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Computes the squared hinge loss between y_true and y_pred.

Inherits From: Loss

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.

Standalone usage:

y_true = [[0., 1.], [0., 0.]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
# Using 'auto'/'sum_over_batch_size' reduction type.
h = tf.keras.losses.SquaredHinge()
h(y_true, y_pred).numpy()
# Calling with 'sample_weight'.
h(y_true, y_pred, sample_weight=[1, 0]).numpy()
# Using 'sum' reduction type.
h = tf.keras.losses.SquaredHinge(
h(y_true, y_pred).numpy()
# Using 'none' reduction type.
h = tf.keras.losses.SquaredHinge(
h(y_true, y_pred).numpy()
array([1.46, 2.26], dtype=float32)

Usage with the compile() API:

model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())

reduction Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.
name Optional name for the instance. Defaults to 'squared_hinge'.



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

config Output of get_config().

A Loss instance.


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Returns the config dictionary for a Loss instance.


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Invokes the Loss instance.

y_true Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]
y_pred The predicted values. shape = [batch_size, d0, .. dN]