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tf.keras.losses.categorical_hinge

TensorFlow 1 version View source on GitHub

Computes the categorical hinge loss between y_true and y_pred.

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

Usage:

y_true = np.random.randint(0, 3, size=(2,))
y_true = tf.keras.utils.to_categorical(y_true, num_classes=3)
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.categorical_hinge(y_true, y_pred)
assert loss.shape == (2,)
pos = np.sum(y_true * y_pred, axis=-1)
neg = np.amax((1. - y_true) * y_pred, axis=-1)
assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.))

y_true The ground truth values. y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided they will be converted to -1 or 1.
y_pred The predicted values.

Categorical hinge loss values.