Computes the hinge loss between y_true
and y_pred
.
tf.keras.losses.hinge(
y_true, y_pred
)
loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)
Standalone usage:
y_true = np.random.choice([-1, 1], size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.hinge(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
loss.numpy(),
np.mean(np.maximum(1. - y_true * y_pred, 0.), axis=-1))
Args |
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.
shape = [batch_size, d0, .. dN] .
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN] .
|
Returns |
Hinge loss values. shape = [batch_size, d0, .. dN-1] .
|