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tfr.keras.losses.ListMLELoss

Computes ListMLE loss between y_true and y_pred.

Implements ListMLE loss (Xia et al, 2008). For each list of scores s in y_pred and list of labels y in y_true:

loss = - log P(permutation_y | s)
P(permutation_y | s) = Plackett-Luce probability of permutation_y given s
permutation_y = permutation of items sorted by labels y.

Standalone usage:

tf.random.set_seed(42)
y_true = [[1., 0.]]
y_pred = [[0.6, 0.8]]
loss = tfr.keras.losses.ListMLELoss()
loss(y_true, y_pred).numpy()
0.7981389
# Using ragged tensors
tf.random.set_seed(42)
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.ListMLELoss(ragged=True)
loss(y_true, y_pred).numpy()
1.1613163

Usage with the compile() API:

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

Definition:

\[ \mathcal{L}(\{y\}, \{s\}) = - \log(P(\pi_y | s)) \]

where \(P(\pi_y | s)\) is the Plackett-Luce probability of a permutation \(\pi_y\) conditioned on scores \(s\). Here \(\pi_y\) represents a permutation of items ordered by the relevance labels \(y\) where ties are broken randomly.

References:

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, tfr.keras.losses.PrecisionLambdaWeight, or, tfr.keras.losses.ListMLELambdaWeight.
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.

Methods

from_config

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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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Returns the config dictionary for a Loss instance.

__call__

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See tf.keras.losses.Loss.