View source on GitHub |
Computes approximate MRR loss between y_true
and y_pred
.
tfr.keras.losses.ApproxMRRLoss(
reduction: tf.losses.Reduction = tf.losses.Reduction.AUTO,
name: Optional[str] = None,
lambda_weight: Optional[losses_impl._LambdaWeight] = None,
temperature: float = 0.1,
ragged: bool = False
)
Implementation of ApproxMRR loss (Qin et al, 2008). This loss is
an approximation for tfr.keras.metrics.MRRMetric
. It replaces the
non-differentiable ranking function in MRR with a differentiable approximation
based on the logistic function.
For each list of scores s
in y_pred
and list of labels y
in y_true
:
loss = sum_i (1 / approxrank(s_i)) * y_i
approxrank(s_i) = 1 + sum_j (1 / (1 + exp(-(s_j - s_i) / temperature)))
Standalone usage:
y_true = [[1., 0.]]
y_pred = [[0.6, 0.8]]
loss = tfr.keras.losses.ApproxMRRLoss()
loss(y_true, y_pred).numpy()
-0.53168947
# Using ragged tensors
y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
y_pred = tf.ragged.constant([[0.6, 0.8], [0.5, 0.8, 0.4]])
loss = tfr.keras.losses.ApproxMRRLoss(ragged=True)
loss(y_true, y_pred).numpy()
-0.73514676
Usage with the compile()
API:
model.compile(optimizer='sgd', loss=tfr.keras.losses.ApproxMRRLoss())
Definition:
\[ \mathcal{L}(\{y\}, \{s\}) = -\sum_{i} \frac{1}{\text{approxrank}_i} y_i \]
where:
\[ \text{approxrank}_i = 1 + \sum_{j \neq i} \frac{1}{1 + \exp\left(\frac{-(s_j - s_i)}{\text{temperature} }\right)} \]
References | |
---|---|
Args | |
---|---|
reduction
|
Type of tf.keras.losses.Reduction to apply to
loss. Default value is AUTO . AUTO indicates that the
reduction option will be determined by the usage context. For
almost all cases this defaults to SUM_OVER_BATCH_SIZE . When
used under a tf.distribute.Strategy , except via
Model.compile() and Model.fit() , using AUTO or
SUM_OVER_BATCH_SIZE will raise an error. Please see this
custom training tutorial
for more details.
|
name
|
Optional name for the instance. |
Methods
from_config
@classmethod
from_config( config, custom_objects=None )
Instantiates a Loss
from its config (output of get_config()
).
Args | |
---|---|
config
|
Output of get_config() .
|
Returns | |
---|---|
A Loss instance.
|
get_config
get_config() -> Dict[str, Any]
Returns the config dictionary for a Loss
instance.
__call__
__call__(
y_true: tfr.keras.model.TensorLike
,
y_pred: tfr.keras.model.TensorLike
,
sample_weight: Optional[utils.TensorLike] = None
) -> tf.Tensor
See tf.keras.losses.Loss.