{ }
View source on GitHub |
Computes the hinge loss between y_true
& y_pred
.
tf.keras.losses.hinge(
y_true, y_pred
)
Formula:
loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)
Returns | |
---|---|
Hinge loss values with shape = [batch_size, d0, .. dN-1] .
|
Example:
y_true = np.random.choice([-1, 1], size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.hinge(y_true, y_pred)