Computes the hinge loss between y_true & y_pred.
tf.keras.losses.hinge(
y_true, y_pred
)
loss = mean(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 with shape = [batch_size, d0, .. dN].
|
y_pred
|
The predicted values with shape = [batch_size, d0, .. dN].
|
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)
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