tfr.keras.losses.MixtureEMLoss

Computes mixture EM loss between y_true and y_pred.

tfr.keras.losses.MixtureEMLoss(
    reduction: tf.losses.Reduction = tf.losses.Reduction.AUTO,
    name: Optional[str] = None,
    lambda_weight: Optional[losses_impl._LambdaWeight] = None,
    temperature: float = 1.0,
    alpha: float = 1.0,
    ragged: bool = False
)

Implementation of mixture Expectation-Maximization loss (Yan et al, 2018). This loss assumes that the clicks in a session are generated by one of mixture models.

Standalone usage:

y_true = [[1., 0.]]
y_pred = [[[0.6, 0.9], [0.8, 0.2]]]
loss = tfr.keras.losses.MixtureEMLoss()
loss(y_true, y_pred).numpy()
1.3198698
 
# Using ragged tensors
y_true = tf.ragged.constant([[1., 0.], [0., 1., 0.]])
y_pred = tf.ragged.constant([[[0.6, 0.9], [0.8, 0.2]],
    [[0.5, 0.9], [0.8, 0.2], [0.4, 0.8]]])
loss = tfr.keras.losses.MixtureEMLoss(ragged=True)
loss(y_true, y_pred).numpy()
1.909512

Usage with the compile() API:

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

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.
alpha (Optional) The smooth factor of the probability.
ragged (Optional) If True, this loss will accept ragged tensors. If False, this loss will accept dense tensors.

Methods

from_config

View source

@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

View source

get_config() -> Dict[str, Any]

Returns the config dictionary for a Loss instance.

__call__

View source

__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.