tf.keras.losses.CategoricalHinge

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Computes the categorical hinge loss between y_true and y_pred.

tf.keras.losses.CategoricalHinge(
    reduction=losses_utils.ReductionV2.AUTO, name='categorical_hinge'
)

loss = maximum(neg - pos + 1, 0) where neg = sum(y_true * y_pred) and pos = maximum(1 - y_true)

Usage:

ch = tf.keras.losses.CategoricalHinge()
loss = ch([0., 1., 1.], [1., 0., 1.])
print('Loss: ', loss.numpy())  # Loss: 1.0

Usage with the compile API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.CategoricalHinge())

Methods

__call__

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__call__(
    y_true, y_pred, sample_weight=None
)

Invokes the Loss instance.

Args:

• y_true: Ground truth values. shape = [batch_size, d0, .. dN]
• y_pred: The predicted values. shape = [batch_size, d0, .. dN]
• sample_weight: Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)

Returns:

Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)

Raises:

• ValueError: If the shape of sample_weight is invalid.

from_config

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@classmethod
from_config(
    config
)

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()