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Computes the squared hinge loss between y_true
& y_pred
.
tf.keras.metrics.squared_hinge(
y_true, y_pred
)
loss = mean(square(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.squared_hinge(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
loss.numpy(),
np.mean(np.square(np.maximum(1. - y_true * y_pred, 0.)), axis=-1))
Returns | |
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Squared hinge loss values. shape = [batch_size, d0, .. dN-1] .
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