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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/16 06:42:01 UTC
[incubator-mxnet] Diff for: [GitHub] szha merged pull request #13745: Fix
the order of error term's operands
diff --git a/python/mxnet/gluon/loss.py b/python/mxnet/gluon/loss.py
index 7b5832e1ace..29d0105ae8d 100644
--- a/python/mxnet/gluon/loss.py
+++ b/python/mxnet/gluon/loss.py
@@ -99,11 +99,11 @@ def hybrid_forward(self, F, x, *args, **kwargs):
class L2Loss(Loss):
- r"""Calculates the mean squared error between `pred` and `label`.
+ r"""Calculates the mean squared error between `label` and `pred`.
- .. math:: L = \frac{1}{2} \sum_i \vert {pred}_i - {label}_i \vert^2.
+ .. math:: L = \frac{1}{2} \sum_i \vert {label}_i - {pred}_i \vert^2.
- `pred` and `label` can have arbitrary shape as long as they have the same
+ `label` and `pred` can have arbitrary shape as long as they have the same
number of elements.
Parameters
@@ -131,17 +131,17 @@ def __init__(self, weight=1., batch_axis=0, **kwargs):
def hybrid_forward(self, F, pred, label, sample_weight=None):
label = _reshape_like(F, label, pred)
- loss = F.square(pred - label)
+ loss = F.square(label - pred)
loss = _apply_weighting(F, loss, self._weight/2, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
class L1Loss(Loss):
- r"""Calculates the mean absolute error between `pred` and `label`.
+ r"""Calculates the mean absolute error between `label` and `pred`.
- .. math:: L = \sum_i \vert {pred}_i - {label}_i \vert.
+ .. math:: L = \sum_i \vert {label}_i - {pred}_i \vert.
- `pred` and `label` can have arbitrary shape as long as they have the same
+ `label` and `pred` can have arbitrary shape as long as they have the same
number of elements.
Parameters
@@ -169,7 +169,7 @@ def __init__(self, weight=None, batch_axis=0, **kwargs):
def hybrid_forward(self, F, pred, label, sample_weight=None):
label = _reshape_like(F, label, pred)
- loss = F.abs(pred - label)
+ loss = F.abs(label - pred)
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
@@ -195,7 +195,7 @@ class SigmoidBinaryCrossEntropyLoss(Loss):
(1 - {label}_i) * \log(1 - {pred}_i)
- `pred` and `label` can have arbitrary shape as long as they have the same
+ `label` and `pred` can have arbitrary shape as long as they have the same
number of elements.
Parameters
@@ -344,7 +344,7 @@ class KLDivLoss(Loss):
L = \sum_i {label}_i * \big[\log({label}_i) - log({pred}_i)\big]
- `pred` and `label` can have arbitrary shape as long as they have the same
+ `label` and `pred` can have arbitrary shape as long as they have the same
number of elements.
Parameters
@@ -481,13 +481,13 @@ class HuberLoss(Loss):
exceeds rho but is equal to L2 loss otherwise. Also called SmoothedL1 loss.
.. math::
- L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({pred}_i - {label}_i)^2 &
- \text{ if } |{pred}_i - {label}_i| < {rho} \\
- |{pred}_i - {label}_i| - \frac{{rho}}{2} &
+ L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({label}_i - {pred}_i)^2 &
+ \text{ if } |{label}_i - {pred}_i| < {rho} \\
+ |{label}_i - {pred}_i| - \frac{{rho}}{2} &
\text{ otherwise }
\end{cases}
- `pred` and `label` can have arbitrary shape as long as they have the same
+ `label` and `pred` can have arbitrary shape as long as they have the same
number of elements.
Parameters
@@ -518,7 +518,7 @@ def __init__(self, rho=1, weight=None, batch_axis=0, **kwargs):
def hybrid_forward(self, F, pred, label, sample_weight=None):
label = _reshape_like(F, label, pred)
- loss = F.abs(pred - label)
+ loss = F.abs(label - pred)
loss = F.where(loss > self._rho, loss - 0.5 * self._rho,
(0.5/self._rho) * F.square(loss))
loss = _apply_weighting(F, loss, self._weight, sample_weight)
@@ -532,7 +532,7 @@ class HingeLoss(Loss):
L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)
where `pred` is the classifier prediction and `label` is the target tensor
- containing values -1 or 1. `pred` and `label` must have the same number of
+ containing values -1 or 1. `label` and `pred` must have the same number of
elements.
Parameters
@@ -576,7 +576,7 @@ class SquaredHingeLoss(Loss):
L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)^2
where `pred` is the classifier prediction and `label` is the target tensor
- containing values -1 or 1. `pred` and `label` can have arbitrary shape as
+ containing values -1 or 1. `label` and `pred` can have arbitrary shape as
long as they have the same number of elements.
Parameters
@@ -621,7 +621,7 @@ class LogisticLoss(Loss):
where `pred` is the classifier prediction and `label` is the target tensor
containing values -1 or 1 (0 or 1 if `label_format` is binary).
- `pred` and `label` can have arbitrary shape as long as they have the same number of elements.
+ `label` and `pred` can have arbitrary shape as long as they have the same number of elements.
Parameters
----------
@@ -666,14 +666,14 @@ def hybrid_forward(self, F, pred, label, sample_weight=None):
class TripletLoss(Loss):
r"""Calculates triplet loss given three input tensors and a positive margin.
- Triplet loss measures the relative similarity between prediction, a positive
- example and a negative example:
+ Triplet loss measures the relative similarity between a positive
+ example, a negative example, and prediction:
.. math::
- L = \sum_i \max(\Vert {pred}_i - {pos_i} \Vert_2^2 -
- \Vert {pred}_i - {neg_i} \Vert_2^2 + {margin}, 0)
+ L = \sum_i \max(\Vert {pos_i}_i - {pred} \Vert_2^2 -
+ \Vert {neg_i}_i - {pred} \Vert_2^2 + {margin}, 0)
- `pred`, `positive` and `negative` can have arbitrary shape as long as they
+ `positive`, `negative`, and 'pred' can have arbitrary shape as long as they
have the same number of elements.
Parameters
@@ -703,7 +703,7 @@ def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs):
def hybrid_forward(self, F, pred, positive, negative):
positive = _reshape_like(F, positive, pred)
negative = _reshape_like(F, negative, pred)
- loss = F.sum(F.square(pred-positive) - F.square(pred-negative),
+ loss = F.sum(F.square(positive-pred) - F.square(negative-pred),
axis=self._batch_axis, exclude=True)
loss = F.relu(loss + self._margin)
return _apply_weighting(F, loss, self._weight, None)
@@ -717,7 +717,7 @@ class PoissonNLLLoss(Loss):
.. math::
L = \text{pred} - \text{target} * \log(\text{pred}) +\log(\text{target!})
- `pred`, `target` can have arbitrary shape as long as they have the same number of elements.
+ `target`, 'pred' can have arbitrary shape as long as they have the same number of elements.
Parameters
----------
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