# tf.keras.losses.Hinge

## Class Hinge

Computes the hinge loss between y_true and y_pred.

### Aliases:

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.

#### Usage:

h = tf.keras.losses.Hinge()
loss = h([-1., 1., 1.], [0.6, -0.7, -0.5])

# loss = max(0, 1 - y_true * y_pred) = [1.6 + 1.7 + 1.5] / 3

print('Loss: ', loss.numpy())  # Loss: 1.6

Usage with the compile API:

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

## __init__

View source

__init__(
reduction=losses_utils.ReductionV2.AUTO,
name='hinge'
)

Initialize self. See help(type(self)) for accurate signature.

## Methods

### __call__

View source

__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

View source

from_config(
cls,
config
)

Instantiates a Loss from its config (output of get_config()).

#### Args:

• config: Output of get_config().

A Loss instance.

View source

get_config()