tfr.keras.losses.UniqueSoftmaxLoss

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Computes unique softmax cross-entropy loss between y_true and y_pred.

tfr.keras.losses.UniqueSoftmaxLoss(
    reduction=tf.losses.Reduction.AUTO,
    name=None,
    lambda_weight=None,
    temperature=1.0,
    ragged=False
)

Implements unique rating softmax loss (Zhu et al, 2020).

For each list of scores s in y_pred and list of labels y in y_true:

loss = - sum_i (2^{y_i} - 1) *
               log(exp(s_i) / sum_j I(y_i > y_j) exp(s_j) + exp(s_i))

Standalone usage:

y_true = [[1., 0.]]
y_pred = [[0.6, 0.8]]
loss = tfr.keras.losses.UniqueSoftmaxLoss()
loss(y_true, y_pred).numpy()
0.7981389

# 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.UniqueSoftmaxLoss(ragged=True)
loss(y_true, y_pred).numpy()
0.83911896

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tfr.keras.losses.UniqueSoftmaxLoss())

Definition:

$$ \mathcal{L}({y}, {s}) =

• \sum_i (2^{y_i} - 1) \cdot \log\left(\frac{\exp(s_i)}{\sumj I{y_i > y_j} \exp(s_j) + \exp(s_i)}\right) $$

References
Listwise Learning to Rank by Exploring Unique Ratings, Zhu et al, 2020

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 with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and 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

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@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

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get_config()

Returns the config dictionary for a Loss instance.

__call__

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__call__(
    y_true, y_pred, sample_weight=None
)

See tf.keras.losses.Loss.