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tf.keras.metrics.squared_hinge

Computes the squared hinge loss between y_true and 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))

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. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].

Squared hinge loss values. shape = [batch_size, d0, .. dN-1].