Computes the hinge loss between y_true & y_pred.

Formula:

loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)

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.

Usage

loss_hinge(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "hinge"
)

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].
...: For forward/backward compatability.
reduction: Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size" or NULL.
name: Optional name for the loss instance.

Hinge loss values with shape = [batch_size, d0, .. dN-1].

y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3))
y_pred <- random_uniform(c(2, 3))
loss <- loss_hinge(y_true, y_pred)
loss

## tf.Tensor([1.0610152  0.93285507], shape=(2), dtype=float32)