tf.keras.losses.Hinge

Computes the hinge loss between y_true & y_pred.

Inherits From: Loss

tf.keras.losses.Hinge(
    reduction='sum_over_batch_size', name='hinge'
)

Used in the notebooks

Used in the tutorials
MNIST classification

Formula:

loss = maximum(1 - y_true * y_pred, 0)

y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1.

Args
reduction	Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size" or None.
name	Optional name for the loss instance.

Methods

call

View source

call(
    y_true, y_pred
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

__call__

View source

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.