have the same shape and dtype as `input`.
target: N-D Tensor, target have the same shape and dtype as `input`.
out: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
label: N-D Tensor, label have the same shape and dtype as `input`.
output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
Returns:
A callable object of MarginRankingLoss.
@ -691,9 +691,9 @@ class MarginRankingLoss(fluid.dygraph.Layer):
input = paddle.to_variable(np.array([[1, 2], [3, 4]]).astype("float32"))
other = paddle.to_variable(np.array([[2, 1], [2, 4]]).astype("float32"))
target = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype("float32"))
label = paddle.to_variable(np.array([[1, -1], [-1, -1]]).astype("float32"))
margin_rank_loss = paddle.nn.MarginRankingLoss()
loss = margin_rank_loss(input, other, target)
loss = margin_rank_loss(input, other, label)
print(loss.numpy()) # [0.75]
"""
@ -707,7 +707,7 @@ class MarginRankingLoss(fluid.dygraph.Layer):
self.reduction = reduction
self.name = name
def forward(self, input, other, target):
def forward(self, input, other, label):
out = paddle.nn.functional.margin_ranking_loss(
input, other, target, self.margin, self.reduction, self.name)
input, other, label, self.margin, self.reduction, self.name)
return out

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