Source code for mxnet.gluon.loss

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# coding: utf-8
# pylint: disable=arguments-differ
""" losses for training neural networks """
from __future__ import absolute_import
__all__ = ['Loss', 'L2Loss', 'L1Loss',
           'SigmoidBinaryCrossEntropyLoss', 'SigmoidBCELoss',
           'SoftmaxCrossEntropyLoss', 'SoftmaxCELoss',
           'KLDivLoss', 'CTCLoss', 'HuberLoss', 'HingeLoss',
           'SquaredHingeLoss', 'LogisticLoss', 'TripletLoss', 'PoissonNLLLoss']

import numpy as np
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_like(F, x, y):
    """Reshapes x to the same shape as y."""
    return x.reshape(y.shape) if F is ndarray else F.reshape_like(x, y)

[docs]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__)
[docs] 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): r"""Calculates the mean squared error between `pred` and `label`. .. math:: L = \frac{1}{2} \sum_i \vert {pred}_i - {label}_i \vert^2. `pred` 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. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **label**: target tensor with the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, weight=1., batch_axis=0, **kwargs): super(L2Loss, self).__init__(weight, batch_axis, **kwargs) def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) loss = F.square(pred - 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): r"""Calculates the mean absolute error between `pred` and `label`. .. math:: L = \sum_i \vert {pred}_i - {label}_i \vert. `pred` 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. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **label**: target tensor with the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, weight=None, batch_axis=0, **kwargs): super(L1Loss, self).__init__(weight, batch_axis, **kwargs) def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) loss = F.abs(pred - label) loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class SigmoidBinaryCrossEntropyLoss(Loss): r"""The cross-entropy loss for binary classification. (alias: SigmoidBCELoss) BCE loss is useful when training logistic regression. If `from_sigmoid` is False (default), this loss computes: .. math:: prob = \frac{1}{1 + \exp(-{pred})} L = - \sum_i {label}_i * \log({prob}_i) + (1 - {label}_i) * \log(1 - {prob}_i) If `from_sigmoid` is True, this loss computes: .. math:: L = - \sum_i {label}_i * \log({pred}_i) + (1 - {label}_i) * \log(1 - {pred}_i) `pred` and `label` can have arbitrary shape as long as they have the same number of elements. 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 BCE together, which is more numerically stable through log-sum-exp trick. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **label**: target tensor with values in range `[0, 1]`. Must have the same size as `pred`. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ 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, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) if not self._from_sigmoid: # We use the stable formula: max(x, 0) - x * z + log(1 + exp(-abs(x))) loss = F.relu(pred) - pred * label + F.Activation(-F.abs(pred), act_type='softrelu') else: loss = -(F.log(pred+1e-12)*label + F.log(1.-pred+1e-12)*(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): r"""Computes the softmax cross entropy loss. (alias: SoftmaxCELoss) If `sparse_label` is `True` (default), label should contain integer category indicators: .. math:: \DeclareMathOperator{softmax}{softmax} p = \softmax({pred}) L = -\sum_i \log p_{i,{label}_i} `label`'s shape should be `pred`'s shape with the `axis` dimension removed. i.e. for `pred` with shape (1,2,3,4) and `axis = 2`, `label`'s shape should be (1,2,4). If `sparse_label` is `False`, `label` should contain probability distribution and `label`'s shape should be the same with `pred`: .. math:: p = \softmax({pred}) 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. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: the prediction tensor, where the `batch_axis` dimension ranges over batch size and `axis` dimension ranges over the number of classes. - **label**: the truth tensor. When `sparse_label` is True, `label`'s shape should be `pred`'s shape with the `axis` dimension removed. i.e. for `pred` with shape (1,2,3,4) and `axis = 2`, `label`'s shape should be (1,2,4) and values should be integers between 0 and 2. If `sparse_label` is False, `label`'s shape must be the same as `pred` and values should be floats in the range `[0, 1]`. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as label. For example, if label has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ 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, pred, label, sample_weight=None): if not self._from_logits: pred = F.log_softmax(pred, self._axis) if self._sparse_label: loss = -F.pick(pred, label, axis=self._axis, keepdims=True) else: label = _reshape_like(F, label, pred) loss = -F.sum(pred*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): r"""The Kullback-Leibler divergence loss. KL divergence measures the distance between contiguous distributions. It can be used to minimize information loss when approximating a distribution. If `from_logits` is True (default), loss is defined as: .. math:: L = \sum_i {label}_i * \big[\log({label}_i) - {pred}_i\big] If `from_logits` is False, loss is defined as: .. math:: \DeclareMathOperator{softmax}{softmax} prob = \softmax({pred}) 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 number of elements. Parameters ---------- from_logits : bool, default is `True` Whether the input is log probability (usually from log_softmax) instead of unnormalized numbers. axis : int, default -1 The dimension along with to compute softmax. Only used when `from_logits` is False. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape. If `from_logits` is True, `pred` should be log probabilities. Otherwise, it should be unnormalized predictions, i.e. from a dense layer. - **label**: truth tensor with values in range `(0, 1)`. Must have the same size as `pred`. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. References ---------- `Kullback-Leibler divergence `_ """ def __init__(self, from_logits=True, axis=-1, weight=None, batch_axis=0, **kwargs): super(KLDivLoss, self).__init__(weight, batch_axis, **kwargs) self._from_logits = from_logits self._axis = axis def hybrid_forward(self, F, pred, label, sample_weight=None): if not self._from_logits: pred = F.log_softmax(pred, self._axis) loss = label * (F.log(label+1e-12) - pred) loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class CTCLoss(Loss): r"""Connectionist Temporal Classification Loss. Parameters ---------- layout : str, default 'NTC' Layout of prediction tensor. 'N', 'T', 'C' stands for batch size, sequence length, and alphabet_size respectively. label_layout : str, default 'NT' Layout of the labels. 'N', 'T' stands for batch size, and sequence length respectively. weight : float or None Global scalar weight for loss. Inputs: - **pred**: unnormalized prediction tensor (before softmax). Its shape depends on `layout`. If `layout` is 'TNC', pred should have shape `(sequence_length, batch_size, alphabet_size)`. Note that in the last dimension, index `alphabet_size-1` is reserved for internal use as blank label. So `alphabet_size` is one plus the actual alphabet size. - **label**: zero-based label tensor. Its shape depends on `label_layout`. If `label_layout` is 'TN', `label` should have shape `(label_sequence_length, batch_size)`. - **pred_lengths**: optional (default None), used for specifying the length of each entry when different `pred` entries in the same batch have different lengths. `pred_lengths` should have shape `(batch_size,)`. - **label_lengths**: optional (default None), used for specifying the length of each entry when different `label` entries in the same batch have different lengths. `label_lengths` should have shape `(batch_size,)`. Outputs: - **loss**: output loss has shape `(batch_size,)`. **Example**: suppose the vocabulary is `[a, b, c]`, and in one batch we have three sequences 'ba', 'cbb', and 'abac'. We can index the labels as `{'a': 0, 'b': 1, 'c': 2, blank: 3}`. Then `alphabet_size` should be 4, where label 3 is reserved for internal use by `CTCLoss`. We then need to pad each sequence with `-1` to make a rectangular `label` tensor:: [[1, 0, -1, -1], [2, 1, 1, -1], [0, 1, 0, 2]] References ---------- `Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks `_ """ def __init__(self, layout='NTC', label_layout='NT', weight=None, **kwargs): assert layout in ['NTC', 'TNC'],\ "Only 'NTC' and 'TNC' layouts for pred are supported. Got: %s"%layout assert label_layout in ['NT', 'TN'],\ "Only 'NT' and 'TN' layouts for label are supported. Got: %s"%label_layout self._layout = layout self._label_layout = label_layout batch_axis = label_layout.find('N') super(CTCLoss, self).__init__(weight, batch_axis, **kwargs) def hybrid_forward(self, F, pred, label, pred_lengths=None, label_lengths=None, sample_weight=None): if self._layout == 'NTC': pred = F.swapaxes(pred, 0, 1) if self._batch_axis == 1: label = F.swapaxes(label, 0, 1) loss = F.CTCLoss(pred, label, pred_lengths, label_lengths, use_data_lengths=pred_lengths is not None, use_label_lengths=label_lengths is not None, blank_label='last') return _apply_weighting(F, loss, self._weight, sample_weight)
[docs]class HuberLoss(Loss): r"""Calculates smoothed L1 loss that is equal to L1 loss if absolute error 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} & \text{ otherwise } \end{cases} `pred` and `label` can have arbitrary shape as long as they have the same number of elements. Parameters ---------- rho : float, default 1 Threshold for trimmed mean estimator. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **label**: target tensor with the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, rho=1, weight=None, batch_axis=0, **kwargs): super(HuberLoss, self).__init__(weight, batch_axis, **kwargs) self._rho = rho def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) loss = F.abs(pred - label) 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) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class HingeLoss(Loss): r"""Calculates the hinge loss function often used in SVMs: .. math:: 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 elements. Parameters ---------- margin : float The margin in hinge loss. Defaults to 1.0 weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape. - **label**: truth tensor with values -1 or 1. Must have the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs): super(HingeLoss, self).__init__(weight, batch_axis, **kwargs) self._margin = margin def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) loss = F.relu(self._margin - pred * label) loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class SquaredHingeLoss(Loss): r"""Calculates the soft-margin loss function used in SVMs: .. math:: 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 long as they have the same number of elements. Parameters ---------- margin : float The margin in hinge loss. Defaults to 1.0 weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **label**: truth tensor with values -1 or 1. Must have the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs): super(SquaredHingeLoss, self).__init__(weight, batch_axis, **kwargs) self._margin = margin def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) loss = F.square(F.relu(self._margin - pred * label)) loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]class LogisticLoss(Loss): r"""Calculates the logistic loss (for binary losses only): .. math:: L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i)) 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. Parameters ---------- weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. label_format : str, default 'signed' Can be either 'signed' or 'binary'. If the label_format is 'signed', all label values should be either -1 or 1. If the label_format is 'binary', all label values should be either 0 or 1. Inputs: - **pred**: prediction tensor with arbitrary shape. - **label**: truth tensor with values -1/1 (label_format is 'signed') or 0/1 (label_format is 'binary'). Must have the same size as pred. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out. """ def __init__(self, weight=None, batch_axis=0, label_format='signed', **kwargs): super(LogisticLoss, self).__init__(weight, batch_axis, **kwargs) self._label_format = label_format if self._label_format not in ["signed", "binary"]: raise ValueError("label_format can only be signed or binary, recieved %s." % label_format) def hybrid_forward(self, F, pred, label, sample_weight=None): label = _reshape_like(F, label, pred) if self._label_format == 'signed': label = (label + 1.0) / 2.0 # Transform label to be either 0 or 1 # Use a stable formula in computation loss = F.relu(pred) - pred * label + F.Activation(-F.abs(pred), act_type='softrelu') loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)
[docs]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: .. math:: L = \sum_i \max(\Vert {pred}_i - {pos_i} \Vert_2^2 - \Vert {pred}_i - {neg_i} \Vert_2^2 + {margin}, 0) `pred`, `positive` and `negative` can have arbitrary shape as long as they have the same number of elements. Parameters ---------- margin : float Margin of separation between correct and incorrect pair. weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. Inputs: - **pred**: prediction tensor with arbitrary shape - **positive**: positive example tensor with arbitrary shape. Must have the same size as pred. - **negative**: negative example tensor with arbitrary shape Must have the same size as pred. Outputs: - **loss**: loss tensor with shape (batch_size,). """ def __init__(self, margin=1, weight=None, batch_axis=0, **kwargs): super(TripletLoss, self).__init__(weight, batch_axis, **kwargs) self._margin = margin 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), axis=self._batch_axis, exclude=True) loss = F.relu(loss + self._margin) return _apply_weighting(F, loss, self._weight, None)
[docs]class PoissonNLLLoss(Loss): r"""For a target (Random Variable) in a Poisson distribution, the function calculates the Negative Log likelihood loss. PoissonNLLLoss measures the loss accrued from a poisson regression prediction made by the model. .. 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. Parameters ---------- from_logits : boolean, default True indicating whether log(predicted) value has already been computed. If True, the loss is computed as :math:`\exp(\text{pred}) - \text{target} * \text{pred}`, and if False, then loss is computed as :math:`\text{pred} - \text{target} * \log(\text{pred}+\text{epsilon})`.The default value weight : float or None Global scalar weight for loss. batch_axis : int, default 0 The axis that represents mini-batch. compute_full: boolean, default False Indicates whether to add an approximation(Stirling factor) for the Factorial term in the formula for the loss. The Stirling factor is: :math:`\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})` epsilon: float, default 1e-08 This is to avoid calculating log(0) which is not defined. Inputs: - **pred**: Predicted value - **target**: Random variable(count or number) which belongs to a Poisson distribution. - **sample_weight**: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1). Outputs: - **loss**: Average loss (shape=(1,1)) of the loss tensor with shape (batch_size,). """ def __init__(self, weight=None, from_logits=True, batch_axis=0, compute_full=False, **kwargs): super(PoissonNLLLoss, self).__init__(weight, batch_axis, **kwargs) self._from_logits = from_logits self._compute_full = compute_full def hybrid_forward(self, F, pred, target, sample_weight=None, epsilon=1e-08): target = _reshape_like(F, target, pred) if self._from_logits: loss = F.exp(pred) - target * pred else: loss = pred - target * F.log(pred + epsilon) if self._compute_full: # Using numpy's pi value stirling_factor = target * F.log(target)- target + 0.5 * F.log(2 * target * np.pi) target_gt_1 = target > 1 stirling_factor *= target_gt_1 loss += stirling_factor loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss)