# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# coding: utf-8
# pylint: disable=arguments-differ
""" losses for training neural networks """
from __future__ import absolute_import
from .. import ndarray
from ..base import numeric_types
from .block import HybridBlock
def _apply_weighting(F, loss, weight=None, sample_weight=None):
"""Apply weighting to loss.
Parameters
----------
loss : Symbol
The loss to be weighted.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch separately, `sample_weight` should have
shape (64, 1).
Returns
-------
loss : Symbol
Weighted loss
"""
if sample_weight is not None:
loss = F.broadcast_mul(loss, sample_weight)
if weight is not None:
assert isinstance(weight, numeric_types), "weight must be a number"
loss = loss * weight
return loss
def _reshape_label_as_output(F, output, label):
# for symbolic output.shape is not available so we reshape
# to empty shape and let it be inferred from output's shape
# via the '-' operator later.
return label.reshape(output.shape) if F is ndarray else label.reshape(())
class Loss(HybridBlock):
"""Base class for loss.
Parameters
----------
weight : float or None
Global scalar weight for loss.
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, weight, batch_axis, **kwargs):
super(Loss, self).__init__(**kwargs)
self._weight = weight
self._batch_axis = batch_axis
def __repr__(self):
s = '{name}(batch_axis={_batch_axis}, w={_weight})'
return s.format(name=self.__class__.__name__, **self.__dict__)
def hybrid_forward(self, F, x, *args, **kwargs):
"""Overrides to construct symbolic graph for this `Block`.
Parameters
----------
x : Symbol or NDArray
The first input tensor.
*args : list of Symbol or list of NDArray
Additional input tensors.
"""
# pylint: disable= invalid-name
raise NotImplementedError
[docs]class L2Loss(Loss):
"""Calculates the mean squared error between output and label:
.. math::
L = \\frac{1}{2}\\sum_i \\Vert {output}_i - {label}_i \\Vert^2.
Output and label can have arbitrary shape as long as they have the same
number of elements.
Parameters
----------
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, `sample_weight` should have shape (64, 1).
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, weight=1., batch_axis=0, **kwargs):
super(L2Loss, self).__init__(weight, batch_axis, **kwargs)
def hybrid_forward(self, F, output, label, sample_weight=None):
label = _reshape_label_as_output(F, output, label)
loss = F.square(output - label)
loss = _apply_weighting(F, loss, self._weight/2, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class L1Loss(Loss):
"""Calculates the mean absolute error between output and label:
.. math::
L = \\frac{1}{2}\\sum_i \\vert {output}_i - {label}_i \\vert.
Output and label must have the same shape.
Parameters
----------
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, `sample_weight` should have shape (64, 1).
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, weight=None, batch_axis=0, **kwargs):
super(L1Loss, self).__init__(weight, batch_axis, **kwargs)
def hybrid_forward(self, F, output, label, sample_weight=None):
label = _reshape_label_as_output(F, output, label)
loss = F.abs(output - label)
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
class SigmoidBinaryCrossEntropyLoss(Loss):
r"""The cross-entropy loss for binary classification. (alias: SigmoidBCELoss)
BCE loss is useful when training logistic regression.
.. math::
loss(o, t) = - 1/n \sum_i (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i]))
Parameters
----------
from_sigmoid : bool, default is `False`
Whether the input is from the output of sigmoid. Set this to false will make
the loss calculate sigmoid and then BCE, which is more numerically stable through
log-sum-exp trick.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, `sample_weight` should have shape (64, 1).
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, from_sigmoid=False, weight=None, batch_axis=0, **kwargs):
super(SigmoidBinaryCrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
self._from_sigmoid = from_sigmoid
def hybrid_forward(self, F, output, label, sample_weight=None):
label = _reshape_label_as_output(F, output, label)
if not self._from_sigmoid:
max_val = F.maximum(-output, 0)
loss = output - output*label + max_val + F.log(F.exp(-max_val)+F.exp(-output-max_val))
else:
loss = -(F.log(output+1e-8)*label + F.log(1.-output+1e-8)*(1.-label))
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
SigmoidBCELoss = SigmoidBinaryCrossEntropyLoss
[docs]class SoftmaxCrossEntropyLoss(Loss):
"""Computes the softmax cross entropy loss. (alias: SoftmaxCELoss)
If `sparse_label` is `True`, label should contain integer category indicators:
.. math::
p = {softmax}({output})
L = -\\sum_i {log}(p_{i,{label}_i})
Label's shape should be output's shape without the `axis` dimension. i.e. for
`output.shape` = (1,2,3,4) and axis = 2, `label.shape` should be (1,2,4).
If `sparse_label` is `False`, label should contain probability distribution
with the same shape as output:
.. math::
p = {softmax}({output})
L = -\\sum_i \\sum_j {label}_j {log}(p_{ij})
Parameters
----------
axis : int, default -1
The axis to sum over when computing softmax and entropy.
sparse_label : bool, default True
Whether label is an integer array instead of probability distribution.
from_logits : bool, default False
Whether input is a log probability (usually from log_softmax) instead
of unnormalized numbers.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, `sample_weight` should have shape (64, 1).
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None,
batch_axis=0, **kwargs):
super(SoftmaxCrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
self._axis = axis
self._sparse_label = sparse_label
self._from_logits = from_logits
def hybrid_forward(self, F, output, label, sample_weight=None):
if not self._from_logits:
output = F.log_softmax(output)
if self._sparse_label:
loss = -F.pick(output, label, axis=self._axis, keepdims=True)
else:
loss = -F.sum(output*label, axis=self._axis, keepdims=True)
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
SoftmaxCELoss = SoftmaxCrossEntropyLoss
[docs]class KLDivLoss(Loss):
"""The Kullback-Leibler divergence loss.
KL divergence is a useful distance measure for continuous distributions
and is often useful when performing direct regression over the space of
(discretely sampled) continuous output distributions.
.. _Kullback-Leibler divergence:
https://en.wikipedia.org/wiki/Kullback-Leibler_divergence
.. math::
L = 1/n \\sum_i (label_i * (log(label_i) - output_i))
Label's shape should be the same as output's.
Parameters
----------
from_logits : bool, default is `True`
Whether the input is log probability (usually from log_softmax) instead
of unnormalized numbers.
weight : float or None
Global scalar weight for loss.
sample_weight : Symbol or None
Per sample weighting. Must be broadcastable to
the same shape as loss. For example, if loss has
shape (64, 10) and you want to weight each sample
in the batch, `sample_weight` should have shape (64, 1).
batch_axis : int, default 0
The axis that represents mini-batch.
"""
def __init__(self, from_logits=True, weight=None, batch_axis=0, **kwargs):
super(KLDivLoss, self).__init__(weight, batch_axis, **kwargs)
self._from_logits = from_logits
def hybrid_forward(self, F, output, label, sample_weight=None):
if not self._from_logits:
output = F.log_softmax(output)
loss = label * (F.log(label+1e-8) - output)
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
