R: Computes the squared hinge loss between 'y_true' & 'y

loss_squared_hinge {keras3}

R Documentation

Computes the squared hinge loss between `y_true` & `y_pred`.

Formula:

loss <- square(maximum(1 - y_true * y_pred, 0))

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.

loss_squared_hinge(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "squared_hinge",
  dtype = NULL
)

`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]⁠`.
`...`	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.
`dtype`	The dtype of the loss's computations. Defaults to `NULL`, which means using `config_floatx()`. `config_floatx()` is a `"float32"` unless set to different value (via `config_set_floatx()`).

Squared 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_squared_hinge(y_true, y_pred)

[Package keras3 version 1.1.0 Index]