tf.keras.losses.categorical_hinge

Computes the categorical hinge loss between y_true & y_pred.

tf.keras.losses.categorical_hinge(
    y_true, y_pred
)

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

Standalone 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 either {-1, +1} or {0, 1} (i.e. a one-hot-encoded tensor).
y_pred The predicted values.

Categorical hinge loss values.