Computes the squared hinge loss between y_true
& y_pred
.
tf.keras.losses.squared_hinge(
y_true, y_pred
)
loss = mean(square(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 we will convert them
to -1 or 1 with shape = [batch_size, d0, .. dN] .
|
y_pred
|
The predicted values with shape = [batch_size, d0, .. dN] .
|
Returns |
Squared 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.squared_hinge(y_true, y_pred)