def __init__(self, reduction=tf.losses.Reduction.AUTO, name=None, lambda_weight=None): super(PairwiseHingeLoss, self).__init__(reduction, name) self._loss = losses_impl.PairwiseHingeLoss(name='{}_impl'.format(name), lambda_weight=lambda_weight)
def _pairwise_hinge_loss( labels, logits, weights=None, lambda_weight=None, reduction=tf.compat.v1.losses.Reduction.SUM_BY_NONZERO_WEIGHTS, name=None): """Computes the pairwise hinge loss for a list. The hinge loss is defined as Hinge(l_i > l_j) = max(0, 1 - (s_i - s_j)). So a correctly ordered pair has 0 loss if (s_i - s_j >= 1). Otherwise the loss increases linearly with s_i - s_j. When the list_size is 2, this reduces to the standard hinge loss. Args: labels: A `Tensor` of the same shape as `logits` representing graded relevance. logits: A `Tensor` with shape [batch_size, list_size]. Each value is the ranking score of the corresponding item. weights: A scalar, a `Tensor` with shape [batch_size, 1] for list-wise weights, or a `Tensor` with shape [batch_size, list_size] for item-wise weights. lambda_weight: A `_LambdaWeight` object. reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to reduce training loss over batch. name: A string used as the name for this loss. Returns: An op for the pairwise hinge loss. """ loss = losses_impl.PairwiseHingeLoss(name, lambda_weight) with tf.compat.v1.name_scope(loss.name, 'pairwise_hinge_loss', (labels, logits, weights)): return loss.compute(labels, logits, weights, reduction)
def __init__(self, reduction=tf.losses.Reduction.AUTO, name=None, lambda_weight=None, temperature=1.0, ragged=False): """Pairwise hinge loss. Args: reduction: (Optional) The `tf.keras.losses.Reduction` to use (see `tf.keras.losses.Loss`). name: (Optional) The name for the op. lambda_weight: (Optional) A lambdaweight to apply to the loss. Can be one of `tfr.keras.losses.DCGLambdaWeight`, `tfr.keras.losses.NDCGLambdaWeight`, or, `tfr.keras.losses.PrecisionLambdaWeight`. temperature: (Optional) The temperature to use for scaling the logits. ragged: (Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors. """ super().__init__(reduction, name, lambda_weight, temperature, ragged) self._loss = losses_impl.PairwiseHingeLoss( name='{}_impl'.format(name) if name else None, lambda_weight=lambda_weight, temperature=temperature, ragged=ragged)
def test_pairwise_compute_per_list(self): with tf.Graph().as_default(): scores = [[1., 3., 2.], [1., 2., 3.]] labels = [[0., 0., 1.], [0., 0., 2.]] per_item_weights = [[2., 3., 4.], [1., 1., 1.]] with self.cached_session(): # PairwiseHingeLoss is chosen as an arbitrary pairwise loss to test the # `compute_per_list` behavior. loss_fn = losses_impl.PairwiseHingeLoss(name=None) losses, weights = loss_fn.compute_per_list( labels, scores, per_item_weights) losses, weights = losses.eval(), weights.eval() self.assertAllClose(losses, [1., 0.]) self.assertAllClose(weights, [4. + 4., 1. + 1.])
def make_loss_metric_fn(loss_key, weights_feature_name=None, lambda_weight=None, name=None): """Factory method to create a metric based on a loss. Args: loss_key: A key in `RankingLossKey`. weights_feature_name: A `string` specifying the name of the weights feature in `features` dict. lambda_weight: A `_LambdaWeight` object. name: A `string` used as the name for this metric. Returns: A metric fn with the following Args: * `labels`: A `Tensor` of the same shape as `predictions` representing graded relevance. * `predictions`: A `Tensor` with shape [batch_size, list_size]. Each value is the ranking score of the corresponding example. * `features`: A dict of `Tensor`s that contains all features. """ metric_dict = { RankingLossKey.PAIRWISE_HINGE_LOSS: losses_impl.PairwiseHingeLoss(name, lambda_weight=lambda_weight), RankingLossKey.PAIRWISE_LOGISTIC_LOSS: losses_impl.PairwiseLogisticLoss(name, lambda_weight=lambda_weight), RankingLossKey.PAIRWISE_SOFT_ZERO_ONE_LOSS: losses_impl.PairwiseSoftZeroOneLoss( name, lambda_weight=lambda_weight), RankingLossKey.SOFTMAX_LOSS: losses_impl.SoftmaxLoss(name, lambda_weight=lambda_weight), RankingLossKey.SIGMOID_CROSS_ENTROPY_LOSS: losses_impl.SigmoidCrossEntropyLoss(name), RankingLossKey.MEAN_SQUARED_LOSS: losses_impl.MeanSquaredLoss(name), RankingLossKey.LIST_MLE_LOSS: losses_impl.ListMLELoss(name, lambda_weight=lambda_weight), RankingLossKey.APPROX_NDCG_LOSS: losses_impl.ApproxNDCGLoss(name), RankingLossKey.APPROX_MRR_LOSS: losses_impl.ApproxMRRLoss(name), RankingLossKey.GUMBEL_APPROX_NDCG_LOSS: losses_impl.ApproxNDCGLoss(name), } def _get_weights(features): """Get weights tensor from features and reshape it to 2-D if necessary.""" weights = None if weights_feature_name: weights = tf.convert_to_tensor(value=features[weights_feature_name]) # Convert weights to a 2-D Tensor. weights = utils.reshape_to_2d(weights) return weights def metric_fn(labels, predictions, features): """Defines the metric fn.""" weights = _get_weights(features) loss = metric_dict.get(loss_key, None) if loss is None: raise ValueError('loss_key {} not supported.'.format(loss_key)) return loss.eval_metric(labels, predictions, weights) return metric_fn