tf.keras.losses.SquaredHinge

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Computes the squared hinge loss between y_true and y_pred.

tf.keras.losses.SquaredHinge(
    reduction=losses_utils.ReductionV2.AUTO, name='squared_hinge'
)

loss = square(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:

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

# loss = (max(0, 1 - y_true * y_pred))^2 = [1.6^2 + 1.7^2 + 1.5^2] / 3

print('Loss: ', loss.numpy())  # Loss: 2.566666

Usage with the compile API:

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

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()